Ilyes Benlala

Rabea Klaar

Thomas Gaass

Julie Macey

Stéphanie Bui

Baudouin Denis De Senneville

Patrick Berger

François Laurent

Gael Dournes

Julien Dinkel

Non-Contrast-Enhanced Functional Lung MRI to Evaluate Treatment Response of Allergic Bronchopulmonary Aspergillosis in Patients With Cystic Fibrosis: A Pilot Study

Keywords: ABPA, CF, ventilation, perfusion, Fourier decomposition

Background: Allergic bronchopulmonary aspergillosis (ABPA) in cystic fibrosis (CF) patients is associated with severe lung damage and requires specific therapeutic management. Repeated imaging is recommended to both diagnose and follow-up the response to treatment of ABPA in CF. Purpose: To evaluate whether Fourier decomposition (FD) functional lung MRI can detect the response to treatment of ABPA in CF patients. Study type: Retrospective longitudinal. Population: 12 patients (7 M, median age: 14 years) with CF and ABPA (according to Cystic Fibrosis Foundation Consensus Conference criteria) and pre-and post-treatment MRI.

Field strength/sequence: 2D balanced steady-state free precession (bSSFP) sequence with Fourier decomposition (FD) at 1.5T.

Assessment: Ventilation weighted (V) and perfusion weighted (Q) maps were obtained after FD processing of 2D coronal bSSFP time resolved images acquired before and 3 -9 months after treatment of ABPA. Defects extent was assessed on the functional maps using a qualitative semi-quantitative score. Mean and coefficient of variation (cv) of the ventilation signal intensity (VSI) and the perfusion signal intensity (QSI) were calculated. Measurements were performed independently by two readers and averaged. The inter-reader reproducibility of the measurements was also assessed. Pulmonary function tests (PFTs) using body plethysmography were performed within 1 week of both MRI studies as markers of the airflow limitation severity.

Statistical tests:

Comparisons of medians were performed using the paired Wilcoxon test.

Reproducibility was assessed using the intraclass correlation coefficient (ICC). Correlations between MRI and PFT parameters were assessed using the Spearman test (correlation coefficient: rho). A p value <0.05 was considered as significant.

Results: Defects extent on both V and Q maps showed a significant reduction after ABPA treatment. VSI_mean was significantly increased after treatment. Qualitative analyses reproducibility showed an ICC >0.93, while the ICCs of the quantitative measurements was almost perfect (>0.99). Changes in VSI_cv and QSI_cv before and after treatment correlated inversely with changes of obstructive parameters assessed by PFTs (rho = -0.68 for both).

Data conclusion: Non-contrast enhanced FD lung MRI has potential to reproducibly assess response to treatment of ABPA in CF patients and correlates with PFT obstructive parameters.

Introduction

Cystic fibrosis (CF) is one of the most frequent genetic disorders in the Caucasian population [START_REF] Jain | Update in Cystic Fibrosis 2013[END_REF]. It is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, resulting in diverse pathologic manifestations including bronchiectasis, nasal polyposis, pancreatic insufficiency, and infertility [START_REF] Sheppard | The pathology of cystic fibrosis[END_REF]. Lung involvement (i.e., destruction of lung parenchyma and decline in pulmonary function) is responsible for most life-limiting complications [START_REF] Jain | Update in Cystic Fibrosis 2013[END_REF]. Mucus composition and thickness in CF patients provide a favourable environment for fungal respiratory infections [START_REF] Kousha | Pulmonary aspergillosis: a clinical review[END_REF]. Indeed, Aspergillus fumigatus has been found in the sputum of up to 57% of patients with CF [START_REF] Saunders | Chronic Aspergillus fumigatus colonization of the pediatric cystic fibrosis airway is common and may be associated with a more rapid decline in lung function[END_REF]. The growth of A. fumigatus hyphae within the bronchial lumen may be responsible for an immunoglobulin E (IgE)-mediated hypersensitivity response, known as allergic bronchopulmonary aspergillosis (ABPA), leading to bronchial inflammation and airway destruction. Its prevalence in CF patients ranges between 3 to 25%, and is higher than that in asthmatic patients [START_REF] Mastella | Allergic bronchopulmonary aspergillosis in cystic fibrosis. A European epidemiological study. Epidemiologic Registry of Cystic Fibrosis[END_REF]. In CF patients, ABPA, which is associated with severe lung damage and requires specific therapeutic management, may be suspected due to worsening of pulmonary function and evidence of new infiltrates on chest radiographs or computed tomography (CT). Also, the findings of ABPA on chest CT include central bronchiectasis, often with mucoid impaction, centrilobular nodules, mosaic attenuation, and cavitation [START_REF] Ward | Accuracy of CT in the diagnosis of allergic bronchopulmonary aspergillosis in asthmatic patients[END_REF][START_REF] Neeld | Computerized tomography in the evaluation of allergic bronchopulmonary aspergillosis[END_REF][START_REF] Logan | High-attenuation mucous plugging in allergic bronchopulmonary aspergillosis[END_REF]. Another finding of ABPA is high-attenuation mucus plugging (HAM) on CT images, which has been considered highly specific, if not pathognomonic of the disease [START_REF] Refait | CT evaluation of hyperattenuating mucus to diagnose allergic bronchopulmonary aspergillosis in the special condition of cystic fibrosis[END_REF]. One of the important aspects in the management of ABPA in CF patients is repeating imaging after treatment to evaluate the effectiveness of the chosen therapeutics. However, repeating CT examinations should be limited due to the risk associated with high cumulative radiation exposure [START_REF] Brenner | Computed tomography--an increasing source of radiation exposure[END_REF][START_REF] Pearce | Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study[END_REF]. Recently, lung MRI has been evaluated to assess structural alterations in CF [START_REF] Dournes | Lung morphology assessment of cystic fibrosis using MRI with ultra-short echo time at submillimeter spatial resolution[END_REF][START_REF] Dournes | Quiet Submillimeter MR Imaging of the Lung Is Feasible with a PETRA Sequence at 1.5[END_REF]. Indeed, lung morphological modifications in CF assessed using ultra-short echo time (UTE)-MRI have been found to be similar to those using CT [START_REF] Dournes | Lung morphology assessment of cystic fibrosis using MRI with ultra-short echo time at submillimeter spatial resolution[END_REF][START_REF] Dournes | 3D ultrashort echo time MRI of the lung using stack-of-spirals and spherical k-Space coverages: Evaluation in healthy volunteers and parenchymal diseases[END_REF]. Furthermore, inverted mucus impaction signal on MRI (IMIS), which is characterized by a high signal intensity on T1-weighted images and low signal intensity on T2-weighted images, can be considered as the counterpart of HAM and has been found to be 100% specific to detect ABPA in CF patients [START_REF] Dournes | Allergic Bronchopulmonary Aspergillosis in Cystic Fibrosis: MR Imaging of Airway Mucus Contrasts as a Tool for Diagnosis[END_REF]. Moreover, lung MRI can evaluate pulmonary functional changes in a regional fashion [START_REF] Dournes | The Clinical Use of Lung MRI in Cystic Fibrosis: What, Now[END_REF]. Non-contrast techniques of pulmonary ventilation-perfusion assessment in CF patients (e.g., Fourier decomposition (FD) MRI) have been reported [START_REF] Bauman | Non-contrast-enhanced perfusion and ventilation assessment of the human lung by means of fourier decomposition in proton MRI[END_REF][START_REF] Bondesson | Nonuniform Fourier-decomposition MRI for ventilation-and perfusion-weighted imaging of the lung[END_REF]. These have been validated against photon emission computed tomography (SPECT), dynamic contrast-enhanced MRI and hyperpolarized 3He, which is considered to be the reference standard in assessing lung function using imaging [START_REF] Bauman | Lung ventilation-and perfusion-weighted Fourier decomposition magnetic resonance imaging: in vivo validation with hyperpolarized 3He and dynamic contrast-enhanced MRI[END_REF]. Nonetheless, longitudinal evaluations before and after treatment of ABPA in CF patients using morphological and functional lung MRI are lacking. We hypothesised that FD-MRI could detect response to treatment in CF patients with ABPA. Thus, the main aim of this study was to evaluate the response to treatment of ABPA in CF patients based on FD-MRI functional changes. Our secondary aims were to: (a) evaluate morphological changes before and after treatment of ABPA using UTE-MRI; (b) investigate correlations between both morphological and functional alterations and pulmonary function test (PFT) parameters; and (c) assess the structure-function relationships in CF patients with ABPA.

Material and methods

Study population:

This retrospective longitudinal pilot study was performed in a single center and approved by the local ethics committee. The requirement for written consent was waived. All consecutive CF patients referred to our institution, a tertiary CF center, were screened between August 2018 and September 2021. Inclusion criteria were: (a) diagnosis of CF proven by sweat chloride and/or genetic testing, (b) age older than 6 years, (c) diagnosis of ABPA established by multidisciplinary sessions involving pediatricians, pneumologists, physicians, immunologists, physiologists, and mycologists with full knowledge of the patient's medical history and according to the CFFC criteria [START_REF] Stevens | Allergic Bronchopulmonary Aspergillosis in Cystic Fibrosis-State of the Art: Cystic Fibrosis Foundation Consensus Conference[END_REF]. All included patients underwent lung MRI with UTE and FD sequences before and after treatment of ABPA. Median interval between the two examinations was 5 months (minimum of 3 months and maximum of 9 months). In addition, total IgE and anti-A fumigatus specific IgE levels were measured before and after treatment as well as pulmonary function tests within one week from the lung MRI examinations. Clinical and radiological improvement and/or a drop of at least 25% of total IgE were considered as positive response to ABPA treatments [START_REF] Agarwal | Allergic bronchopulmonary aspergillosis: review of literature and proposal of new diagnostic and classification criteria[END_REF].

Pulmonary Function Tests:

PFTs were assessed using body plethysmography (BodyBox, Medisoft. Leeds, UK).

Reference values were based on American Thoracic Society and European Respiratory

Society guidelines [START_REF] Miller | Standardisation of spirometry[END_REF][START_REF] Wanger | Standardisation of the measurement of lung volumes[END_REF]. Measurements of forced expiratory volume in 1 second (FEV1), forced volume capacity (FVC), FEV1/FVC and forced expiratory flow at 25%-75% (FEF were measured and expressed as percentage of predicted values (FEV1%p, FVC%p, FEV1%p/FVC%p and FEF 25-75 %p, respectively).

MRI examinations:

MRI examinations were completed on a 1.5 Tesla Siemens Aera scanner (Siemens, Erlangen, Germany). Images of a stack of spirals spoiled ultra-short gradient echo sequence (3D-UTE) were acquired in the supine position using the following parameters: TR/TE/flip angle=4.3ms/0.05ms/5° and a voxel size of 1mm 3 . Respiratory synchronization at end normal expiration was allowed by automated self-navigated respiratory gating. Scan duration was between 6 and 8 minutes. Time resolved images of a 2D balanced steady-state free precession (bSSFP) sequence were acquired in two coronal planes: at the level of the carina and posteriorly at the level of the descending aorta. The main pulse sequence parameters were: TR/TE/flip angle=1.74ms/0.71ms/47°, pixel size of (1.2x1.2 mm) 2 and slice thickness = 12 mm. Each bSSFP series consisted of 200 images. Scan duration was around 1 minute per slice. In addition, for IMIS identification, a T2-weighted radial fast spin-echo (RFSE) sequence was acquired using the following parameters: TR/TEs/flip angle=2350ms/20ms-150ms/145°; pixel size of (1.6 x 1.6) mm 2 and slice thickness = 1.6mm. A T1-weighted VIBE sequence was also acquired using the following parameters: TR/TE/flip angle=3.37ms/1.28ms/8°; pixel size of (1.2 x 1.2) mm 2 and slice thickness = 3mm.

Fourier decomposition (FD) image Processing Workflow:

The image processing workflow was fully implemented in Python (version 3.9). After discarding the first 20 bSSFP images, where the steady state condition was not fulfilled [START_REF] Bauman | Non-contrast-enhanced perfusion and ventilation assessment of the human lung by means of fourier decomposition in proton MRI[END_REF], the acquired image series was first elastically registered to a reference image in mid-position between full inspiration and full expiration which was determined automatically using average lung signal variation. The deformable registration was performed using the EVolution algorithm [START_REF] De Senneville | EVolution: an edge-based variational method for non-rigid multi-modal image registration[END_REF], which is based on a similarity term that favours edge alignment and on a diffeomorphic transformation that ensures the preservation of the image topology. Based on this reference image, a manual segmentation of the lung was performed and a region of interest (ROI) drawn within the aorta by two radiologists. The sampling times were used to calculate the fast Fourier Transform (FFT) per pixel in the segmented lung. Ventilation weighted (V) and perfusion weighted (Q) maps were then generated by taking the maximum magnitude of the corresponding peak in the Fourier spectrum using the lung ROI for ventilation and the aorta ROI for perfusion.

Finally, the signal within the V-map and the Q-map was normalized using the mean signal in the aorta ROI of the reference image for each examination.

A muscular ROI was also drawn on the reference image of each examination to check if the overall signal varied between the two examinations before and after treatment.

Functional analysis:

Qualitative evaluation: Defect extents on V and Q maps were assessed using a semi quantitative score (0= absence/negligible, 1=<50%, 2=>50%) regarding the right/left lungs, superior, median or inferior regions for each slice. The score was then averaged between the two acquired slices and ranged between 0 and 12.

Quantitative evaluation: Mean and coefficient of variation (cv) of the signal intensity (SI) of both ventilation and perfusion maps were calculated (i.e., VSI_mean, VSI_cv, QSI_mean and QSI_cv).

Morphological analysis:

A widely used structural alterations scoring system in CF patients (Bhalla score) was implemented [START_REF] Bhalla | Cystic fibrosis: scoring system with thinsection CT[END_REF]. Detection of IMIS was also part of the evaluation [START_REF] Dournes | Allergic Bronchopulmonary Aspergillosis in Cystic Fibrosis: MR Imaging of Airway Mucus Contrasts as a Tool for Diagnosis[END_REF]. Two radiologists with 5 and 10 years experience in thoracic imaging performed morphological and functional evaluations. All the measurements performed in this study were averaged between the two readers.

Structure-function evaluation:

For the structure-function relationships evaluation, a matched 3D UTE-MRI coronal slice was assessed in terms of structural changes (i.e., bronchiectasis without mucus plugging, bronchiectasis filled with mucus plugging and consolidation) alongside the V and Q maps.

Regions were then classified as defect regions or no defect regions and the predominant structural alteration identified. A consensus reading was carried out for these evaluations and scrolling through the adjacent slices of the 3D UTE images was allowed for identification of structural changes.

Statistical analysis:

Statistical analyses were performed using MedCalc software (Version 20.216). Distribution normality was assessed using the Shapiro-wilk test. Data were expressed as medians with [minimum to maximum range] for continuous variables and absolute numbers for categorical variables. Comparisons of paired medians were performed using the Wilcoxon signed-rank test and paired percentages using the McNemar test; correlations were assessed using the Spearman test (correlation coefficient, rho). A p-value<0.05 was considered statistically significant. Reproducibility was assessed using intraclass correlation coefficients (ICCs), with mixed model analysis and absolute agreement option. ICC values were classified as null (=0), slight (>0 and <0.20), fair (≥0.20 and <0.40), moderate (≥0.40 and <0.60), good (≥0.60 and <0.80), very good (≥0.80 and <0.95) and almost perfect (≥0.95) [START_REF] Landis | The measurement of observer agreement for categorical data[END_REF]. Structure-function relationships were evaluated using Chi2 tests.

Results:

Study population

Among 109 CF patients referred to our institution between 2018 and 2021, 29 patients showed an exacerbation attributed to ABPA according to the multidisciplinary care meeting and CFFC criteria. Seventeen patients were not included (nine did not undergo lung MRI before treatment and eight did not have lung MRI after treatment within a maximum interval of one week from the IgE measurements). Twelve patients were therefore included in this study, each having had lung MRI, total IgE and specific IgE measurements and PFTs before and after treatment of ABPA (Figure 1). All included patients showed a positive response to specific treatment (i.e., oral corticosteroids and itraconazole) either with a drop of total IgE or clinical and radiological improvement or both. Patients' characteristics are summarized in Table 1. Median age of the included patients before treatment was 14 years with 7 males and 5 females. The medians of total IgE and specific IgE before treatment were 667.5 UI/ml and 10.5 UI/ml respectively with a significant reduction to 467.5 UI/ml and 8.36 UI/ml respectively after treatment. FEV1%p showed a significant improvement after ABPA treatment with the median FEV1%p increasing from 61% to 67%.

Comparison of functional FD-MRI ventilation and perfusion before and after ABPA treatment

The extent of the ventilation and perfusion defects decreased significantly after ABPA treatment reflecting a significant improvement of the lung function evaluated using FD-MRI (Table 2, Figure 2 and Figure 3).

Using the fully automated quantification of lung ventilation, the median of VSI_mean was significantly increased after ABPA treatment (Table 2, Figure 3) reflecting a global improvement of the lung ventilation whereas QSI_mean showed only a non-significant trend for increase after treatment (Table 2, Figure 3). Regarding the heterogeneity of V map and Q map, the VSI_cv and QSI_cv did not demonstrate significant changes after ABPA treatment (p=0.33 and p=0.62, respectively) (Table 2).

No statistically significant difference was found between the mean and the standard deviation of the signal in the muscular ROIs before and after treatment (p=0.42) (Table E1).

Comparison of morphological Bhalla score before and after ABPA treatment

The median of the MR-Bhalla score was significantly increased after ABPA treatment reflecting a decrease of structural alterations' severity assessed using 3D UTE morphological MRI. In addition, 6 patients showed an IMIS on lung MRI before treatment which were not present on the follow-up MRI (p=0.03) (Table 2).

Correlations with the pulmonary function tests

Before treatment, no significant correlation was found between PFT parameters and lung MRI functional or morphological measurements. After treatment, ventilation defects extent assessed visually was inversely correlated to PFT obstructive parameters (Table E2). Using quantitative imaging, QSI_mean was significantly correlated to FEV1%p and QSI_cv showed a significant inverse correlation with FEF25-75%p (Table E2). The MR-Bhalla score also showed a significant correlation with PFTs' obstructive parameters after treatment (Table E2).

However, only changes in quantitative functional imaging parameters before and after treatment (i.e., ΔVSI_cv and ΔQSI_cv) showed significant correlations with changes of PFT obstructive parameters before and after treatment (ΔFEV1%p and ΔFEF25-75%p) (Figure 4, Table E3).

Assessment of reproducibility

Very good inter-observer reproducibility was obtained for functional and morphological qualitative scores (ICC>0.93). Reproducibility of quantitative measurements was almost perfect, with ICCs >0.99 (Table 3).

Structure-function relationships

A total of 576 regions were evaluated. Three hundred and twenty-two (322) regions had no defect and 254 had defects (130 with Q defects and 124 with V defects). Regarding the defect extent, there were115 defects with less than 50% extent and 139 with more than 50% extent.

According to structural alterations evaluated in this study (Table 4), the presence of bronchiectasis without mucus plugging was found in 13 regions without any defects, and no bronchiectasis without mucus plugging was found in regions with V or Q defects.

Bronchiectasis with mucus plugging was found in 39 regions without any defect and in 232 regions with V or Q defects (Figure 5). Consolidations were found only in regions with V or Q defects (18 regions). Only 4 regions showed functional defects (1 with V defects and 3 with Q defects) without any visible structural alteration. Presence of structural alterations was not different between regions with V defects and regions with Q defects (p=0.06). The presence of functional defects (V or Q) was significantly different between regions with structural alterations and regions with no visible structural alterations. In addition, the presence of functional defects (V or Q) differed significantly according to the structural alterations assessed. Only 6 regions showed a mismatch between V and Q defects (p=0.33).

Moreover, no correlations were found between the MR-Bhalla morphological score and both qualitative and quantitative evaluations of ventilation and perfusion (p>0.30) (Table E4).

Discussion:

We demonstrated in this study that lung functional FD-MRI is able to assess ventilation and perfusion defects in CF patients with ABPA. We also showed that automatic quantification of lung V and Q is feasible in CF patients with ABPA using FD-MRI. Our findings showed that the extent of V and Q defects decreased after positive response to treatment of ABPA in CF patients. Moreover, we found an increase of VSI_mean after treatment. Our qualitative and automatic quantitative methods showed a very good to almost perfect inter-reader reproducibility. Interestingly, only the pre and post-ABPA treatment changes in VSI_cv and QSI_cv, reflecting more homogenous V and Q maps following treatment, were found to correlate with pre-and post-ABPA treatment changes of FEV1%p. Finally, we showed that functional defects are associated with structural alterations, especially with mucus plugging and consolidations.

Regarding morphological lung MRI, our findings are in line with previously reported results which showed a complete resolution of IMIS in the follow-up [START_REF] Dournes | Allergic Bronchopulmonary Aspergillosis in Cystic Fibrosis: MR Imaging of Airway Mucus Contrasts as a Tool for Diagnosis[END_REF]. Moreover, a recent study [START_REF] Munidasa | Free-breathing MRI for monitoring ventilation changes following antibiotic treatment of pulmonary exacerbations in paediatric cystic fibrosis[END_REF] has shown that free breathing phase resolved functional lung MRI imaging could detect improvement following exacerbation treatments using antibiotics out of the context of ABPA.

Furthermore, recent studies have demonstrated the benefit of using other MRI techniques (i.e., diffusion weighted imaging and T2 weighted imaging) in monitoring CF exacerbations [START_REF] Ciet | Detection and monitoring of lung inflammation in cystic fibrosis during respiratory tract exacerbation using diffusion-weighted magnetic resonance imaging[END_REF][START_REF] Benlala | Quantification of MRI T2-weighted High Signal Volume in Cystic Fibrosis: A Pilot Study[END_REF].

In this study, functional defects were found to be associated with ABPA exacerbation and showed a significant decrease after specific treatment. Indeed, CF patients with ABPA are prone to more severe exacerbations and require a specific treatment and repeated imaging.

However, iterative radiation exposure raises concerns in young CF patients with a need of a radiation-free alternative. Moreover, FD-MRI is performed without any contrast agent administration, and thus avoids long-term effects of MRI contrast deposition [START_REF] Rozenfeld | Gadolinium-based contrast agents in children[END_REF].

In this study, automatic quantification of VSI and QSI also showed an improvement inof global respiratory function after specific ABPA treatment. We used a simple normalization technique to account for signal intensity bias in longitudinal examinations and we also confirmed that the signal in a muscular ROI was not different between the pre-and posttreatment examinations. Interestingly, the pre-and post-treatment changes of VSI_cv and QSI_cv, which represent the homogeneity of V and Q maps, were correlated to the pre-and post-treatment change of FEV1%p. Thus, the decrease in the number and the extent of functional defects was associated with more homogenous maps and improved PFTs obstructive parameters. Insights into local and regional respiratory function allowed by the FD-MRI has potential to bring complementary information to the global evaluation of the respiratory function using PFTs in addition to the morphological information available from 3D UTE-MRI.

Morphological MR-Bhalla score was also found improved in the follow-up, which is in line with previously published data on structural changes in the follow-up of exacerbations in CF patients [START_REF] Shah | High-resolution CT in the acute exacerbation of cystic fibrosis: evaluation of acute findings, reversibility of those findings, and clinical correlation[END_REF][START_REF] Davis | Computed tomography reflects lower airway inflammation and tracks changes in early cystic fibrosis[END_REF][START_REF] Byrnes | Prospective evaluation of respiratory exacerbations in children with cystic fibrosis from newborn screening to 5 years of age[END_REF][START_REF] Wielpütz | Magnetic resonance imaging detects changes in structure and perfusion, and response to therapy in early cystic fibrosis lung disease[END_REF].

In our study, we found that functional defects were associated with structural lung alterations without any distinction between V defects and Q defects. However, bronchiectasis without mucus plugging was not associated with functional defects assessed by using FD-MRI. Indeed, airway obstruction is more likely responsible for matched ventilation-perfusion defects. However, since we did not assess the relationship between the severity of the bronchiectasis and functional defects, our findings need to be interpreted with caution.

Limitations

First, this was a retrospective pilot study in a small number of patients. However, the patients included had a high confidence diagnosis of ABPA and also had IgE serological measurements performed before and after treatment close to the MRI examinations. However, large prospective cohorts to validate our findings are needed. Second, in this study, we did not correlate FD-MRI findings to dynamic contrast enhanced (DCE) MRI or to hyperpolarized gas MRI. However, functional FD-MRI has already been validated using these techniques [START_REF] Bauman | Lung ventilation-and perfusion-weighted Fourier decomposition magnetic resonance imaging: in vivo validation with hyperpolarized 3He and dynamic contrast-enhanced MRI[END_REF][START_REF] Bauman | Validation of Fourier decomposition MRI with dynamic contrast-enhanced MRI using visual and automated scoring of pulmonary perfusion in young cystic fibrosis patients[END_REF]. Third, although our qualitative evaluation of functional defects showed a high interreader reproducibility, it could be worth using an automatic relative quantification of defects based on threshold techniques [START_REF] Nyilas | Novel magnetic resonance technique for functional imaging of cystic fibrosis lung disease[END_REF]. Finally, a further comparison of different non-rigid image registration algorithms may provide information about the robustness of these findings regarding preprocessing techniques.

Conclusion

Functional changes in CF patients with ABPA may be reproducibly assessed using FD lung MRI and correlate with pulmonary function test changes after specific treatment of ABPA.

Therefore, non-contrast enhanced functional FD lung MRI has potential be used to monitor treatment of ABPA in CF patients.

Figure legends:Figure 1 :

 1 Figure legends:

Figure 2 :

 2 Figure 2: Coronal 2D balanced steady-state free precession image of the lungs of a 16 year old male CF patient before (A) and after (D) ABPA treatment. Note the matched defect of ventilation (B) and perfusion (C) before specific treatment (white arrows) with the decrease in the extent of the defects after treatment on both ventilation map (E) and perfusion map (F).

Figure 3 :

 3 Figure 3: Paired comparisons of the qualitative and quantitative functional FD-lung MRI before and after ABPA treatment. ABPA= allergic bronchopulmonary aspergillosis ; V= ventilation; Q= perfusion; VSI= ventilation signal intensity; QSI= perfusion signal intgensity ; au= arbitrary unit.

Figure 4 :

 4 Figure 4: Spearman correlations between the change of FEV1%p and the change of both ventilation and perfusion coefficents of variation before and after treatment (ΔVSI_cv and ΔQSI_cv).

Figure 5 :

 5 Figure 5: Matched coronal slice of the lung UTE-MRI image and FD ventilation map of a 16 year old male CF patient before (A, B) and after (C, D) ABPA treatment. Note the ventilation defect indicated by the solid arrows before (B) and after (D) treatment and the structural alterations (hollow arrows) seen on the UTE-MRI images showing mucus plugging (A) decreasing after treatment (C).

Table 1 . Clinical and functional characteristics of study participants

 1

	Tables:
			Before Treatment	After Treatment	p-value
			(n=12)	(n=12)
	Age	Years	14 [6.5-32]	14 [7-33]	<0.01
	Sex ratio	Male/Female	7 / 5	7 / 5	0.77
	BMI	kg.m -2	18.5 [13-22]	19 [14-23]	0.95
	Mutation ΔF508	Homozygous (yes/no)	7 / 5	7 / 5	0.77
	Total IgE	UI/ml	667.5 [221-3529]	467.5 [26-1917]	<0.01
	Anti-A
	fumigatus	UI/ml	10.5 [2.15-32.3]	8.36 [0-25.3]	<0.01
	Specific IgE
	PFT	FEV 1 (%p)	61 [20-106]	67 [22-126]	<0.01
		FVC (%p)	78 [30-107]	93 [32-131]	<0.01
		FEV 1 /FVC	74 [55-100]	71 [51-103]	0.84
		FEF 25_75 (%p)	28 [8-95]	32.5 [9-104]	0.15
	Note: Data are median with [minimum to maximum range] for continuous variables and absolute
	number for categorical variables. Legends: ABPA= allergic bronchopulmonary aspergillosis;
	BMI=body mass index; IgE= immunoglobulin E; FEV1=forced expiratory volume in 1 second;
	FVC=forced volume capacity; FEF25-75=forced expiratory flow at 25%-75%; %p=percentage of
	predicted value

Table 2 . Imaging characteristics of study participants

 2

			Before Treatment	After Treatment	p-value
			(n=12)	(n=12)
	Qualitative
	Analysis
	Functional evaluation	V defect score	4.125 [3-10.75]	2 [1-7.25]	<0.01
		Q defect score	5 [2.5-10.75]	2.75 [0-7]	<0.01
	Morphological evaluation	MR-Bhalla score	12.75 [9-21]	15.5 [9.5-21]	0.02
		IMIS (present/absent)	6/ 6	0/ 12	0.03
	Quantitative
	Analysis
		VSI_mean (au)	167 [100-532]	280 [112-535]	<0.01
		VSI_cv	1.18 [0.96-2.09]	1.16 [0.62-1.76]	0.33
		QSI_mean (au)	112 [53-298]	122 [46-282]	0.85
		QSI_cv	1.32 [0.99-1.97]	1.30 [0.82-1.92]	0.62
	Note: Data are median with [minimum to maximum range] for continuous variables and absolute
	number for categorical variables. Legends: IMIS= inverted mucus signal intensity; V=
	ventilation; Q= perfusion; VSI= ventilation signal intensity; QSI= perfusion signal intensity; au=
	arbitrary unit; cv= coefficient of variation

Table 3 . Interobserver reproducibility of qualitative and quantitative analyses

 3

	N=24

Legends: ICC= intraclass correlation coeficient ; CI= confidence interval ; ABPA= allergic bronchopulmonary aspergillosis; V= ventilation; Q= perfusion; VSI= ventilation signal intensity; QSI= perfusion signal intensity; cv= coefficient of variation


Arthur Longuefosse

Gaël Dournes

Ilyes Benlala

Baudouin Denis De Senneville

François Laurent

Pascal Desbarats

Fabien Baldacci

Denis Baudouin

De Senneville

Lung CT Synthesis Using GANs with Conditional Normalization on Registered Ultrashort Echo-Time MRI

Keywords: CT Synthesis, Lung, UTE MRI, Generative Adversarial Networks

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Lung imaging using MRI is still a major challenge in medical imaging, owing to the low proton content, the rapid decay of lung signal due to susceptibility artifacts and respiratory motions. Still, lung MRI would be a desirable approach to computed tomography (CT) given its lack of radiation exposure, and the possibility to combine both morphological and functional information. Recently, the advent of lung MRI with ultrashort or zero echo-time (UTE/ZTE) appears to have initiated some advances to obtain structural imaging of the lung at high resolution [START_REF] Dournes | Quiet Submillimeter MR Imaging of the Lung Is Feasible with a PE-TRA Sequence at 1[END_REF][START_REF] Dournes | 3D ultrashort echo-time MRI of the lung using stack-of-spirals and spherical k -Space coverages: Evaluation in healthy volunteers and parenchymal diseases: Lung MRI With 3D UTE Spiral VIBE Sequence[END_REF]. However, due to differences in lung appearance (cf Figure 1), notably imaging texture, blurring and noise, that would still substantially differ from that of CT, the UTE/ZTE technique has not been widely adopted for clinical use. Although several statements have been published to encourage its clinical use [START_REF] Hatabu | Expanding Applications of Pulmonary MRI in the Clinical Evaluation of Lung Disorders: Fleischner Society Position Paper[END_REF][START_REF] Dournes | The Clinical Use of Lung MRI in Cystic Fibrosis: What, Now[END_REF], other studies reported that this MR technique still requires specific MRI training and expertise from radiologists, which would sometimes prevent its clinical adoption [START_REF] Gräfe | Pediatric MR lung imaging with 3D ultrashort-TE in free breathing: Are we past the conventional T2 sequence?[END_REF][START_REF] Sodhi | Practical protocol for lung magnetic resonance imaging and common clinical indications[END_REF]. Moreover, several UTE/ZTE sequence schemes have been published, and a current issue with this novel technique remains the standardization of results. We believe that the generation of CT images from MR could solve the main issues of lung imaging, especially by using UTE MR images as input. Over the recent years, deep learning approaches, notably Generative Adversarial Networks (GAN) [START_REF] Goodfellow | Generative adversarial nets[END_REF], have been extensively studied for image synthesis in medical imaging. Previous methods in cross-modality synthesis have used GANs in several regions, such as brain [START_REF] Wolterink | Deep MR to CT Synthesis using Unpaired Data[END_REF][START_REF] Nie | Medical Image Synthesis with Context-Aware Generative Adversarial Networks[END_REF], aorta [START_REF] Wang | Unsupervised Learning for Cross-Domain Medical Image Synthesis Using Deformation Invariant Cycle Consistency Networks[END_REF], pelvic region [START_REF] Lei | MRI-Only Based Synthetic CT Generation Using Dense Cycle Consistent Generative Adversarial Networks[END_REF]. A study has also been performed on the generation of thoracic CTs from Dixon MRI [START_REF] Baydoun | Dixon-based thorax synthetic CT generation using Generative Adversarial Network[END_REF], but, although it can be useful for radiation treatment, the results do not allow reconstruction of fine structures inside the lung, such as vessels and bronchi. The vast majority of these methods are based on unpaired training, notably with the development of cycle-consistent adversarial networks (Cy-cleGAN) [START_REF] Zhu | Unpaired Imageto-Image Translation Using Cycle-Consistent Adversarial Networks[END_REF]. This type of network only requires that the backward transformed image is similar to the original source image, which can lead to visually convincing results, at the expense of adding excessive deformations [START_REF] Cao | Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis[END_REF]. Methods based on paired data are often neglected, since CT and MR images are acquired on separate scanners, and slight voxelwise misalignment between MR and CT images may lead to synthesis of blurred images [START_REF] Wolterink | Deep MR to CT Synthesis using Unpaired Data[END_REF][START_REF] Kaji | Overview of image-to-image translation by use of deep neural networks: denoising, superresolution, modality conversion, and reconstruction in medical imaging[END_REF]. However, recent methods in multi-modal deformable image registration, based on edge alignment, seems particularly well suited for the generation of paired dataset with images from different modalities [START_REF] Denis De Senneville | Evolution: an edge-based variational method for non-rigid multi-modal image registration[END_REF]. In this work, we propose a complete framework for the generation of thoracic CT, based on ultrashort echo-time MRI. We use an accurate edge-based deformable registration as an input to compare state-of-the-art paired image-translation GANs. We demonstrate the applicability of our method and the benefits of using the SPADE [START_REF] Park | Semantic Image Synthesis with Spatially-Adaptive Normalization[END_REF] generator, by evaluating synthetic CTs with quantitative and qualitative metrics. Finally, we illustrate the robustness of the MR-to-CT translation by applying bronchial segmentation algorithms, originally designed for real CT, on synthesized CT.

MATERIALS

This study is performed on MR and CT images of 110 patients in the routine follow-up of cystic fibrosis, from 2018 to 2022. Both modalities were acquired on the same day. The CT images were obtained using a Siemens SOMATOM Force and a GE Medical Systems Revolution CT, in end-expiration, with sharp filters. The parameters used were a DLP of 10 mGy.cm and a SAFIRE iterative reconstruction. UTE MR images were acquired using the SpiralVibe sequence on a SIEMENS Aera scanner, with the following parameters : TR/TE/flip angle=4.1ms/0.07ms/5°. Since the slice plane is encoded in Cartesian mode, native acquisition was performed in the coronal plane with field-of-view outside the anterior and posterior chest edges to prevent aliasing. To avoid artifacts due to chest motion, prospective respiratory gating was used. To this end, the user had to check which coil element was the closest to the area of maximum respiratory motions, typically the diaphragm.

METHOD

We present here the state-of-the-art deep learning models used in paired image synthesis. The pix2pix model [START_REF] Isola | Image-to-Image Translation with Conditional Adversarial Networks[END_REF], uses a conditional GAN to learn a mapping from input to output images. It consists of a generator G that tries to minimize adversarial and pixel-wise loss functions, and a discriminator D that tries to maximize adversarial loss function. The adversarial loss is defined as

L GAN (G, D) = E y [logD(y)]+E x [log(1-D(G(y)))], (1)
The pix2pix model can produce realistic translations, but is still dependent on the L1 distance, which tends to yield blurry target images [START_REF] Isola | Image-to-Image Translation with Conditional Adversarial Networks[END_REF][START_REF] Larsen | Autoencoding beyond pixels using a learned similarity metric[END_REF]. In [START_REF] Wang | High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs[END_REF], the authors proposed an improvement of the pix2pix method, namely pix2pixHD, by using a multi-scale discriminator and a robust adversarial learning objective function. This method is no longer dependent on the pixel-wise loss but on a new feature matching loss, defined as

L F M (G, D) = E x,y T i=1 1 N i [||D (i) (x, y) -D (i) (x, G(x))|| 1 ]
(2) T being the total number of layers, N the number of elements in each layer, and D (i) the i-th layer of D. This loss helps to stabilize the training, since the generator has to produce natural features at multiple scales.

The authors of pix2pixHD also experimented adding a perceptual loss, based on the pre-trained VGG network :

L V GG (x, y) = T i=1 1 N i [||F (i) (y) -F (i) (G(x))|| 1 ] (3)
F (i) being the i-th layer with N i elements of the VGG network. This loss is closer to perceptual similarity than traditionnal pixel-wise losses and slightly improves the results. Finally, SPADE [START_REF] Park | Semantic Image Synthesis with Spatially-Adaptive Normalization[END_REF] focuses on normalization layers, which tend to discard semantic information due to their non-conditional approach. For that purpose, this method introduces a spatially adaptive normalization based on the inputs, that improves the performance and reliability of the generator. The SPADE architecture can be integrated in other models, such as pix2pixHD, to apply additional constraints on the inputs and guide training.

EXPERIMENTS AND RESULTS

The aim of this study is to compute and compare synthesized CT volumes based on the state-of-the-art paired methods, and determine if the conditional normalization presented in SPADE improves the performance of the reconstruction. Although 3D GANs allow perception of volumetric and neighborhood spatial information, they involve an excessive computational cost and a reduction of the number of samples in the dataset. Therefore, we choose to train the models on the 2D axial slices of the CT and MR volumes. Unlike most studies in the literature, we keep a voxel size of 0.6 × 0.6 × 0.6 mm 3 for both modalities, which allows us to obtain high resolution 2D input and output slices of size 512x512. After inference, the acquired 2D synthetic CT slices can be stacked to reconstruct the synthetic CT volume.

We present here the preprocessing applied to generate the 2D slices, the training architecture and parameters, as well as a quantitative evaluation to compare the methods.

Preprocessing

To generate paired sets, all volumes are resampled on a common grid with a voxel size of 0.6 × 0.6 × 0.6 mm 3 . CT images are aligned on MR images using a rigid translation based on a gradient-driven optimization scheme [START_REF] Irani | Robust multi-sensor image alignment[END_REF], and a deformable registration is applied using the EVolution algorithm [START_REF] Denis De Senneville | Evolution: an edge-based variational method for non-rigid multi-modal image registration[END_REF][START_REF] Lafitte | Accelerating multi-modal image registration using a supervoxel-based variational framework[END_REF]. This method is based on a similarity term that favors edge alignment, and on a diffeomorphic transformation that ensures the preservation of the CT volume topology.

We perform a manual verification to detect images acquired at different times of the respiratory activity, that lead to poorly registered volumes, and remove them from the training set.

Training

All models are trained using the same procedure and architecture defined in SPADE [START_REF] Park | Semantic Image Synthesis with Spatially-Adaptive Normalization[END_REF], apart from the dataloader and inference parts, which have been adapted to support 16-bit input and output arrays. We use the Adam optimizer [START_REF] Kingma | Adam: A Method for Stochastic Optimization[END_REF], a batch size of 1, a learning rate of 0.0001 and ReLU activation for the generator, and a learning rate of 0.0004 and LeakyReLU activation for the discriminator. The loss function is defined as a combination of GAN, feature-matching and VGG losses.

The training process includes 100 epochs, and lasted around 400 hours on a 12GB NVIDIA RTX 2080 Ti. MR to CT inference with a trained model requires around thirty seconds on one single GPU.

Quantitative Validation

Fig. 1 shows an example UTE MR input slice, synthesized slices obtained with pix2pixHD and SPADE methods, and the reference CT slice. Both models achieve to produce a high quality CT slice, but differs on some aspects ; the pix2pixHD output presents a noise-free image, an accurate reconstruction of the bones and body, but seems to deviate from the CT at the level of the lung parenchyma, in particular with the appearance of bronchi not visible on the ground-truth CT.

Conversely, the SPADE output presents a slightly less sharp image, a less accurate reconstruction of the bones, but a very faithful synthesis of the lung parenchyma, in particular at the level of the vessels and bronchi.

To validate these assumptions, we reconstruct the 3D CT volumes by stacking each 2D slices, and compute standard metrics, namely MSE, cross correlation and SSIM between ground truth CT volumes and synthesized CT volumes. In order to compare only the intensities inside the lung parenchyma, these metrics are constrained on the intersection between the CT lung mask and the generated CT lung masks, such that the results reflect the quality of vessels and bronchi reconstruction (Table 1). Results show that SPADE, i.e. pix2pixHD method combined with a conditional normalization layer, provides better performances than the pix2pixHD method alone. We can therefore argue that the use of the spatially adaptive normalization helps to ensure content preservation by adding constraints on the input MRI, and avoids the hallucination of anatomic structures, a typical trait of GANs [START_REF] Cohen | Distribution Matching Losses Can Hallucinate Features in Medical Image Translation[END_REF].

Regarding bone reconstruction, we assert that since bones in MRI have low intensities and bones in CT have high intensities, using a spatial normalization such as SPADE will add constraints on the MRI input and will eventually lead to a less accurate reconstruction of the synthesized CT bones. From a clinical point of view, the importance seems to remain in the precise reconstruction of structures in the lung parenchyma, such as vessels and bronchi, notably to allow the detection and evaluation of severity of diseases such as bronchiectasis.

DISCUSSION

In this study, we demonstrate that paired image-to-image translation GANs can be trained to synthesize a lung CT image from UTE MRI. Unpaired methods, such as Cycle-GAN [START_REF] Zhu | Unpaired Imageto-Image Translation Using Cycle-Consistent Adversarial Networks[END_REF], have often been employed in the literature. From our experiments, this kind of models can lead to an alteration of fine structures, and artifacts not present in the input image may be added. These observations are in correlation with statements in state-of-the-art [START_REF] Kaji | Overview of image-to-image translation by use of deep neural networks: denoising, superresolution, modality conversion, and reconstruction in medical imaging[END_REF][START_REF] Cao | Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis[END_REF], and validated our initial assumption of using paired methods.

The quantitative evaluation (Table 1) shows that the correspondence between synthesized and ground truth CT images using the pix2pixHD model combined with SPADE normalization is superior to the pix2pixHD model alone, that can lead to false positives in the generation of fine structures in lung parenchyma. Regarding the assessment of results, we found it more relevant to use standard metrics, such as MSE, SSIM, and cross correlation, constrained within the lung masks, rather than GAN metrics such as FID [START_REF] Heusel | GANs trained by a two time-scale update rule converge to a local nash equilibrium[END_REF] and KID [START_REF] Bińkowski | Demystifying MMD GANs[END_REF], that have been proven to be insensitive to the global structure of the data distribution, since they are based on extrinsic properties [START_REF] Tsitsulin | The Shape of Data: Intrinsic Distance for Data Distributions[END_REF].

Fig. 2: Airways segmentations from real CT (red) and SPADE (green), using the NaviAirway pipeline [START_REF] Wang | Navi-Airway: a bronchiole-sensitive deep learning-based airway segmentation pipeline for planning of navigation bronchoscopy[END_REF].

To illustrate the accurate reconstruction of the SPADE model, we compute the airways segmentations of synthesized and ground truth CT using NaviAirway [START_REF] Wang | Navi-Airway: a bronchiole-sensitive deep learning-based airway segmentation pipeline for planning of navigation bronchoscopy[END_REF], a bronchiolesensitive airway segmentation pipeline designed for CT data. Results, presented in Figure 2, show that the synthetic CT volume achieves to generate high quality airways ; the generation levels of the bronchial tree are nearly as deep as the ground truth, and present very few false positives.

A qualitative validation based on the correlation coefficient of the bronchial dilatation subscores [START_REF] Bhalla | Cystic fibrosis: scoring system with thin-section CT[END_REF] has also been conducted by a radiologist (Table 2), and illustrates the diagnostic improvement of synthetic CT over UTE MRI. These results are most likely due to the better overview of the lung from the synthetic CT, along with denoising of the initial MR image and enhanced visualization of structures. Despite these positive results, there is still room for improvement, such as the use of 3D information for training instead of 2D slices, which could lead to a better reconstruction of the CT volume. Future works will aim at a pseudo-3D training, which can alleviate the GPU constraints of traditional 3D training, by using each of the 2D axes (axial, coronal, sagittal) and reconstructing the 3D volume using the information of each axis.

CONCLUSION

A complete framework using paired generative adversarial networks is presented to synthesize lung CT volumes with UTE MR volumes as inputs. From a cohort of 110 patients, with both modalities acquired on the same day, we generated paired dataset using an accurate edge-based deformable registration, to compare state-of-the-art image-to-image translation methods. Based on obtained results, we have seen that the pix2pixHD model can provide high quality images at the expense of false positives in fine structures. We demonstrated that the use of a spatially conditioned normalization, namely SPADE [START_REF] Park | Semantic Image Synthesis with Spatially-Adaptive Normalization[END_REF], can overcome these false positives by adding constraints on the inputs, and delivers a promising high quality generation that could have implications for MR-only radiotherapy treatment. The reconstructed synthetic CT volume enables the use of algorithms originally designed for real CT, such as lung mask segmentation [START_REF] Hofmanninger | Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem[END_REF] and airways extraction [START_REF] Wang | Navi-Airway: a bronchiole-sensitive deep learning-based airway segmentation pipeline for planning of navigation bronchoscopy[END_REF]. Future works will aim at adding 3D information in the training process, and also developing a model specific to the constraints of lung synthesis (i.e. preserving morphological and functional information during the CT translation) while extending evaluation by using a segmentation of the bronchi and vessels, and a comparison of quantitative 3D measures.

Fig. 1 :

 1 Fig. 1: Comparison between MR axial slice, synthetic CT from pix2pixHD and SPADE models and ground truth CT.

Table 1 :

 1 Mean squared error (MSE), cross correlation (CC) and structural similarity index (SSIM) between real CT and synthesized CT, based on intensities inside the intersection of lung masks [23]. MSE CC SSIM pix2pixHD 81.6 ± 22.6 0.860 ± 0.05 0.907 ± 0.02 SPADE 51.3 ± 15.7 0.893 ± 0.04 0.922 ± 0.01

  To speed up calculations and guide training, an automatic lung segmentation of the registered CT images is computed using the U-net R-231 convolutional network proposed in[START_REF] Hofmanninger | Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem[END_REF]. This segmentation allows to extract the Volume of Interest (VOI) of lungs CT, which also corresponds to the VOI of lungs MR, given that CT is registered on MR. CT intensities are then

	cropped to [-1000; 2000] Hounsfield unit and rescaled to [-1;
	1]. Since MR intensity values strongly depend on acquisi-
	tion parameters, we normalize them using zero mean and unit
	variance, crop the values to [-4σ ; 4σ] to remove outliers,
	σ being the standard deviation, and rescale them to [-1; 1]
	based on minimum and maximum intensities. All axial slices
	are then either cropped or zero-padded to 512 × 512, depend-
	ing on the CT lung mask size, and saved as 16-bit NumPy
	arrays. The training set is composed of 82 patients (33002
	slices), and the testing set is composed of 28 patients (10725
	slices).

Table 2 :

 2 Correlation of bronchial dilatation subscores between UTE MRI and SPADE synthetic CT with real CT, based on a radiologist with 4 years of experience in thoracic imaging.

		UTE MRI	SPADE
	Correlation with real CT	0.70	0.91