Anne-Marie Mouly

Caroline Bouillot

Nicolas ; Costes

Luc Zimmer

Nadine Ravel

Philippe Litaudon
email: philippe.litaudon@cnrs.fr

Pet Cermep

Luc; CRNL Bioran Zimmer

Ravel

PET metabolic imaging of time-dependent reorganization of olfactory cued fear memory networks in rats

Keywords: [18F]FDG micro PET, Recent memory, Remote memory, System-level consolidation, Odor fear conditioning Cerebral Cortex microPET imaging of odor fear memory [18F]FDG micro PET, Cued Fear conditioning, Recent memory, Remote memory, System-level consolidation, Odor

PET metabolic imaging of time-dependent reorganization of olfactory cued fear memory networks in rats

Introduction

It is known from a long time that new memories are gradually stabilized from an initially labile state to a more permanent state through consolidation processes [START_REF] Müller | Experimentelle beiträge zur lehre vom gedächtniss[END_REF]. Consolidation involves reorganization at both the synaptic and system levels [START_REF] Dudai | The neurobiology of consolidations, or, how stable is the engram?[END_REF]. Synaptic consolidation refers to cellular and molecular modifications at the synaptic level and occurs over relatively short periods of time following learning acquisition, while system consolidation involves large-scale circuit reorganization and can encompass several days or months following the initial learning episode [START_REF] Frankland | The organization of recent and remote memories[END_REF].

According to the standard memory consolidation model, recent declarative memories are initially hippocampus-dependent but are progressively reorganized as time passes [START_REF] Winocur | Memory formation and long-term retention in humans and animals: convergence towards a transformation account of hippocampal-neocortical interactions[END_REF][START_REF] Squire | Memory consolidation[END_REF]. Following training, there is continued interplay between the hippocampus and relevant neocortical regions, with the hippocampus becoming gradually less important over time for storage and retrieval, and a more permanent memory developing in distributed regions of the neocortex. This model is supported by results from cellular imaging and pharmacological inactivation studies in animals [START_REF] Bontempi | Time-dependent reorganization of brain circuitry underlying long-term memory storage[END_REF][START_REF] Frankland | The involvement of the anterior cingulate cortex in remote contextual fear memory[END_REF][START_REF] Maviel | Sites of neocortical reorganization critical for remote spatial memory[END_REF], showing that recall of recent spatial and contextual memories is associated with activation of the hippocampus, and inactivating the hippocampus preferentially disrupts the recall of these recent memories. By contrast, as these memories get older, their recall is predominantly associated with activation of cortical regions such as the prefrontal, frontal, anterior cingulate, retrosplenial and temporal cortices and inactivation of the prelimbic or anterior cingulate cortices block recall of remote memory. Of note, system consolidation has been mostly investigated using hippocampaldependent tasks like spatial learning paradigms [START_REF] Bontempi | Time-dependent reorganization of brain circuitry underlying long-term memory storage[END_REF][START_REF] Maviel | Sites of neocortical reorganization critical for remote spatial memory[END_REF] [START_REF] Frankland | The involvement of the anterior cingulate cortex in remote contextual fear memory[END_REF]. By contrast, the reorganization of circuits with the passage of time in amygdala-dependent tasks like cued fear conditioning, has been much less investigated [START_REF] Bergstrom | The neurocircuitry of remote cued fear memory[END_REF].

In cued fear conditioning, a discrete sensory cue predicts the arrival of a foot-shock and information about the sensory cue and shock converges in the lateral amygdala nucleus via thalamic and cortical inputs and triggers synaptic modifications necessary for memory consolidation [START_REF] Ledoux | Emotion circuits in the brain[END_REF][START_REF]Neurobiology of Pavlovian fear conditioning[END_REF]). The lateral amygdala then communicates with the central amygdala nucleus, which sends projections to different structures in the brainstem and in the hypothalamus that orchestrate conditioned fear autonomic and motor responses. While the amygdala plays a key role in the acquisition, storage and retrieval of cued fear memories, there are now a series of studies examining the role of sensory cortices in both recent and remote cued fear memory retrieval (Grosso et al. 2015a;Grosso et al. 2015b). Most of these studies concern the auditory cortex. First, electrophysiological and pharmacological studies have shown that auditory stimuli paired with footshock induce robust changes of synaptic strength in the auditory cortex [START_REF] Quirk | Fear conditioning enhances different temporal components of tone-evoked spike trains in auditory cortex and lateral amygdala[END_REF][START_REF] Weinberger | Auditory associative memory and representational plasticity in the primary auditory cortex[END_REF][START_REF] Letzkus | A disinhibitory microcircuit for associative fear learning in the auditory cortex[END_REF], and that some of these learning-related changes are driven by the amygdala [START_REF] Chavez | The basolateral amygdala modulates specific sensory memory representations in the cerebral cortex[END_REF]) suggesting that the amygdala may strengthen emotional memory trace formation occurring in the auditory cortex. Second, [START_REF] Sacco | Role of secondary sensory cortices in emotional memory storage and retrieval in rats[END_REF] performing localized excitotoxic lesions of different sensory cortices, demonstrated temporally graded deficits in the retrieval of remote fear memory. Indeed, lesions of the auditory cortex disrupted remote, but not recent, auditory cued fear memory. In the same way, lesions of the piriform cortex and visual cortex disrupted remote but not recent olfactory and visual cued fear memory, respectively. Similarly, a pharmacological study showed that blockade of NMDA receptors in the posterior piriform cortex decreased remote memory performance while recent memory was unaffected [START_REF] Hegoburu | Differential involvement of amygdala and cortical NMDA receptors activation upon encoding in odor fear memory[END_REF]. These data support the view that emotional memories, like hippocampal-dependent memories, undergo some kind of system-level reorganization, with the sensory cortices being involved in remote but not recent fear memory retrieval. Importantly, in contrast to what happens for hippocampal-dependent memories showing a dispensable role for the hippocampus in remote memory retrieval, the amygdala continues to be crucially involved in fear expression during remote memory retrieval [START_REF] Bergstrom | The neurocircuitry of remote cued fear memory[END_REF]. So far, system-level reorganization of amygdala-dependent cued fear memories has been investigated primarily through lesion or inactivation experiments and, to a lesser degree, using autoradiography or histochemistry imaging techniques, thus precluding the comparison of recent and remote memory networks in the same animals. To address this question, we performed 18F-fluorodeoxyglucose ([ 18 F]FDG) positron emission tomography (PET) imaging in rats engaged in an odor fear conditioning paradigm. The key advantage of PET compared to other imaging approaches is that the PET radiotracer allows the whole-brain function to be examined without the confounding effects of anesthesia during radiotracer uptake. Indeed, the animal is awake and freely moving outside the PET scanner during [ 18 F]FDG uptake, allowing the assessment of brain activation when the animal is engaged in a behavioral task [START_REF] Jang | Neural responses of rats in the forced swimming test: [F-18]FDG micro PET study[END_REF][START_REF] Sung | Neural responses in rat brain during acute immobilization stress: a [F-18]FDG micro PET imaging study[END_REF]). Moreover, [ 18 F]FDG PET imaging can be used for serial examination of metabolic function in the same animal [START_REF] Kornblum | In vivo imaging of neuronal activation and plasticity in the rat brain by high resolution positron emission tomography (microPET)[END_REF]. In a recent study, we brought the first evidence that [ 18 F]FDG PET was a powerful tool to study odor representation in large-scale rodent neuronal networks [START_REF] Litaudon | Activity in the rat olfactory cortex is correlated with behavioral response to odor: a microPET study[END_REF]). Here we capitalized on this observation to investigate system-level reorganization in odor fear conditioning.

Material and methods

Animals

All experiments were conducted in strict accordance with the European Community Council Directive of September 22, 2010 (2010/63/UE), and received approval from the French national ethics committee (APAFIS #8682-201701261053886v3). Care was taken at all stages to minimize stress and discomfort to the animals. Data were obtained from 18 male Long Evans rats (10 weeks, 287± 20 g at the beginning of the experiment, Charles River, L'Arbresle, France). They were group-housed at 23°C and maintained under a 12/12 h light/dark cycle (lights on from 8 A.M. to 8 P.M.). Food and water were available ad libitum. At their arrival, rats were randomly assigned to one of the two experimental groups.

Experimental apparatus

The apparatus has been described in detail in a previous study [START_REF] Hegoburu | The RUB Cage: Respiration-Ultrasonic Vocalizations-Behavior Acquisition Setup for Assessing Emotional Memory in Rats[END_REF].

It consisted of a plexiglass cage (diameter 20 cm, height 30 cm, EMKA Technologies) placed in a sound attenuating cage (length, 60 cm; width, 60 cm; height, 70 cm, 56-dB background noise). The bottom of the animal chamber was equipped with a shock floor connected to a programmable Coulbourn shocker (Bilaney Consultants GmbH). Three Tygon tubing connected to a programmable custom olfactometer were inserted in the tower on the top of the cage to deliver air and odorants. Deodorized air flowed constantly through the cage (2 l/min). When programmed, an odor was introduced smoothly in the air stream through the switching of a solenoid valve (fluid automation systems, CH-1290 Versoix), thus minimizing its effect on change in pressure. The bottom of the animal chamber had a port connected to a ventilation pump which could draw air out of the plethysmograph (at a rate of up to 2 l/min) thus maintaining a

Fear conditioning paradigm

Conditioning

The training protocol is illustrated in figure 1. The animals were handled individually and placed in the experimental apparatus (two sessions of 30 min per day) during the two days preceding the beginning of the experiment to minimize stress induced by exposure to a novel environment and to familiarize them with being manipulated. For the conditioning session, rats from the Fear group (F, n=9) were allowed free exploration during the first 4 min, and then an odor (1:10 peppermint vapor to air) was introduced into the cage for 20 seconds, the last second of which overlapped with the delivery of a 0.4-mA foot-shock. The animal received 10 odor-shock trials, with an intertrial interval of 4 min. After the last pairing, the animal returned to its home cage. Rats in the Control group (C, n=9) followed the same protocol except that they received 10 presentations of 20s-odor but no shock.

Retention test

The conditioned fear response was assessed in both groups during two retention tests: one 24h after conditioning (recent memory, C1 and F1) and another 30 days later (remote memory, C2 and F2) (Fig. 1). The testing cage was equipped with new visual cues and with a plastic floor to avoid contextual fear expression. Four hours prior to each retention test session, the rats were food-deprived but were allowed free access to water. This restriction was applied to selectively maximize and homogenize [ 18 F]FDG uptake at the brain level [START_REF] Fueger | Impact of animal handling on the results of 18F-FDG PET studies in mice[END_REF]. Immediately before the start of the retention session, the animals were briefly anesthetized with isoflurane (5% for F o r P e e r R e v i e w 8 induction and 2-2.5% for maintenance), and an intravenous catheter was placed in the tail vein. Immediately after bolus [ 18 F]FDG injection (10 ± 1.6 MBq, 500 µl), the catheter was removed and isoflurane was discontinued. Then, the rat was placed in the testing cage, and allowed a 5min period to recover from anesthesia. The learned odor was then presented ten times for 20 s with a 2-min inter-trial interval. The fear memory strength was assessed by measuring the duration of the animal's freezing response during the retention test. Freezing behavior defined as the absence of any visible movement except that due to breathing [START_REF] Blanchard | Crouching as an index of fear[END_REF]) was automatically detected using a Labview homemade software and further verified by an experimenter (for a detailed description, see [START_REF] Dupin | New Insights from 22-kHz Ultrasonic Vocalizations to Characterize Fear Responses: Relationship with Respiration and Brain Oscillatory Dynamics[END_REF]. The animal's freezing response was quantified during each 20s odor presentation. The obtained values were then expressed as a percentage of the sampled period total duration, averaged across the trials for a given animal, and averaged among animals of the same experimental group. Freezing amounts were compared using a two-way repeated-measures ANOVA, with group (Control versus Fear) as an independent factor and age of memory (recent vs remote) as a repeated factor, followed by post-hoc Fisher tests for pairwise comparisons. For all the statistical comparisons performed, the significance level was set at 0.05.

PET imaging

Data acquisition

At the end of each retention test session, the rats were anesthetized with isoflurane and placed in a prone position on the bed of the scanner, and an emission scan lasting 20 minutes was initiated (Fig. 1). To minimize head movement, the rat was head-fixed with ear bars and a tooth bar in a head holder. Its body temperature was maintained at 37±0.2 °C using a dedicated heating pad and the respiration rate was monitored F o r P e e r R e v i e w 9 using a pressure sensor. Data were acquired in list-mode using a dedicated small animal PET/CT scanner manufactured by Inveon (Siemens, Erlangen, Germany). The scanner has an axial field of view of 12.7 cm and a spatial resolution of 1.8 mm full width at half maximum (FWHM) [START_REF] Bao | Performance evaluation of the inveon dedicated PET preclinical tomograph based on the NEMA NU-4 standards[END_REF]. This nominal resolution means that two sources of the same activity separated by less than 1.8 mm cannot be distinguished. Nevertheless, smaller sources can be detected and quantified. A CT scan was performed in the same position immediately after PET emission scan to correct for tissue attenuation. The images were reconstructed in four frames of five minutes each with attenuation as well as scatter correction by a 3D-filtered backprojection algorithm (Hamming filter; cut-off frequency 0.5 cycles/pixel) and a zoom factor of two. This led to reconstructed volumes of 159 slices comprising 128 × 128 voxels in a bounding box of 49.7 × 49.7 × 126 mm with voxel size 0.388 × 0.388 × 0.796 mm. After scanning, the animals were returned to their home cages.

Data analyses

Data processing was carried out using statistical parametric mapping software (SPM12, Welcome trust center for neuroimaging, London;

http://www.fil.ion.ucl.ac.uk/spm/). Individual PET images were spatially normalized to a custom PET [ 18 F]FDG template made from the MRI template in the space of the Lancelot rat brain atlas [START_REF] Lancelot | A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity[END_REF]) and resliced to a resolution of 0.1 x 0.1 x 0.205 mm. [START_REF] Lancelot | A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity[END_REF] showed that the sensitivity of detection in the subject space does not differ after spatially normalizing PET images in the template space via a functional template. Furthermore, this method resulted in excellent reproducibility.

The normalized brain images were smoothed using a Gaussian filter (isotropic 0.5 mm full width at half maximum) and subsequently masked to remove extra-cerebral activity.

Subsequently, PET image intensities were normalized using the standardized uptake ). Such a cluster-forming threshold is a reasonable statistical level inference to identify regions that are localized enough to be interpretable [START_REF] Woo | Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations[END_REF].

Results

Behavioral data

In both groups of rats (Control and Fear groups), two retention tests were carried out respectively 24 h (recent memory) and 30 days (remote memory) after the acquisition session. Freezing behavior measured in the two groups during each test session is shown in Figure 2. Due to technical issues, three video recordings could not be analyzed. A two-way repeated-measures ANOVA revealed a main effect of group (F 1,13 = 70.467, p < 0.001) but no effect of memory age (F 1,13 = 0.100, p = 0.757).

Further post-hoc comparisons showed that for both retention tests, the level of freezing in response to the odor is significantly higher in the Fear group than in the Control group. In the two groups, the level of freezing is not different between recent and remote memory.

PET imaging data

First, the functional network related to recent memory was assessed by contrasting 1). First, regional brain activations were observed in several olfactory cortical areas including the anterior piriform cortex, the posterior piriform cortex, the anterior olfactory nucleus, and the entorhinal cortex (Fig. 3a-g,j). Clusters of significant voxels were also observed in the prefrontal cortex including the prelimbic cortex and the cingulate cortex (Fig. 3a-d), in the somatosensory cortex including the barrel field cortex (Fig. 3c-f). In addition to cortical structures, significant increases in glucose metabolism were observed in the basolateral amygdaloid complex (Fig. 3f,g), the striatum (Fig. 3b,c-e), the thalamus (Fig. 3f,g) and the hippocampal formation including dorsal and ventral hippocampus and the subiculum (Fig. 3g-j). Finally, activations were also observed in the ventral part of the periaqueductal gray (Fig. 3j,k)

and in several modules of the cerebellum (Fig. 3l,m). We did not observe decrease in [ 18 F]FDG uptake.

We then investigated the functional network related to remote memory by contrasting the maps obtained for the Fear group (n=9) and Control group (n=8, one animal could not be recorded due to a technical issue with the PET scanner) during the 30 days retention test (F2 VS C2). In striking contrast with the network activated during the recent memory retention test, the remote memory network was limited to posterior piriform cortex (Fig. 4, Table 2).

Discussion

The present study investigated the fear neural networks by questioning the existence of system-level reorganization of amygdala-dependent emotional memories as assessed using odor fear conditioning. Our study is the first to our knowledge to describe the networks involved in recent and remote fear memory in the same animals

Behavior

The strength of recent and remote odor memories was assessed by measuring the freezing response to the learned odor during two retention tests carried out respectively 24h and 30 days after the conditioning session. The data showed that the level of freezing was high and similar during the two retention tests, which indicates that the strength of the remote odor memory was equivalent to that of the recent one. This data is important because the recent memory test consisted in the repeated presentation of the learned odor alone, which could have triggered extinction of the memory, resulting in low levels of freezing during the remote memory test. This was not the case, suggesting that the learned association was strong and resistant to extinction processes that could have occurred during the recent memory test.

Recent memory network

The recent memory network includes olfactory cortical areas and brain areas classically described as playing a critical role in cued fear memory, like the amygdala, the prefrontal cortex and the periaqueductal grey. This network also includes less classical brain areas like the cerebellum, the striatum and the somatosensory cortex.

Olfactory cortical network

F o r P e e r R e v i e w 13
Our data showed bilateral activations in primary olfactory areas. Among them, the piriform cortex is the largest area and is classically divided into two parts: the anterior (aPC) and posterior (pPC) piriform cortex.

Previous studies in the literature have highlighted that the aPC is preferentially involved in sensory processes and simple forms of memory, such as short-term habituation (Wilson 1998a(Wilson , 1998b;;[START_REF] Kadohisa | Separate encoding of identity and similarity of complex familiar odors in piriform cortex[END_REF], pattern completion [START_REF] Barnes | Olfactory perceptual stability and discrimination[END_REF], and perceptual learning [START_REF] Wilson | Olfactory perceptual learning: the critical role of memory in odor discrimination[END_REF]. In contrast, the pPC is involved in cognitive mnesic processes, including learning and recall of associations between odorants and information from other sensory modalities [START_REF] Litaudon | Learning-induced changes in rat piriform cortex activity mapped using multisite recording with voltage sensitive dye[END_REF][START_REF] Litaudon | Piriform cortex functional heterogeneity revealed by cellular responses to odours[END_REF][START_REF] Chabaud | Functional coupling in rat central olfactory pathways: a coherence analysis[END_REF][START_REF] Chabaud | Exposure to behaviourally relevant odour reveals differential characteristics in rat central olfactory pathways as studied through oscillatory activities[END_REF][START_REF] Mouly | Olfactory learning induces differential long-lasting changes in rat central olfactory pathways[END_REF][START_REF] Mouly | Polysynaptic potentiation at different levels of rat olfactory pathways following learning[END_REF][START_REF] Mouly | Entorhinal cortex stimulation modulates amygdala and piriform cortex responses to olfactory bulb inputs in the rat[END_REF]. The activation reported here in aPC and pPC is in accordance with previous studies showing the role of aPC [START_REF] Barnes | Odor fear conditioning modifies piriform cortex local field potentials both during conditioning and during postconditioning sleep[END_REF][START_REF] Chen | Generalized vs. stimulus-specific learned fear differentially modifies stimulus encoding in primary sensory cortex of awake rats[END_REF] and pPC [START_REF] Sevelinges | Olfactory fear conditioning induces field potential potentiation in rat olfactory cortex and amygdala[END_REF][START_REF] Sevelinges | Neonatal odor-shock conditioning alters the neural network involved in odor fear learning at adulthood[END_REF][START_REF] Jones | Differential regional expression of brain-derived neurotrophic factor following olfactory fear learning[END_REF][START_REF] Hegoburu | Differential dynamics of amino acid release in the amygdala and olfactory cortex during odor fear acquisition as revealed with simultaneous high temporal resolution microdialysis[END_REF] [START_REF] Maren | Retrograde abolition of conditional fear after excitotoxic lesions in the basolateral amygdala of rats: absence of a temporal gradient[END_REF][START_REF] Cousens | Both pre-and posttraining excitotoxic lesions of the basolateral amygdala abolish the expression of olfactory and contextual fear conditioning[END_REF]. In our study, the retrieval of recent odor fear memory activates the BLA and confirms its role in the expression of odor fear memory highlighted in previous studies using electrophysiological recordings [START_REF] Rosenkranz | Dopamine-mediated modulation of odour-evoked amygdala potentials during pavlovian conditioning[END_REF][START_REF] Sevelinges | Olfactory fear conditioning induces field potential potentiation in rat olfactory cortex and amygdala[END_REF][START_REF] Sevelinges | Enduring effects of infant memories: infant odor-shock conditioning attenuates amygdala activity and adult fear conditioning[END_REF][START_REF] Dupin | New Insights from 22-kHz Ultrasonic Vocalizations to Characterize Fear Responses: Relationship with Respiration and Brain Oscillatory Dynamics[END_REF][START_REF] Dupin | Respiration and brain neural dynamics associated with interval timing during odor fear learning in rats[END_REF], intracerebral microdialysis monitoring [START_REF] Hegoburu | Differential dynamics of amino acid release in the amygdala and olfactory cortex during odor fear acquisition as revealed with simultaneous high temporal resolution microdialysis[END_REF]) and lesion or pharmacological inactivation [START_REF] Cousens | Both pre-and posttraining excitotoxic lesions of the basolateral amygdala abolish the expression of olfactory and contextual fear conditioning[END_REF][START_REF] Kilpatrick | Modulation of memory consolidation for olfactory learning by reversible inactivation of the basolateral amygdala[END_REF][START_REF] Walker | Glutamate receptor antagonist infusions into the basolateral and medial amygdala reveal differential contributions to olfactory vs. context fear conditioning and expression[END_REF][START_REF] Hegoburu | Differential involvement of amygdala and cortical NMDA receptors activation upon encoding in odor fear memory[END_REF].

Prefrontal cortex

Our data showed an activation of the median prefrontal cortex (mPFC), and more specifically its prelimbic part, during recent odor fear memory recall. In rodents, the mPFC is subdivided into infralimbic (IL) and prelimbic (PL) regions, which have been described as having opposite influences on fear behavior: while the PL is involved in the expression of fear, the IL is rather involved in the suppression of fear during during the retrieval of a recent odor fear memory, suggesting its involvement is independent of the sensory modality of the learned CS. These data extend previous electrophysiological data showing that the PL might be involved in the encoding of the temporal relationships between an odor CS and the US during acquisition of an odor fear conditioning [START_REF] Dupin | Respiration and brain neural dynamics associated with interval timing during odor fear learning in rats[END_REF]). In addition to median prefrontal cortex, our data showed that the cingulate cortex was activated during odor fear memory retrieval as observed for auditory cued fear memory [START_REF] Tang | Pavlovian fear memory induced by activation in the anterior cingulate cortex[END_REF].

Hippocampus

Here we report an activation in both dorsal and ventral parts of the hippocampus during the retrieval of a recent odor fear memory. [START_REF] Maren | Hippocampus and Pavlovian fear conditioning in rats: muscimol infusions into the ventral, but not dorsal, hippocampus impair the acquisition of conditional freezing to an auditory conditional stimulus[END_REF], comparing the effects of pharmacological inactivation of the dorsal hippocampus or ventral hippocampus on Pavlovian auditory fear conditioning in rats, reported that inactivation of the ventral, but not the dorsal hippocampus, induced impairments in the acquisition of the learning. They also showed that postraining lesion of the ventral hippocampus disrupted the expression of conditioned fear to the tone. Thus, the ventral hippocampus might be involved in the acquisition and expression of auditory fear memory. The dorsal hippocampus, as for it, is thought to play a critical role in contextual fear conditioning [START_REF] Anagnostaras | Hippocampus and contextual fear conditioning: recent controversies and advances[END_REF][START_REF] Rudy | Context representations, context functions, and the parahippocampalhippocampal system[END_REF][START_REF] Maren | The contextual brain: implications for fear conditioning, extinction and psychopathology[END_REF], and does not seem to be involved in cued auditory fear conditioning [START_REF] Kim | Modality-specific retrograde amnesia of fear[END_REF]Phillips andLeDoux 1992, 1994). In the present study, although the testing cage was equipped with new visual cues and with a plastic floor for the retention test the animals might have partly recognized the initial training cage thus expressing contextual fear in addition to odor fear. This could explain why both ventral and dorsal parts of the hippocampus were activated during the recent memory test. The PAG has long been implicated as the organizer of the different behavioral components of the response to threat [START_REF] Ledoux | Emotion circuits in the brain[END_REF][START_REF] Tovote | Midbrain circuits for defensive behaviour[END_REF]. Previous studies showed that activation of the ventrolateral periaqueductal gray (vlPAG) by the central amygdala (CeA) drives immobility and lesions of the vlPAG reduce the conditioned freezing response [START_REF] Ledoux | Different projections of the central amygdaloid nucleus mediate autonomic and behavioral correlates of conditioned fear[END_REF][START_REF] Vianna | Lesion of the ventral periaqueductal gray reduces conditioned fear but does not change freezing induced by stimulation of the dorsal periaqueductal gray[END_REF], while expression of conditioned freezing is associated with increased vlPAG neuronal activation [START_REF] Carrive | Conditioned fear to context is associated with increased Fos expression in the caudal ventrolateral region of the midbrain periaqueductal gray[END_REF]. In a recent study, [START_REF] Tovote | Midbrain circuits for defensive behaviour[END_REF] identified an inhibitory pathway from the CeA to the vlPAG that produces freezing by disinhibition of vlPAG excitatory outputs to pre-motor targets in the magnocellular nucleus of the medulla. Our data confirm the involvement of the vlPAG during the retrieval of a recent odor fear memory that is accompanied by a high level of freezing in the trained animals.

Activations beyond the classically described fear conditioning network

In the present study we found activation in several modules of the cerebellum. This is in accordance with the data reported in the literature showing that the cerebellum has functional connections with fear-related areas, including the PAG, the amygdala, and the prefrontal cortex [START_REF] Koutsikou | Neural substrates underlying fear-evoked freezing: the periaqueductal grey-cerebellar link[END_REF][START_REF] Frontera | Bidirectional control of fear memories by cerebellar neurons projecting to the ventrolateral periaqueductal grey[END_REF][START_REF] Vaaga | Cerebellar modulation of synaptic input to freezing-related neurons in the periaqueductal gray[END_REF]).

Nevertheless, only few studies have directly investigated cerebellar contributions to fear behavior. Thus, Pavlovian fear conditioning affects cerebellar plasticity [START_REF] Sacchetti | Long-term synaptic changes induced in the cerebellar cortex by fear conditioning[END_REF], and cerebellar lesions or inactivation modulate freezing response [START_REF] Supple | Effects of cerebellar vermal lesions on species-specific fear responses, neophobia, and taste-aversion learning in rats[END_REF][START_REF] Koutsikou | Neural substrates underlying fear-evoked freezing: the periaqueductal grey-cerebellar link[END_REF]. Activation observed in the present study in Crus 2 lobule, as also reported in a previous study [START_REF] Litaudon | Activity in the rat olfactory cortex is correlated with behavioral response to odor: a microPET study[END_REF], is in accordance with previous data showing that Crus 2 is activated by whisker stimulation [START_REF] Sharp | c-fos expression and (14C) 2deoxyglucose uptake in the caudal cerebellum of the rat during motor/sensory cortex stimulation[END_REF][START_REF] Bosman | Encoding of whisker input by cerebellar Purkinje cells[END_REF] and by stimulation of the skin of the snout [START_REF] Armstrong | Responses in the posterior lobe of the rat cerebellum to electrical stimulation of cutaneous afferents to the snout[END_REF], both being associated with sniffing. This result could also be related to These activations might be related to whisker movements, which are coupled with sniffing during exploration of the environment [START_REF] Welker | Analysis of sniffing of the albino rat[END_REF][START_REF] Deschênes | Sniffing and whisking in rodents[END_REF].

Interestingly, [START_REF] Maier | Layer 4 organization and respiration locking in the rodent nose somatosensory cortex[END_REF] analyzed the nose/nostril representation in the rodent somatosensory cortex using receptive field mapping and subsequent histological reconstruction. Their results reveal previously unknown cytoarchitectonic and physiological properties of the rodent nose somatosensory cortex, potentially enabling it to integrate multiple sensory modalities.

The other brain structure showing increased metabolism during the recent memory test was the striatum, and more specifically its dorsal part. Importantly, the medial posterior dorsal striatum receives inputs from the piriform cortex [START_REF] Mcgeorge | The organization of the projection from the cerebral cortex to the striatum in the rat[END_REF] and the amygdala [START_REF] Mcdonald | Topographical organization of amygdaloid projections to the caudatoputamen, nucleus accumbens, and related striatal-like areas of the rat brain[END_REF]. The dorsal striatum has been suggested to be involved in aversive learning in general such as in auditory fear conditioning [START_REF] Ferreira | Effects of dorsal striatum lesions in tone fear conditioning and contextual fear conditioning[END_REF][START_REF] Kishioka | A novel form of memory for auditory fear conditioning at a lowintensity unconditioned stimulus[END_REF][START_REF] Wendler | The roles of the nucleus accumbens core, dorsomedial striatum, and dorsolateral striatum in learning: performance and extinction of Pavlovian fear-conditioned responses and instrumental avoidance responses[END_REF], or in a two-way active avoidance task [START_REF] Darvas | Requirement of dopamine signaling in the amygdala and striatum for learning and maintenance of a conditioned avoidance response[END_REF][START_REF] Wendler | The roles of the nucleus accumbens core, dorsomedial striatum, and dorsolateral striatum in learning: performance and extinction of Pavlovian fear-conditioned responses and instrumental avoidance responses[END_REF] but not in contextual fear conditioning [START_REF] Ferreira | Effects of dorsal striatum lesions in tone fear conditioning and contextual fear conditioning[END_REF]. Our present data are in line with results from Boulanger [START_REF] Bertolus | Infant rats can learn time intervals before the maturation of the striatum: evidence from odor fear conditioning[END_REF] assessing the activity of the striatum in odor fear conditioning using 2-Deoxyglucose autoradiography. They revealed that, in paired rats, the medial part of the posterior striatum showed an increased activity compared to control animals. Thus, the present data extend the role of the dorsal striatum to the retrieval of a recent odor fear memory.

Remote memory network

The remote memory network revealed using [ 18 F]FDG uptake was limited to pPC. The present data are in accordance with studies showing the role of PC in remote odor fear 18 memory. Indeed, [START_REF] Sacco | Role of secondary sensory cortices in emotional memory storage and retrieval in rats[END_REF] found that excitotoxic lesions of auditory, visual, or olfactory secondary sensory cortices impaired remote, but not recent, fear memories in rats, leading the authors to suggest that sensory cortices support memory storage and retrieval of sensory stimuli that have acquired a behavioral salience with the experience.

Surprisingly, the amygdala did not show an increased activity compared to control group. There is abundant literature showing that the amygdala is involved in fear expression, but very few studies have investigated its role over time in cued fear memories [START_REF] Bergstrom | The neurocircuitry of remote cued fear memory[END_REF]). In one of these studies the basolateral amygdala was lesioned 1-day, 7-days and 28-days following auditory cued Pavlovian fear conditioning [START_REF] Maren | Retrograde abolition of conditional fear after excitotoxic lesions in the basolateral amygdala of rats: absence of a temporal gradient[END_REF]. At each time point, the expression of cued fear was abolished. These data suggest the dependence of cued fear memory retrieval on amygdala function whatever the age of memory. In the present study, we would thus have expected an activation of the amygdala during remote memory retrieval. It could be the case that the amygdala was still activated, albeit at a lower level or for a very short period of time, that fell below the sensitivity of the [ 18 F]FDG PET imaging technology. Indeed, we can imagine that for the recall of an old memory, the amygdala, even if it is still necessary, is involved in a transitory way and/or at a lower energy cost.

Limitations

The present study entails some limitations that need to be considered.

Indeed, some technical issues must be considered when using FDG PET. First, the temporal resolution of this technique is poor compared to that of other functional methods. Indeed, [ 18 F]FDG is irreversibly trapped within tissues over the course of the uptake session, and the final PET image is the cumulative result of all brain activations occurring during the retention test, thus precluding to assess the time course of brain activation across the session. Second, the spatial resolution of this technique is limited, making it difficult to assess the activity of small brain areas. Despite these caveats, PET imaging has important advantages. First, compared to other imaging methods like fMRI and optical imaging that require the animal to be anesthetized or head-fixed, probe uptake occurs while the animal is conscious and freely moving. Thus, this technique allows the examination of whole brain function without the confounding effects of anesthesia during radiotracer uptake or animal sacrifice. PET should also be considered as a complementary approach to electrophysiological recording. Indeed, by assessing whole brain activity, even at low spatial resolution, this technique could help to identify otherwise unexplored areas of interest for subsequent electrophysiological recording.

Conclusions

The present work allowed us to characterize for the first time, in the same animals involved in an olfactory cued fear conditioning paradigm, the networks activated during the recall of a recent vs remote amygdala-dependent memory. The data pinpointed to the drastic difference between the sizes of the two networks. These results bring strong support to the view that amygdala-dependent memories, like hippocampal-dependent memories, undergo system-level reorganization, and that sensory cortical areas might participate in the long-term storage of emotional memories.

Only very few studies have investigated rodent fear memory network using imaging methods initially developed for the human brain. questioning the existence of system-level reorganization using a longitudinal approach.

The fact that we highlighted the neuronal network classically described in the literature for fear memory shows that the results obtained with FDG PET are reliable. In addition, by assessing the whole brain activity, our results extend the fear associated network to other less investigated structures.

Overall [START_REF] Paxinos | The rat brain in stereotaxic coordinates[END_REF]. The x-coordinate corresponds to the distance lateral from the midline between the hemispheres (positive values for spots located on the right side) (L, Left; R, Right); the y-coordinate denotes the dorsoventral position; the z-coordinate indicates the position relative to Bregma (positive values for sections anterior to Bregma). Cluster numbers corresponds to red and white numbers on figure 3 panels.

Cluster Peak Coordinates (mm) Cluster

Nb

  Animal's behavior was monitored with two video cameras placed on the walls of the sound-attenuating cage.

  activity concentration/injected dose x body weight). A voxel-based SPM analysis was performed within a one-way measures ANOVA design with C1, C2, F1 and F2 as factors. Whole-brain mean [ 18 F]FDG activity computed for each scan within the template mask of the brain was used for ANCOVA normalization. Statistical maps data were thresholded at p<0.001 (uncorrected) with an extent threshold of 100 voxels (0.205 mm3

  F]FDG PET. The data showed that the recall of a recent odor fear memory activates a large network of structures spanning from the prefrontal cortex to the cerebellum. In sharp contrast, in our experimental conditions, remote fear memory retrieval activates significantly a very restricted network limited to a single cortical area, the piriform cortex.

  ;[START_REF] Dupin | Respiration and brain neural dynamics associated with interval timing during odor fear learning in rats[END_REF] in odor fear conditioning. They are also in agreement with recent data showing that olfactory fear conditioning activates sparse and distributed ensembles of neurons in the mouse piriform cortex and that specific silencing of these neurons impairs odor fear memory recall[START_REF] Meissner-Bernard | Encoding of Odor Fear Memories in the Mouse Olfactory Cortex[END_REF]).The activation observed in the anterior olfactory nucleus (AON) is more surprising. A few studies have shown that AON was activated during context-odor association[START_REF] Aqrabawi | Olfactory memory representations are stored in the anterior olfactory nucleus[END_REF][START_REF] Levinson | Context-dependent odor learning requires the anterior olfactory nucleus[END_REF]. More interestingly, a study by[START_REF] Staples | Neural activation during cat odor-induced conditioned fear and "trial 2" fear in rats[END_REF] showed that AON was activated during predator odor contextual fear conditioning. Finally, our results confirmed data from a previous study showing that fos expression is enhanced in the AON following odor fear conditioning[START_REF] Funk | Enhanced fos expression within the primary olfactory and limbic pathways induced by an aversive conditioned odor stimulus[END_REF].Activation in the classically described fear conditioning network from a long time that circuits within the amygdala are essential to the acquisition, storage, and retrieval of contextual and cued (mostly auditory) fear memories[START_REF] Ledoux | Emotion circuits in the brain[END_REF][START_REF]Neurobiology of Pavlovian fear conditioning[END_REF][START_REF] Pape | Plastic synaptic networks of the amygdala for the acquisition, expression, and extinction of conditioned fear[END_REF]. Specifically, selective lesions of the basolateral amygdaloid complex (BLA) produce severe deficits in both the acquisition and expression of Pavlovian fear conditioning independent of the stimulus modality used for the CS[START_REF] Ledoux | The lateral amygdaloid nucleus: sensory interface of the amygdala in fear conditioning[END_REF][START_REF] Campeau | Involvement of the central nucleus and basolateral complex of the amygdala in fear conditioning measured with fear-potentiated startle in rats trained concurrently with auditory and visual conditioned stimuli[END_REF]

  extinction learning and retrieval[START_REF] Sierra-Mercado | Dissociable roles of prelimbic and infralimbic cortices, ventral hippocampus, and basolateral amygdala in the expression and extinction of conditioned fear[END_REF][START_REF] Milad | Fear extinction as a model for translational neuroscience: ten years of progress[END_REF][START_REF] Giustino | The Role of the Medial Prefrontal Cortex in the Conditioning and Extinction of Fear[END_REF]. There are now several lines of evidence linking activity in the prelimbic cortex, with the retrieval of auditory cued fear conditioning[START_REF] Corcoran | Activity in prelimbic cortex is necessary for the expression of learned, but not innate, fears[END_REF][START_REF] Burgos-Robles | Sustained conditioned responses in prelimbic prefrontal neurons are correlated with fear expression and extinction failure[END_REF][START_REF] Choi | Prelimbic cortical BDNF is required for memory of learned fear but not extinction or innate fear[END_REF][START_REF] Sierra-Mercado | Dissociable roles of prelimbic and infralimbic cortices, ventral hippocampus, and basolateral amygdala in the expression and extinction of conditioned fear[END_REF][START_REF] Courtin | Prefrontal parvalbumin interneurons shape neuronal activity to drive fear expression[END_REF][START_REF] Do-Monte | Revisiting the role of infralimbic cortex in fear extinction with optogenetics[END_REF]. Here we show that the PL is also activated

  observed in the somatosensory cortex including barrel field cortex.

  [START_REF] Uselman | Evolution of brain-wide activity in the awake behaving mouse after acute fear by longitudinal manganese-enhanced MRI[END_REF] used manganese-enhanced MRI to analyze innate fear response to predator odor. In the only other study using PET imaging associated with auditory fear conditioning,[START_REF] Luyten | Micro-positron emission tomography imaging of rat brain metabolism during expression of contextual conditioning[END_REF] focused their analysis on the neurocircuitry of contextual anxiety. The a step further in the investigation of the fear neural networks by

  x, y, z are the coordinates of the peak activation centers in the Paxinos and Watson stereotaxic space (Paxinos and Watson 2005). The x-coordinate corresponds to the distance lateral from the midline between the hemispheres (positive values for spots located on the right side) (L, Left; R, Right); the y-coordinate denotes the dorsoventral position; the z-coordinate indicates the position relative to Bregma (positive values for sections anterior to Bregma). Abbreviations: pPC (posterior piriform cortex).

Figure 1 :

 1 Figure captions

Figure 2 :

 2 Figure 2: Freezing response

Figure 3 :

 3 Figure 3: Voxel-based SPM results for the T-contrast Fear versus Control for recent

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: Training and imaging protocol. During the conditioning session, the animals in the Fear group (F) were trained with 10 odor-shock pairings, while the animals in the Control group (C) received 10 odor presentations but no shock. For both groups, a retention test was conducted 24h (recent memory, F1 & C1) and 30 days (remote memory, F2 & C2) later using ten odor presentations during which the animal's freezing response was assessed. Five minutes before each retention test, the animals received a [18F]FDG intravenous injection. At the end of each retention test, the animals were anesthetized for the PET imaging session.

  FDG uptake along the brain antero-posterior axis. The main activated brain areas are reported in Table 1 (all the significant clusters are reported in supplementary table

	increases in [ 18 F]
	F
	o r
	P
	e e r
	R e v
	i e w
	11

Fear group (n=9) and Control group (n=9) maps obtained during the 24h retention test (F1 VS C1 contrast). This analysis revealed several clusters showing significant

Table 1 :

 1 , our results demonstrate that PET imaging offers a powerful approach for neuroimaging in behaving animals. By enabling longitudinal studies thanks to serial brain imaging of the same subject, PET imaging supports the necessary reduction in the number of animals used in experimental procedures. Main brain regions showing significant increases in [ 18 F]FDG uptake between Fear (F1) and Control (C1) conditions for recent memory retention test.

	F
	o r
	P
	e e r
	R e v
	i e w

Table 2 :

 2 Brain regions showing significant increases in [ 18 F]FDG uptake between Fear and Control conditions for remote memory retention test.

table 1

 1

	and supplementary

table 1

 1

	and

Figure 4: Voxel-based SPM results for the T-contrast Fear versus Control for remote memory retention test. Coronal slice through T-maps showing cluster of significant increases in [18F]FDG uptake overlaid on an MRI template. The image follows neurological convention. White number on panel corresponds to cluster number in table 2.

Table 1 : Clusters showing significant increases in [ 18 F]FDG uptake between Fear (F1) and Control (C1) conditions for recent memory retention test.

 1 Values x, y, z are the coordinates of the peak activation centers in the Paxinos and Watson stereotaxic space

Declaration of interest: None

Data/code availability statement

The data that support the findings of this study are available from the corresponding author, Philippe Litaudon, upon reasonable request.


Catarina Veiga

Guillaume Janssens

Thomas Baudier

Lucian Hotoiu

Sebastien Brousmiche

Jamie Mcclelland

Ching-Ling Teng

Lingshu Yin

Gary Royle

Boon-Keng Kevin Teo

PhD Kevin Teo

A comprehensive evaluation of the accuracy of CBCT and deformable registration based dose calculation in lung proton therapy

Keywords: adaptive radiotherapy, cone-beam CT, deformable image registration, lung, proton therapy

The uncertainties in water equivalent thickness (WET) and accuracy of dose estimation using a virtual CT (vCT), generated from deforming the planning CT (pCT) onto the daily cone-beam CT (CBCT), were comprehensively evaluated in the context of lung malignancies and passive scattering proton therapy. The validation methodology utilized multiple CBCT datasets to generate the vCTs of twenty lung cancer patients. A correction step was applied to the vCTs to account for anatomical modifications that could not be modeled by deformation alone. The CBCT datasets included a regular CBCT (rCBCT) and synthetic CBCTs created from the rCBCT and rescan CT (rCT), which minimized the variation in setup between the vCT and the gold-standard image (i.e., rCT). The uncertainty in WET was defined as the voxelwise difference in WET between vCT and rCT, and calculated in 3D (planning target volume, PTV) and 2D (distal and proximal surfaces). The uncertainty in WET based dose warping was defined as the difference between the warped dose and a forward dose recalculation on the rCT. The overall root mean square (RMS) uncertainty in WET was 3.6±1.8, 2.2±1.4 and 3.3±1.8 mm for the distal surface, proximal surface and PTV, respectively. For the warped dose, the RMS uncertainty of the voxelwise dose difference was 6±2% of the maximum dose (%mD), using a 20% cut-off. The rCBCT resulted in higher uncertainties due to setup variability with the rCT; the uncertainties reported with the two synthetic CBCTs were similar. The vCT followed by a correction step was found to be an accurate alternative to rCT.

MANUSCRIPT

Introduction

The dosimetric advantage of protons over photons has been used to improve the poor outcome of lung cancer patients [START_REF] Desantis | Cancer treatment and survivorship statistics[END_REF][START_REF] Wink | Particle therapy for non-small cell lung tumors: Where do we stand? A systematic review of the literature[END_REF]. Passive scattering proton therapy (PSPT) has predominantly been used as a robust strategy to deal with the intra-fractional motion of lung tumors; however, other inter-fractional changes during the course of radiotherapy may also affect the dose delivered to target and healthy tissues [START_REF] Guckenberger | Potential of adaptive radiotherapy to escalate the radiation dose in combined radiochemotherapy for locally advanced non-small cell lung cancer[END_REF][START_REF] Qin | Adaptive stereotactic body radiation therapy planning for lung cancer[END_REF]. These factors include changes in tumor size and position, alterations in tissue anatomy, variations in respiratory patterns, and fluctuations in patient weight [START_REF] Gomez | Adaptive radiation for lung cancer[END_REF]. Tumor regression was found to occur in conventional photon radiotherapy for 40% of the patients undergoing definitive treatment [START_REF] Van Zwienen | Anatomical changes during radiotherapy of lung cancer patients[END_REF], with reductions of up 70% of their volume reported in the literature [START_REF] Britton K R | Assessment of gross tumor volume regression and motion changes during radiotherapy for non-small-cell lung cancer as measured by four-dimensional computed tomography[END_REF]. Lung changes during the course of treatment, such as increase or decrease of atelectasis (i.e., lung collapse) and pleural effusion (i.e., liquid accumulated between lungs and the ribs or diaphragm), are less frequent but dramatically modify the density locally. Additionally, movement of the tumor can evolve throughout the treatment fractions; therefore, re-evaluation of the targets may be required [START_REF] Ozhasoglu | Issues in respiratory motion compensation during external-beam radiotherapy[END_REF]. The position of the spread out Bragg peak (SOBP) is sensitive to changes in tissue density along the beam path, which may result in potential shifts of the SOBP and consequently to tumor underdosage and/or high doses delivered outside of the intended target. Inter-fractional adaptive replanning is therefore beneficial to select patients [START_REF] Hui | Effects of interfractional motion and anatomic changes on proton therapy dose distribution in lung cancer[END_REF]. At our clinic (Roberts Proton Therapy Center, Philadelphia, PA), replanning is triggered in about 10-20% of the lung cancer patients treated with PSPT.

Gantry mounted cone beam computed tomography (CBCT) systems are now available for image-guidance in proton therapy. In comparison with repeat computed tomography (CT), CBCT has the advantage of providing three dimensional visualization of the patient's anatomy in treatment position; however, the lower image quality and poor consistency of the Hounsfield units (HU) undermine its direct usability for proton dose calculations [START_REF] Stock | Image quality and stability of image-guided radiotherapy (IGRT) devices: A comparative study[END_REF]. Deformable image registration (DIR) can be used to create a virtual CT (vCT) [START_REF] Peroni | Automatic segmentation and online virtual CT in head-and-neck adaptive radiation therapy[END_REF] by deforming the CT onto the CBCT, and thus provide a solution for the issues with image quality for dose recalculation [START_REF] Veiga | Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CTto-CBCT deformable registration for "dose of the day" calculations[END_REF][START_REF] Moteabbed | Validation of a deformable image registration technique for cone beam CT-based dose verification[END_REF]. vCTs have been shown to be a potential surrogate for repeat CTs for proton treatment verification in the context of head and neck malignancies (Landry et al 2015a, Landry et al 2015b[START_REF] Veiga | Conebeam computed tomography and deformable registration-based "dose of the day" calculations for adaptive proton therapy[END_REF][START_REF] Kurz | Feasibility of automated proton therapy plan adaptation for head and neck tumors using cone beam CT images[END_REF]; however, these retrospective studies used data from linear accelerator CBCT systems.

We previously proposed an adaptive radiotherapy (ART) workflow using on-board CBCT and DIR, where replanning is triggered after three decision-points [START_REF] Veiga | First clinical investigation of CBCT and deformable registration for adaptive proton therapy of lung cancer[END_REF]: (1) an online evaluation of a fast range-corrected dose distribution based on a vCT, which may be followed by (2) a more accurate offline dose recalculation on the vCT and in turn trigger (3) a rescan CT (rCT) for replanning. We critically assessed the clinical application of this ART workflow to generate indicators that prompt patient reevaluation and replanning, and compared its performance to routine repeat CT. DIR validation is however a complex and extensive topic, which merits a separate discussion on its own. Therefore, in this companion study we provide procedures for in-house validation of CBCT and DIR based workflows in adaptive proton therapy, followed by a thorough and comprehensive analysis of the uncertainties of the workflow implemented in our clinic. Particularly, we describe and discuss different types of CBCT datasets that can be used for validation of CBCT based methods. This is the first study that validates DIR and CBCT for lung cancer malignancies in the context of proton therapy that uses CBCT acquired on a proton-gantry.

Materials and methods

Design of the gantry mounted CBCT system for proton therapy

The gantry mounted CBCT system (Ion Beam Applications (IBA) SA, Ottignies-Louvain-la-Neuve, BE) was first used clinically at the Roberts Proton Therapy Center in September 2014 (Fig. 1) [START_REF] Seabra | Design of cone-beam CT for proton therapy gantry[END_REF], Brousmiche et al 2014). This was the first IBA CBCT clinical system and was not part of the original proton room solution, but rather retrospectively fitted by updating the existing kV imaging system.

Modifications to the orthogonal kV X-ray imaging system for CBCT were made by upgrading one X-ray tube to the GS-2075 (Varian Medical Systems, Palo Alto, CA) and one flat panel imager to the Pixium RF 4343 with CsI scintillator (Thales, Neuilly-sur-Seine, France). Image acquisition can be performed with either full or half gantry rotation at up to one revolution per minute. Image reconstruction was performed using the FDK algorithm [START_REF] Feldkamp | Practical cone-beam algorithm[END_REF] implemented in the open-source Reconstruction Toolkit (RTK) [START_REF] Rit | The reconstruction toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the insight toolkit (ITK)[END_REF]. The system has a source-to-axis distance of 288.4 cm, detector-to-axis distance of 58.6 cm and a maximum field of view (FoV) of 34 cm in diameter and length. In this CBCT system, the panel was not designed to be offset laterally and this limited the CBCT FoV.

Fig. 1 -Picture of the gantry mounted CBCT system on the IBA Proteus® Plus proton therapy system. The kV X-ray source and imaging panel orthogonal to the proton beam are used for CBCT acquisition.

Patient data acquisition

Data from 20 patients treated for lung malignancies were included in this retrospective study. All patients underwent PSPT using two treatment fields, with a median daily fractionation of 1.8 Gy[Cobalt Gray Equivalent (CGE)]/fraction up to 66.6 Gy [CGE]. The patient cohort included a variety of tumor sizes, locations and anatomical changes that occurred throughout the treatment course [START_REF] Veiga | First clinical investigation of CBCT and deformable registration for adaptive proton therapy of lung cancer[END_REF].

The imaging protocol consisted of a 4D PET/CT (Gemini TF Big Bore PET/CT, Philips Healthcare, Andover, MA) for treatment planning, CBCT acquired online as part of the image guidance protocol, and an offline rescan 4D CT (Sensation Open, Siemens Healthcare, Malvern, PA) in treatment position for plan verification during the course of treatment. The average of the 4D CT was used for treatment planning and to generate the vCTs. One pair of CBCT and rCT at mid-treatment were selected for evaluation for each patient. The chosen rCT and CBCT pairs were acquired with up to 2 days difference. The CBCTs were acquired using a half-scan mode at 110 kVp. The image voxel sizes were 1.17×1.17×3.0 mm 3 , 1.33×1.33×2.5 mm 3 and 0.98×0.98×3 mm 3 for the planning CT (pCT), CBCT and rCTs, respectively.

CBCT and DIR based adaptive proton therapy workflow for lung malignancies

The online adaptive proton therapy workflow for lung patients implemented in our clinic can be divided in 3 main steps: (1) image registration, (2) vCT correction, and (3) calculation of WET and "dose of the day" [START_REF] Veiga | First clinical investigation of CBCT and deformable registration for adaptive proton therapy of lung cancer[END_REF].

Image registration and virtual CT correction

Rigid registration between the pCT and the CBCT was first applied prior to the DIR using six degrees of freedom by matching the bony anatomy at the target volume level; this was manually performed online.

Then, the CT was registered to the CBCT using a diffeomorphic implementation of the Morphons algorithm [START_REF] Knutsson | Morphons: segmentation using elastic canvas and paint on priors[END_REF]. This algorithm is based on the matching of local phases. It was previously shown to be robust to changes in intensity when applied to CT images of the thorax with and without contrast enhancement [START_REF] Janssens | Diffeomorphic registration of images with variable contrast enhancement, diffeomorphic registration of images with variable contrast enhancement[END_REF] and evaluated in the context of head and neck adaptive proton therapy (Landry et al 2015a, Landry et al 2015b). Eight resolution scales were used, with ten iterations for the six coarsest scales, five and two for the second finest and finest scales, respectively. A Gaussian regularization of 1.25 mm standard deviation was used. These settings were optimized empirically for the thoracic region. After registration, the vCT was corrected for gross registration errors caused by complex changes that occur in the thorax such as atelectasis, pleural effusion and complex tumor response to treatment [START_REF] Veiga | First clinical investigation of CBCT and deformable registration for adaptive proton therapy of lung cancer[END_REF].

Water equivalent thickness and dose warping

Water equivalent thickness (WET) represents the equivalent thickness of water that would cause a proton beam to lose the same amount of energy in a given thickness of a different medium. The CT HUs was first converted to relative stopping power using the tissue-substitute calibration method [START_REF] Schneider | The calibration of CT hounsfield units for radiotherapy treatment planning[END_REF]. The WET maps were obtained per beam angle by accumulating the relative stopping power on a voxel-by-voxel basis of all tissues crossed by an infinitely thin beam reaching the point from a virtual proton source. The changes between planned and daily WET can then be used to range-correct the dose distribution; hence, the "dose of the day" was estimated using a WET based dose warping method as described by [START_REF] Park | Fast range-corrected proton dose approximation using prior dose distribution[END_REF]. The implementation of this algorithm was benchmarked in-house against dose recalculation on our clinically commissioned proton therapy treatment planning system (Eclipse v11.0, Varian Medical Systems, Palo Alto, CA).

Implementation details

The open-source REGGUI package was used to implement the vCT and "dose of the day" estimation workflow. REGGUI is a researcher friendly platform built in MATLAB (MathWorks, Natick, MA) for image processing featuring various registration methods, filtering methods, segmentation tools, CBCT simulation and other proton therapy dedicated functions, including WET computation and WET based dose warping. The software was designed in a way that facilitates the construction of clinical data workflows for research purposes. REGGUI and the clinical workflow implemented in our facility is freely available as part of the recently launched open-source initiative supported by IBA, the Proton Adaptive Therapy (PATh) project (https://openreggui.org/).

Procedures for the validation of CBCT and DIR in proton therapy

CBCT dataset definition

The lack of an ideal imaging dataset is one of the main challenges in validating DIR in clinical settings. In order to demonstrate that the vCT can provide similar clinical information as the rCT, the ideal imaging dataset would be a pair of anatomically matched CBCT (used to generate the vCT) and rCT (used as goldstandard for comparison) and images. Only by using such a perfect dataset the true uncertainty of using a vCT to calculate WET and dose could be estimated. However, the CBCT and rCT cannot be acquired simultaneously and will always display residual geometrical variations from setup variation, patient and respiratory motion. Therefore, for validation purposes we propose to generate the vCTs from two synthetic CBCT datasets in addition to a regular CBCT: a simulated CBCT (created from the rCT) and a virtual CBCT (created from the rCBCT). These synthetic CBCTs were created to minimize the geometrical variations indicated above and permits a more accurate estimate of the true uncertainty in WET and dose of the vCT method arising from the DIR approach for lung patients.

1) Regular CBCT (rCBCT): rCBCTs acquired within 2-3 days from a rCT are commonly used in the literature to validate DIR [START_REF] Peroni | Automatic segmentation and online virtual CT in head-and-neck adaptive radiation therapy[END_REF], Landry et al 2015b). The rCBCTs have the characteristic image quality and real limitations of the cone beam geometry, namely variability in HU and reconstruction artifacts (e.g., scatter, streaks and distortions caused by the couch). The main disadvantage is setup variations between rCT and rCBCT. The rCBCTs used in this study were acquired within two days the rCT; despite the closeness in time, for some patients the apparent anatomical mismatch was considerable.

2) Simulated CBCT (sCBCT): In order to create an anatomically matched CBCT and rCT pair with no setup variations, a simulated CBCT can be generated from the rCT. We implemented a workflow using RTK [START_REF] Rit | The reconstruction toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the insight toolkit (ITK)[END_REF]. The first step is to simulate raw 2D projection data from the average 4D rCT using the same geometry and acquisition parameters of the in-room CBCT system. To further mimic the rCBCT image characteristics, we added scatter and Poisson noise to the raw 2D projections. We used a scatter kernel superposition method (Ohnesorge et al 1999, Sun and[START_REF] Sun | Improved scatter correction using adaptive scatter kernel superposition[END_REF] with parameters estimated through Monte-Carlo simulations (GATE) [START_REF]GATE: a simulation toolkit for PET and SPECT[END_REF]. The weight of these parameters was empirically fine-tuned and tested on the RANDO phantom (The Phantom Laboratory, Greenwich, NY). Finally, the 3D volume was reconstructed using the same reconstruction algorithm as in our facility (i.e., FDK implemented on RTK). The sCBCT is in principle anatomically identical to the rCT but has slightly different image artifacts from the rCBCT.

3) Virtual CBCT (vCBCT): Another way to create an anatomically matched CBCT and rCT pair is to deform the rCBCT to match the rCT. The resulting virtual CBCT contains the Hounsfield unit (HU) information of the rCBCT and the geometry of the rCT [START_REF] Veiga | Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CTto-CBCT deformable registration for "dose of the day" calculations[END_REF]. No further correction was applied after DIR in this case since the differences between rCT and rCBCT were purely setup variation, and no large and/or non-deformable anatomical changes were verified between scans. This process introduces small geometric errors but recreates most of the artifacts present in real CBCT imaging [START_REF] Veiga | Conebeam computed tomography and deformable registration-based "dose of the day" calculations for adaptive proton therapy[END_REF].

The three CBCT datasets (rCBCT, sCBCT and vCBCT) were employed to generate three vCTs (vCTrCBCT, vCTsCBCT and vCTvCBCT, respectively), which were used to report the uncertainties associated with DIR and CBCT imaging in WET and dose estimation. To distinguish between intrinsic limitations of the registration process from the residual uncertainties due to differences in image quality and/or setup, we generated a fourth additional vCT (hereby denominated as vCTrCT) by deforming the pCT directly to the rCT.

Differences observed between the vCTrCT and rCT are therefore attributable only to DIR errors without any influence in CBCT image quality and setup variations.

Validation workflow

Popular methods to validate DIR in ART applications include measuring the accuracy in landmark and/or contour propagation [START_REF] Peroni | Automatic segmentation and online virtual CT in head-and-neck adaptive radiation therapy[END_REF][START_REF] Veiga | Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CTto-CBCT deformable registration for "dose of the day" calculations[END_REF], Landry et al 2015a[START_REF] Zhang | Automatic delineation of on-line head-and-neck computed tomography images: Toward on-line adaptive radiotherapy[END_REF]. However, accurate dose calculations require accurate HU information, and not necessarily accurate voxel-by-voxel mapping. In fact, we acknowledge that in certain situations DIR will fail to recover the real deformations, which lead us to include in our clinical workflow a correction step to account for registration gross errors.

Therefore, we evaluate the uncertainties associated with meaningful quantities for the proposed application, i.e, WET and warped dose. The clinical usefulness of these quantities was explored in the companion study,

where clinical indicators based on potential under/over-ranges (i.e., WET variations at the target location), underdosage of target volumes and/or overdose of organs at risk (OAR) were investigated [START_REF] Veiga | First clinical investigation of CBCT and deformable registration for adaptive proton therapy of lung cancer[END_REF]. The uncertainty in WET was here defined as the voxelwise difference in WET between vCT and rCT, and was calculated in both 3D (planning target volume, PTV) and 2D (distal and proximal surfaces).

The 2D WET maps are the projections of the 3D WET at the entrance and exit surfaces of the PTV (proximal and distal, respectively). The uncertainty in WET based dose warping was defined as the voxelwise difference between the warped dose and the full dose recalculation in the rCT. We report the mean, root-mean square (RMS) and 95 th percentile of the voxelwise uncertainty in WET and WET based warped dose. Additionally, we assessed the relationship between mismatch in HUs and dose uncertainty using linear regression and the correlation coefficient. The mismatch in HUs between each vCT and the corresponding rCT was defined in terms of the normalized cross correlation (NCC) between the images.

Only voxels with dose larger than 20% of the prescribed dose were considered when computing the dose differences and similarity between images.

Results

Summary of the results for all CBCT datasets

Fig. 2 provides a qualitative example of the differences between CBCT datasets used in this study in terms of image quality; the color overlay with the rCT highlights the differences in setup. Note that the vCBCT showed a greater similarity with the rCBCT in terms of image quality, while the sCBCT showed greater similarity with the rCT in terms of setup. The CBCT FoV is smaller than that of CT imaging due to hardware limitations, which causes truncation of the data. This can remove information of the anatomy in the beam path and also cause severe artefacts in the vCT where information is missing; however, with accurate positioning online most of the truncated anatomy can be positioned outside the proton beam path and thus minimize its importance [START_REF] Veiga | First clinical investigation of CBCT and deformable registration for adaptive proton therapy of lung cancer[END_REF].

The overall uncertainties in WET and dose associated with the vCTs were computed using the three CBCTs.

Table 1 presents the difference in WET calculation between the vCT and the rCT on the distal and proximal surfaces, and within the PTV, as well as the comparison between warped dose and the forward dose calculation using the rCT. The dose difference (DD) results were normalized to the maximum dose (%mD).

The results were averaged for all patients, fields and CBCT datasets. Regarding the spatial distribution of the uncertainties, we found the absolute WET uncertainty to increase with depth which is in agreement with the fact that WET is a cumulative quantity. The larger dose uncertainties were found to occur mainly at the distal edge of the fields, and not in the homogeneous region of dose distribution, due to the sharp fall-off of the proton dose distributions We also note that the total uncertainty in dose results not only from the registration errors but also from to the approximations considered in the dose warping process itself.

While Table 1 presents results for individual fields, each patient was actually treated with two fields. When considering the total dose instead of individual field doses, the dose uncertainty (RMS) decreased from the 6±2 reported in Table 1, to 4±2 %mD. Dosimetric parameters were therefore less sensitive to registration errors when multiple fields were used since summing the contribution of each field averaged out the differences.

Effect of dataset used to generate the vCTs

The unique features of the vCT generated by each CBCT dataset were used to decouple the different sources of variation in the WET and dose warping uncertainty (Table 2). The vCTrCBCT exhibited the largest reported uncertainties, while vCTvCBCT exhibited the lowest. The results obtained for vCTsCBCT and vCTvCBCT were however comparable. When assessing the overall similarity between each vCT and the corresponding rCT, we found values for the NCC of 0.942±0.028, 0.976±0.019 and 0.980±0.019 for vCTrCBCT, vCTsCBCT and vCTvCBCT, respectively. This is in agreement with the trends found for WET and dose uncertainties. A linear regression between NCC and uncertainty in dose gives an insight in understanding the relationship between mismatch in HUs and dose uncertainty (Fig. 3. If the vCT perfectly matches the rCT, then one expects the dose uncertainty to be zero; i.e., the higher the similarity in HUs between vCT and rCT the smaller the dose uncertainty. However, we only found a moderate negative correlation between NCC and dose uncertainty RMS (𝜌=-0.53, Pearson's correlation coefficient) and we hypothesize this to be related with the properties of the individual proton fields. Characteristics of the beam may (1) cause some proton beams to be more robust or sensitive than others to registration errors of similar magnitude and (2) the dose warping process itself has higher uncertainties for specific beams. Therefore, choice of beam direction, range and modulation may impact the overall uncertainty in dose. vCBCT 0.6±1.4 0.3±0.8 0.6±1.3 2.9±1.6 1.4±0.9 2.6±1.5 6±4 2.8±1.7 6±3 -0.4±0.7 5.5±1.9 9±5 rCT 0.7±1.5 0.3±0.8 0.6±1.5 2.8±1.7 1.3±0.9 2.5±1.6 6±4 2.6±1.8 5±4 -0.4±0.7 5.4±1.9 9±5

7

As none of the CBCT datasets is ideal, the results of the similarity between vCTrCT and rCT bring additional 8 confidence on where the true uncertainty lies [START_REF] Veiga | Conebeam computed tomography and deformable registration-based "dose of the day" calculations for adaptive proton therapy[END_REF]. The residual uncertainty of up to 1 mm in 9

WET can be attributed to both the poorer quality of the CBCT compared to CT and to residual differences 10 in setup for the different CBCT datasets. 11 12 4. Discussion 13 14 CBCT imaging has an important role in image-guided proton therapy, particularly as a substitute for 15 verification scans. In this work we investigated validation methods of the use of CBCT and DIR for adaptive 16 lung proton therapy radiotherapy and quantified the uncertainties inherent to the workflow. This study is a 17 companion to a recently published work from our group, where the vCT method validated here is applied 18 retrospectively as part of a clinical adaptation workflow. A complete discussion of the clinical implications of the inaccuracies investigated here can be found in its clinical counterpart [START_REF] Veiga | First clinical investigation of CBCT and deformable registration for adaptive proton therapy of lung cancer[END_REF].

The accuracy of vCT for WET estimation and consequently the "dose of the day" for lung cancer patients was investigated in this work using regular and synthetic CBCT datasets in comparison with rescan CTs.

The rCBCTs are not anatomically identical to the reference rCT, hence the reason why vCBCTs and sCBCTs were created to circumvent this limitation. An overall uncertainty of 3.6±1.8 mm (RMS) in the WET was found at the targets' distal surface. While the results taken together offer enough evidence for quantifying the uncertainties associated with DIR and CBCT imaging for the proposed application, each type of CBCT has its own advantages as well as limitations to validate in-house CBCT based ART techniques. Additionally, deforming the pCT directly to the rCT brings additional confidence of where the true uncertainty lies. Validation of new technologies before clinical translation is of utmost importance; however, we appreciate that not all proton facilities have the resources to perform such a lengthy and comprehensive validation. We recommend to other centres validating their in-house or commercial tools to choose whichever datasets are more readily available as part of their clinical routine and software tools available to validate their method, while recognising the limitations of the chosen approach when analysing their results. The limitations and advantages of each of the CBCT datasets are discussed in further detail in the following paragraphs.

The rCBCT reports the highest uncertainties due to setup differences; because the CBCT and rCT are not acquired simultaneously, there are generally differences due to variations in setup and respiratory pattern.

When using the rCBCT for validation purposes, one must assume that it will provide a worst-case estimate of the uncertainties of the vCT. An important point to discuss regarding using rCBCT for validation of DIR is the reasoning behind acquiring a CBCT and rCT in the same day. A rCBCT does not provide any additional clinical information compared to a rCT acquired on the same day, unless the patient was flagged for replanning due to dramatic changes. As part of routine clinical work either a CBCT or a rCT is acquired for plan verification. Acquiring an additional scan poses additional and unnecessary dose to the patient and must be properly justified. For example, in our study this additional scan was acquired as part of the commissioning of the newly-installed CBCT system.

Using the vCBCT to generate the vCT results in the lowest reported uncertainties in the WET and dose calculation. Of all the CBCT datasets, this has the best balance between agreement in geometry with the rCT and agreement in intensities with the rCBCT. However, one can argue that the vCBCT can help the DIR workflow to appear better than it really is. While it is unlikely to occur, it is possible for the errors in registering the pCT to the vCBCT to be the exact opposite of the errors when generating the vCBCT, and hence using vCBCT for validation purposes may locally underestimate the real uncertainties [START_REF] Veiga | Conebeam computed tomography and deformable registration-based "dose of the day" calculations for adaptive proton therapy[END_REF]. One advantage of using the vCBCT resides in the fact that it could be used when rCBCT are not available so close in time to the rCT [START_REF] Veiga | Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CTto-CBCT deformable registration for "dose of the day" calculations[END_REF]. Thus, the vCBCT is a good method to validate DIR for facilities that already routinely use CBCT for image-guidance and want to implement ART workflows clinically. It is also versatile as it benefits from a wide variety of open-source and friendly DIR tools to generate the datasets.

While other publications have used the concept of rCBCT [START_REF] Peroni | Automatic segmentation and online virtual CT in head-and-neck adaptive radiation therapy[END_REF], Landry et al 2015b) and vCBCT [START_REF] Veiga | Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CTto-CBCT deformable registration for "dose of the day" calculations[END_REF][START_REF] Veiga | Conebeam computed tomography and deformable registration-based "dose of the day" calculations for adaptive proton therapy[END_REF] for validation of CBCT-based ART, to the best of our knowledge no other studies have proposed a sCBCT as we did here. The sCBCT removes any geometric uncertainty from the validation workflows; however, the main disadvantage of the proposed methodology as currently implemented was not reproducing all the features in the CBCT imaging exactly. For example, scatter artifacts appeared more severe when farther away from the central region of the volume, cupping artifacts were slightly different, and artifacts and distortions caused by the couch, from neglecting cardiac and respiratory motion, and other contributions (Yao and Leszczynski 2009) were not included in the simulation. In the sCBCTs we found that the scatter was overestimated in the interface between lung and tissue/bone. The deterioration of the contrast resulted in systematic registration errors. This was most likely the main source of discrepancy in WET calculation between the vCBCT and sCBCT datasets. These differences in the intensity of the CBCT datasets are particularly important if the DIR uses monomodal similarity measures. In spite of the limitations of the current implementation, the sCBCT method has the potential to become an ideal platform to validate CT-to-CBCT DIR using patient data. This may be achievable with further optimization of the scatter and noise parameters to tailor for the specificities of the individual CBCT systems, and introduction of 4DCT phase information to reproduce motion artefacts.

However, due to the significant amount of work necessary to develop the sCBCT to include motion we consider this improvement to be out of the scope of this work. In fact, it was in light of the perceived individual limitations of each CBCT dataset that we used several datasets to strengthen the validation. DIR errors lead to inaccuracies in WET and dose computation. The cohort of lung patients included in this study included a broad range of clinical situations that can reduce the accuracy of the registrations achievable. It is paramount to validate the workflow on a cohort representative of the challenges of the particular site. The main limitation of DIR is related with the mathematical basis of the most common and popular DIR algorithms, whose deformations include only translation, expansion and contraction. This is not specific to the Morphons algorithm as we have also performed a similar validation study with a B-Spline Free Form based algorithm (Modat et al 2010) and the hybrid free-form registration algorithm in RayStation (version 4.5, RaySearch Laboratories, Stockholm, SE). The accuracy achieved with each algorithm was comparable (supplementary material) and all algorithms struggled to reproduce nondeformable and/or large anatomical changes. Changes in topology, such as the appearance or disappearance of tissue, cannot be reproduced without introducing singularities in the deformation fields. Since the intensities are mapped from the CT to the CBCT space, variations in the density of the tissue between images cannot be reproduced [START_REF] Palma | Lung density changes after stereotactic radiotherapy: a quantitative analysis in 50 patients[END_REF]. Additionally, the volume of rigid bodies is not necessarily preserved and the displacement between ribs and lung may not be accurately modelled without specialized algorithms [START_REF] Kim | Distance-preserving rigidity penalty on deformable image registration of multiple skeletal components in the neck[END_REF][START_REF] Risser | Piecewise-diffeomorphic registration of 3D CT/MR pulmonary images with sliding conditions[END_REF] There is therefore a large scope to continue developing DIR algorithms and/or post-processing methods that account for site-specific issues.

The ART workflow was implemented as a prototype in REGGUI which is a research platform, and therefore not optimized for fast online use. In its proposed form the workflow can be implemented more efficiently: the most computationally demanding processes (DIR, vCT correction and dose warping) can be performed in several minutes using the GPU. In terms of improving the accuracy, other approaches to estimate the "dose of the day" may be also be implemented in the future; while dose warping can be computed rapidly it is still only an approximation. In fact, the uncertainty in dose results from two different processes: the uncertainty in WET resulting from the DIR process, and the uncertainty of the dose warping process itself.

The method here presented is therefore more appropriate to verify the range than to accurately calculate dose distributions, as it can identify if beam under or over-ranging results in loss of coverage or OAR doses above tolerance levels [START_REF] Veiga | First clinical investigation of CBCT and deformable registration for adaptive proton therapy of lung cancer[END_REF]. Incorporating in the workflow a fast GPU-Monte Carlo calculation [START_REF] Jia | GPU-based fast Monte Carlo dose calculation for proton therapy[END_REF], or by incorporating the current workflow as part of treatment planning systems are rival and more accurate approaches to assess dose on a vCT. Direct dose calculation on the CBCT is a possible way to remove the errors introduced by DIR. However, while feasible in conventional photon treatments [START_REF] Richter | Investigation of the usability of conebeam CT data sets for dose calculation[END_REF][START_REF] Yang | Evaluation of on-board kV cone beam CT (CBCT)based dose calculation[END_REF][START_REF] Guan | Dose calculation accuracy using cone-beam CT (CBCT) for pelvic adaptive radiotherapy[END_REF] for proton therapy the usability of CBCT after simple intensity rescaling methods remains debatable [START_REF] Veiga | Conebeam computed tomography and deformable registration-based "dose of the day" calculations for adaptive proton therapy[END_REF][START_REF] Kurz | Comparing cone-beam CT intensity correction methods for dose recalculation in adaptive intensitymodulated photon and proton therapy for head and neck cancer[END_REF] and other groups are working on correction techniques that use DIR and scatter correction to improve the HU accuracy of CBCT directly [START_REF] Park | Proton dose calculation on scatter-corrected CBCT image: Feasibility study for adaptive proton therapy[END_REF].

Conclusions

We described procedures for in-house validation of CBCT and DIR-based adaptive radiotherapy workflows using regular and synthetic CBCT datasets. The uncertainties in WET and dose warping were quantified in the context of an adaptive proton therapy for lung cancer patients. The vCTs generated were found to provide similar WET and dosimetric information as repeat CT.

Fig. 2 -

 2 Fig. 2 -Example of (a) rCBCT, (b) sCBCT, and (c) vCBCT for one of the patients included in this study (top row), and corresponding CBCT (cyan) color overlay with the rCT (red).

Fig. 3 -

 3 Fig. 3 -Linear regression of similarity between vCT and rCT (expressed as the normalized correlation coefficient,

Table 1 -

 1 Overall uncertainty in WET (vCT vs rCT) within the planning target volume (PTV), and on the distal and proximal surfaces (mean value ± standard deviation), and overall uncertainty in voxelwise warped dose (vs forward calculation in rCT). The results were averaged over all patients, fields and CBCT datasets.

	mean	RMS	95% percentile

Table 2 -

 2 Uncertainty

				Uncertainty in WET:(mm)				Uncertainty in dose
		mean			RMS			95%			(%mD)
	Dist	Prox	PTV	Dist	Prox	PTV	Dist	Prox	PTV	mean	RMS	95%
	rCBCT 0.5±2.8 -0.1±2.6 0.2±2.8 4.6±1.8 3.3±1.5 4.3±2.1 9±4	6±3	8±4	-0.4±1.0 7.3±2.5 14±7
	sCBCT 1.6±1.3	1.1±0.7	1.5±1.2 3.3±1.6 1.8±0.8 2.9±1.4 7±4 3.4±1.7	6±3	-0.6±0.8 5.6±1.9 10±5

in WET (vCT vs rCT) within the planning target volume (PTV), and on the distal and proximal surfaces generated by each CBCT dataset and rCT, and corresponding uncertainty in warped dose (vs forward calculation in rCT) (mean value ± standard deviation). The results were averaged over all patients and fields.

Acknowledgments

The authors have no relevant conflicts of interest to disclose. C. V. is funded by Fundação para a Ciência e a Tecnologia (FCT) grant SFRH/BD/76169/2011, co-financed by FSE, POPH/QREN and EU.