Claudio Stagni

Massimo Carlotta Guiducci

Luca Benini

Hardware-Software Design of a Smart Sensor for Fully-Electronic DNA Hybridization Detection

This paper describes the design of a smart sensor f o r detection of DNA hybridization. The sensor is based on a direct electrical transduction principle: it measures impedance variation at the interface between a bioelectrode and a solution containing the anal-vte. The smart sensor includes a complete signal conditioning and processing subsystem based on an embedded

We outline the sensor architecture, and we describe in details board-level integration as well as hardware and implementation and design choices. The accuracy of' our embedded solution has been evaluated by comparing it with a high-cost laboratory setup. we provide measurements of real sensing structures which demonstrate thefunctionality of our system.

Introduction

Smart sensors, which integrate transducers, signal conditioning and processing electronics in a single device, have played an important role in changing our society and lifestyle. These devices help people in collecting critical information on their environment and they often are embedded in complex control systems that automatically adapt environmental characteristics temperature, luminosity) to the user's preferences. The last few years have witnessed an explosive growth of embedded applications for smart sensors. For example virtually every new car contains numerous smart sensors for engine control, safety and travel comfort [

The main motivation for the widespread diffusion of these devices is their low (and decreasing) cost, coupled with high flexibility and ease of deployment.

The implementation of a smart sensor is the end point of an evolutionary path whose key phases can be summarized as follows: (phase-])discovery of a sensing and transducprinciple; (phase-2) quantitative analysis and modeling, (phase-3)development of high-cost sensors for laboratory applications, (phase-4)miniaturization and board-level integration based on commodity electronic components, (phase-5)single-chip integration. Deployment of smart sen-sors in embedded systems is possible starting from the fourth phase. In many cases single-chip integration is never achieved because of its high NRE cost and it many technology challenges.

In this paper we focus on bio-sensors and, more specifically, on genetic sensors, which can detect the key biomolecular interactions at the basis of gene expression processes. In this area, a new and important line of sensors development is based on the direct electrical detection of the main bio-molecular interaction in genetics, namely DNA hybridization. Highly-parallel DNA hybridization detection systems (know as DNA-microarrays [ are widespread in laboratories, and they have rapidly become one of the main tools used by biologists for analyzing the genetic origins of biological processes. Microarrays are based on an optical sensing and transduction principle, which implies extra steps in processing input samples (a process known as labeling) as well as expensive optical transducers and postprocessing equipment.

For this reason, the leading commercial bio-sensors for DNA hybridization are "phase three" products: they are designed for laboratory usage, hence they are expensive, hard to use and not accessible to the general public or event to physicians for point-of-care usage. Several research efforts are under way, aiming at the development of new DNA hybridization detection systems with the potential for becoming phase-4 (andphase-5) smart sensors. These devices are generally characterized by label-free operation as well as direct electrical detection of hybridization. Even though several label-free direct electrical DNA hybridization sensors have been proposed in the literature 13, 18, none has been yet developed beyond phase-or phase-2.

The main contribution of this paper is the first selfcontained design of a capacitive label-free DNA sensor based on COTS components sensor). Our implementation integrates the sensor within basedboard, which enables customization and self-tuning by exploiting the flexibility of programmable components. The accuracy of the phase-4 implementation has been assessed by comparing it with a laboratory setup which interfaces the sensor with data acquisition and signal gen-instruments and manipulates incoming data with a PC equipped with standard test, measure and control software.

The paper is organized as follows: in the next section we provide basic background information on smart sensors and on DNA sensors. Section 4 describes the transduction principle. Section 5 describes the hardware and software design of our smart sensor. Section 6 details experimental results and provide a comparison with a high-cost laboratory setup. Conclusions are drawn in Section 7.

Background

In the present landscape of commercially available sensing devices, a key role is played by sensors fabricated using processes derived from the microelectronic industry. Among the main reasons of the success of these devices is that they are integrated in a limited space and at low cost with other (traditional) electronic parts dedicated to signal processing, data collection, interfacing and, possibly, wireless remote connection. All these functions, if not performed by the sensor device itself, result quite time and resource consuming and not so accurate as if performed insitu. Biosensors, sensors incorporating biological elements, are finding use in increasingly broader ranges of applications, such as clinical diagnosis food production and analysis, and many others. Within the broad field of biosensors, DNA sensors aim at the detection of the specific binding reaction between two molecules of DNA (hybridization). A detailed treatment of the biochemical basis of hybridization is beyond the scope of this paper (the interested reader is referred to [ for an excellent introductory treatment). For our purpose, it is sufficient to understand that DNA molecules can be described as linear sequences of four different basic components (called cleotides). DNA can be found in two states: single-stranded a single linear sequence) and double-stranded. During hybridization, two single-stranded molecules bind to become a double-stranded molecule. This chemical reaction takes place (with high probability) only if the cleotide sequences of the two single-stranded molecules on a nucleotide-by-nucleotide basis. DNA sors use custom-designed single-stranded DNA molecules as recognizing elements (probes) for sub-sequences in the DNA of test samples. If hybridization between the probe and the sample DNA can be reliably detected, then the ge- netic signuture of the organism can be determined by virtue of the one-to-one affinity relationship.

Previous Work

Micro-arrays based on optical detection of DNAhybridization represent the state of the art [ and are now are capable of testing hundreds of thousands different probes in parallel (Fig.

Each site of the array hosts probes with a unique sequence. Sample sequences which need to be identified are labelled with mophores molecules and spread on the array surface. After specific recognition between the sample and its complementary probe the device is observed optically to localize the chromophores, revealing the unknown sample sequence. This technology presents two main limitations: the cost of the instrument which has to detect and transduce the optical signal and, as already mentioned, the necessity of a labelling step which implies additional reagents and can lead to sample pollution. One rection of evolution, which addresses the first limitation of DNA microarray, is fully electrical detection. It still exploits labels (enzymes) bound on the target DNA, which generate red-ox currents upon hybridization, when the solution containing the sample is polarized with a potential difference

The obvious advantage of this approach is avoiding external complex and expensive optical instruments without decreasing performance, in particular sensibility, of the sensors with respect to optical detection.

To further advance the state of the art, researchers are developing label-free techniques to avoid the modification of DNA targets, further reducing costs and time 15, 18, 4, 9, 16, 2, Among these attempts, a fully-electrical technique based on the measurement of the changes of electrical properties (impedance) of the interface has been implemented starting from standard electrochemical interface analysis methodologies as perometry impedance spectroscopy [ or exploiting field effect devices

The most relevant disadvantage of these techniques is the use of a three-electrode set-up that increases the complexity of the system. Commercial implementations of sensors based on impedance label-free detection have still not been presented.

Sensing Principle

Our system is based on an electrochemical approach which exploits a label-free detection technique based on capacitance measurements of bio-modified interfaces It requires the immobilization of The interface can be modeled with capacitance and resistance as shown in Fig. 2.

The DNA interface modification introduces insulating layers between electrode and solution and the value of the capacitive component is strongly affected by their physical characteristics. When DNA hybridization occurs, target molecules are blocked near the surface and change the structure of the interface, in particular they increase the distance between electrode and solution, thereby modifying the impedance of the interface. The charge-based capacitance (CBCM) technique [5] can measure the variation of the capacitive component of the solution interface impedance. The principle of this technique is to charge and discharge, at an appropriate frequency, the impedance under test and to measure its equivalent capacitance from the average current in a half-period (Fig. 3 ) . In fact, ideally, average is proportional to capacitance, to frequency and to the step voltage applied to the capacitance itself, as shown in the following equation:

Where is known. To extract capacitance, one can measure I , t ,,? for different frequencies and calculate the slope of the interpolated curve versus . This method has been widely employed for the measurement of on-chip parasitic capacitances [ 3 ] reaching very high accuracy limits. Alternatively, the current signal can be converted to a digital voltage signal and processed by a PC to perform averaging and capacitance extraction (Fig. 3).

The main advantage of CBCM technique compared to standard complex impedance meters based on analog soidal signals is that it lends itself to a simple using digital input signals. In fact, the analog part can be reduced to an converter followed by an AID converter. A laboratory setup for performing CBCM requires a signal generator, to control the switches, and a current meter. The data coming from the current meter must then be processed by a PC to perform averaging and capacitance extraction.

Hardware and Software Design

The purpose of our design is to develop a low cost system to replace all the laboratory instruments that are connected to the CBCM cell, as well as the data-processing software running on the PC. The output of the system is in digital form, and can be either directly visualized on a LCD or forwarded to other subsystems through standard digital links USB, etc.). Our implementation is based on p-controller that processes directly the signals produced by the cell. The p-controller also controls pulse generation (frequency and amplitude) by mean of a DAC converter, processes data sampled by the ADC converter and calculates in charge and discharge cycles. In the next subsection we describe in detail both the hardware and software design of our smart sensor.

Hardware

As shown in Fig. 4 the system set-up is based on controller that controls one DAC device, to generate a square wave input signal to the cell, and an ADC device which samples and sends output cell current values (converted to voltages) to the On the board there is also an OP-AMP with tunable gain to amplify output signal. At the end of the measurement, the capacitance value is available in digital form on a serial interface. The conversion is performed with a precision resistor of appropriate value related to the capacitance range.

The p-controller is an 128 8-bit, high performance, low power consumption and very low cost. It is the core of the system, since it controls time and synchronization among all components. The possibility to program this device is the most important point to guarantee flexibility and portability. The control word is sent every time it is necessary to change the output signal, ing an SPI protocol. The component, used to amplify the output signal, is a low noise OPAMP and it is connected with a variable feed-back resistance to regulate gain. The ADC is a MAX186 a bits and 8 channels converter. In case of both positive and negative values (as in our case), a complement integer between to is generated.

ference is below a small threshold defined a priori, the frequency is low enough to finish the discharge (or charge) transient and the current value reaches a constant value. The procedure starts from the maximum frequency, takes 2 5 samples before the following square-wave edge, calculates partial areas, and compare their areas: if the condition imposed by the threshold is verified for 10 consecutive transients, then the frequency can be used for the measurement. If this condition is not verified, the frequency is decreased and the procedure is repeated (Fig.

Software

The programming languages used for are C and Assembler. First of all, the program identifies in a self-tuning step, the maximum frequency that allows correct measurement (see below), then it performs measurements at different frequencies and it finally calculates capacitance. The square wave is generated from the DAC which receives control words from 28, changes and holds its output value until the next command. The frequency is controlled by means of a counter in the Mega 128, that generates an interrupt twice in a period.

Data acquisition starts just after the square wave edge. The sends ADC control bits, waits until the ADC has sampled and converted the data, The sampling time window should be wide enough to allow communication of 32 bit plus the time for sampling and conversion. If these operation are performed sequentially, the maximum achievable sample rate is 25

To increase performance, the ADC allows also a parallel data transfer which increases sample rate to 40

The CBCM technique assumes that the charge (or discharge) transient are completed (total charge is measured as the area under the current waveform when the transient has stabilized). In general, the capacitance value, hence the time constant, is not known a priori. To guarantee correct operation, a tuning step is required before starting the measurement: this step determines the maximum frequency to perform correct measurements.

In a laboratory setup, this tuning step is manually performed. To automate self-tuning, we implemented an iterative procedure that compares the last part of the transient obtained with progressively decreasing frequencies: if the When the system finds the maximum frequency, the test is stopped and a variable is set. Starting from this value, the function generates a vector of frequencies which belong to this new range. The average time that is needed to perform this preliminary test is around that is compatible with biological systems.

After self-tuning, capacitance can be correctly measured. To do that, in principle it is necessary to calculate average currents for different frequencies and then calculate capacitance from equation (1). The system measures average voltage, which is proportional to current thanks to the resistor between the output cell and ground (Fig. 4). The following equation also considers the gain due to the OP-AMP and the current-to-voltage resistance Therefore, assuming ideal capacitive behavior, it is possible to calculate m as the slope of the linear interpolated curve by least squares fitting and to calculate the capacitance from the following equation:

Capacitance is calculated sampling 10 half-period voltage transients. Each transient is sampled before the end of the half-period to timely enable the interrupts, that are disabled during ADC sampling. In fact, an internal counter generates an interrupt twice in a period to warn the to change the DAC output voltage. Nevertheless, if the frequency has been defined according to the previous tuning process, the transient is expected to end at least before the end of the half-period. The main electronic components used to build the system are off-the-shelf and inexpensive. Therefore, the cost of the complete system-on-board can be estimated of about mainly determined by the p-controller, ADC and DAC components. Instead, the overall cost for a Lab-equipment set-up can be extimated to be around ten thousand (in this case virtual instrumentation is considered). The power consumption has been evaluated in idle state and during measurement: its average value is without important variations during measurement. The system has been tested on a wide range of discrete capacitances (from to (section and it has also been employed to determine the equivalent capacitance of an electrochemical two-electrode cell. The tuning procedure has been tested with and discrete capacitances to verify the correct estimation of maximum frequency (section 6.1).

Experimental Results

CAP

Lub-equipment

A Lab-equipment and the set-up are compared for a capacitance range starting from to In particular, we focused on two capacitance ranges that are characteristic of macro and micro electrodes, respectively. The macro-electrodes range was chosen from to while the micro-electrodes range was chosen from to according to available data [START_REF] Berggren | A feasibility study of a capacitive biosensor for direct detection of DNA hybridization[END_REF] which associates capacitance for to an interface. The current to voltage conversion resistor was for larger capacitances and for smaller ones, respectively. Results related to four large capacitances are shown in Tab. comparing the two measurement systems. Relative errors associated with capacitance measurements are below for both set-ups. The tuning procedure determines the maximum frequency which allows the RC circuit (load resistor-cell capacitance) to be completely charged and discharged. This is the highest fequency for which Equation 2 is verified.

Hence, the linear relationship between and V predicted by Equation 2is observed for all frequencies lower than

The value of can be clearly observed on average voltage-frequency plot. For frequencies higher than the linear relationship is lost, as shown Fig. 6. At the maximum frequency determined after self-tuning, the deviation from the linear behavior is always below thereby confirming the robustness and accuracy of the procedure described in section 5.2.

Lab-equipment and set-ups have been tested in the range of to capacitance to estimate the performance expected on micro-electrode cells

In both cases, the curve of experimental data versus nominal values has been plotted and the linear interpolation has been used as a calibration curve.

The calibration is necessary in particular for the equipment implementation because it presents a positive shift of about compared to the nominal value, instead, the p-controller system presents a shift of only 2 -(Fig. 7). The reduction of parasitics is a consequence of the more compact on-board implementation. Nevertheless, exploiting the calibration curves it is possible to eliminate the systematic error.

I caoacitance value

Table 2. measurement test

Performing measurements of known capacitance in the micro-electrode range and exploiting calibration curves, it is possible to estimate the error with respect to the nominal mented by means of a p-controller feedback procedure has been evaluated. The system has also been tested and compared in terms of accuracy with a laboratory high-cost implementation of the same charge-based capacitive measurement technique, showing better performance. Moreover, a programmable p-controller allows for high flexibility and multi-functionality of the system. In particular, the system could host and manage additional components, such as a wireless interface or other sensors value and compare the two systems. An example of measurement using capacitance is shown in Tab. 2. Results show tha the p--controller board exhibits smaller errors on capacitance measurement even in this range.

Electrochemical two-electrode cell

The set-up has been employed to measure the equivalent capacitance of an electrochemical sensing system. The system-on-board has been connected to a cell of two gold electrodes immersed in an electrolyte solution. Three different cells have been tested, bare gold electrodes, one bare gold and a chemo-modified gold one bare gold and a bio-modified gold (thiol-ended single-stranded 30-mer oligonucleotides). As the equivalent capacitance is mainly determined by the interface capacitances, bare gold capacitance can be calculated from the first cell measurement as Chemo and bio-modification of the gold surface leads to an interface capacitance decrease. The entity of the variation is related to the biochemical nature of the molecular layer formed on the surface and to its conformation. In fact, a lower capacitance value has been measured for the cells that hosted chemo-and bio-modified electrodes. In particular, chemo-modified gold exhibits a capacitance of while bio-modified exhibits a capacitance of

In agreement with literature values [ chemo-modification leads to a higher capacitance decrease.

Conclusions

In this work we have developed and tested an embedded system-on-board for DNA detection. The main advantages of this DNA sensor implementation compared to commercially available systems are to avoid the use of high-cost detectors and the pre-treatment of the sample to introduce marker molecules. Moreover, the system is stand-alone and low-cost. Self-tuning functionality, which has been

Figure 1 .

 1 Figure 1. Microarray detection technique.

Figure 3 .Figure 2

 32 Figure 3. Principle of CBCM principle

Figure

  Figure 5. Tuning principle

Figure 6 .

 6 Figure 6. Deviation from linear behavior

Figure 7 .

 7 Figure 7. Calibration curves

Table 1 . Comparison between the two systems from to capacitance range

 1

	0.35	I	2 5 7	I 0.356	1 8
			Measurement	Error (%)
			147.8	1.46
			149.34	0.43

Proceedings of the Design, Automation and Test in Europe Conference and Exhibition (DATE'05) 1530-1591/05 $ 20.00 IEEE


Florent Lalys
email: florent.lalys@therenva.com.

Ketty Favre

Alexandre Villena

Vincent Durrmann

Mathieu Colleaux

Antoine Lucas

Adrien Kaladji

A hybrid image fusion system for endovascular interventions of peripheral artery disease

Keywords: Image fusion, endovascular procedures, peripheral artery disease, lower extremities

Purpose. Interventional endovascular treatment has become the first line of management in the treatment of peripheral artery disease (PAD). However, contrast and radiation exposure continue to limit the feasibility of these procedures. This paper presents a novel hybrid image fusion system for endovascular intervention of PAD. We present two different roadmapping methods from intra-and pre-interventional imaging that can be used either simultaneously or independently, constituting the navigation system.

Methods. The navigation system is decomposed into several steps that can be entirely integrated within the procedure workflow without modifying it to benefit from the roadmapping. First, a 2D panorama of the entire peripheral artery system is automatically created based on a sequence of stepping fluoroscopic images acquired during the intra-interventional diagnosis phase. During the interventional phase, the live image can be synchronized on the panorama to form the basis of the image fusion system. Two types of augmented information are then integrated. First, an angiography panorama is proposed to avoid contrast media re-injection. Information exploiting the pre-interventional computed tomography angiography (CTA) is also brought to the surgeon by means of semi-automatic 3D/2D registration on the 2D panorama. Each step of the workflow was independently validated.

Results. Experiments for both the 2D panorama creation and the synchronization processes showed very accurate results (errors of 1.24 mm and 2.6 +/-1.4 mm, respectively), similarly to the registration on the 3D CTA (errors of 1.5 +/-0.7 mm), with minimal user interaction and very low computation time. First results of an ongoing clinical study highlighted its major clinical added-value on intra-operative parameters.

Conclusions. No image fusion system has been proposed yet for endovascular procedures of PAD in lower extremities. More globally, such a navigation system, combining image fusion from different 2D and 3D image sources, is novel in the field of endovascular procedures.

Introduction

Peripheral artery disease (PAD) results from the build-up of plaque in the arteries of the legs. This plaque can partially obstruct the artery and is called a stenosis or a thrombosis when the artery is fully clogged. The diagnosis is usually made using duplex ultrasound, CT angiography (CTA) or MR angiography. Interventional endovascular treatment, whenever feasible, has become the first line of management in the treatment of PAD, progressively replacing surgery [START_REF]Swedish Council on Health Technology Assessment: Peripheral Arterial Disease -Diagnosis and Treatment: A Systematic Review[END_REF]. Endovascular treatment offers a lower risk alternative to open surgery in many patients with multiple comorbidities, including ischemia. The endovascular methods for revascularization mainly include percutaneous transluminal balloon angioplasty (PTA) and stenting.

Use of duplex ultrasound is the most common approach for assessing the hemodynamic behavior of lesions, though in some cases CTA is performed. Indeed, interventions are in most cases arteriography in intent to treat, i.e. an arteriography of the femoro-popliteal artery is done in order to identify lesions before treatment. This phase, performed in the operating room, can be seen as the diagnosis phase. Conventional X-ray equipment can only provide a limited field-of-view, and all lesions cannot be visualized entirely on a single angiography. In clinical routine, information about lesions are obtained at the beginning of the intervention by stepping the Carm and/or the table sequentially to different locations. In a second phase, each lesion is independently treated. However, moving the C-arm to treat stepped lesions or long occlusions results in a loss of the arterial mask and therefore requires multiple angiographies that have the effect of increasing the volume of iodinated contrast agent injected, increasing the irradiation of the patient and the surgical staff. Reducing the amount of contrast media is of importance because of its potentially nephrotoxic effects in patient with renal failure [START_REF] Beckett | Safe Use of Contrast Media: What the Radiologist Needs to Know[END_REF]. In addition, this projective imaging modality inevitably leads to topographic errors and gives no information on arterial wall quality. Therefore, additional information from pre-interventional and intra-interventional images is needed to gain precision during endovascular tools use and to help visualizing target vessels.

In the context of endovascular procedures, image fusion techniques mainly focused on the use of rotational Carms in hybrid OR, the aim becoming to enrich the intraoperative images by projecting the preoperative image [START_REF] Goudeketting | Pros and Cons of 3D Image Fusion in Endovascular Aortic Repair: A Systematic Review and Meta-analysis[END_REF]. Using a mobile C-arm, very few works have been reported in the context of endovascular procedures [START_REF] Penney | An Image-Guided Surgery System to Aid Endovascular Treatment of Complex Aortic Aneurysms: Description and Initial Clinical Experience[END_REF][START_REF] Duménil | A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm[END_REF], all focusing on abdominal aortic aneurysms. In the specific context of PAD for lower extremities, preliminary work has been reported by Sailer et al. [START_REF] Sailer | Fusion Guidance in Endovascular Peripheral Artery Interventions: A Feasibility Study[END_REF] with fusion of pre-interventional MRA and CTA datasets with live fluoroscopy. However, the system is designed to work only in hybrid OR. While some studies focused on carotid arteries in swine models [START_REF] Klein | Multimodality image fusion to guide peripheral artery chronic total arterial occlusion recanalization in a swine carotid artery occlusion model: unblinding the interventionalist[END_REF][START_REF] Raval | Real-time magnetic resonance imaging-guided endovascular recanalization of chronic total arterial occlusion in a swine model[END_REF], no fusion system has been proposed yet for PAD procedures of lower extremities in conventional OR. The main idea of our hybrid navigation system is to fully exploit 2D and 3D images acquired during the perioperative clinical workflow. The term hybrid is employed here to refer to the use of both sources of data either simultaneously or independently.

Methods

The proposed navigation system is decomposed into several steps that are entirely integrated within the clinical workflow and that will be independently presented and validated. The overall workflow is depicted in Fig. 1. First, the sequence of fluoroscopic images captured during the diagnosis phase is combined to create a fluoroscopic panorama, used as a reference image along the intervention (subsection 2.1). Then, during the interventional phase, the live fluoroscopic image can be superimposed on the panorama using an automatic registration approach (subsection 2.2). These two steps form the basis of our image fusion navigation system. An angiography panorama, constructed during the diagnosis phase, is used together with the fluoroscopic panorama to extract information about the treated lesion (subsection 2.3). Information exploiting the pre-interventional CTA, such as the arterial wall quality and plaques, is also brought to the surgeon by means of semi-automatic 3D/2D registration (subsection 2.4). Finally, first results of an on-going clinical study are shown in subsection 2.5 to highlight the major clinical added-value.

Creation of the fluoroscopic panorama

During the diagnosis phase, a panorama can be formed from successive digital images. The creation of the panorama is presented in the following subsections.

State-of-the-art

Image stitching has gained much interest for X-ray images due to the limited field-of-view, the noise present in images and the fact that relevant structures can be missing in overlapping areas. To propose an alternative solution to hybrid ORs with robotized/fully automatic solutions, which are not accessible in every clinical center, automatic or semi-automatic image-based methods compatible with mobile C-arms in conventional ORs are required [START_REF] Duménil | A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm[END_REF][START_REF] Sailer | Fusion Guidance in Endovascular Peripheral Artery Interventions: A Feasibility Study[END_REF][START_REF] Klein | Multimodality image fusion to guide peripheral artery chronic total arterial occlusion recanalization in a swine carotid artery occlusion model: unblinding the interventionalist[END_REF][START_REF] Raval | Real-time magnetic resonance imaging-guided endovascular recanalization of chronic total arterial occlusion in a swine model[END_REF][START_REF] Gooßen | Automatic Stitching of Digital Radiographies using Image Interpretation[END_REF][START_REF] Samsudin | Development of automated image stitching system for radiographic images[END_REF][START_REF] Capek | A Fully Automatic Stitching of 2D Medical Data Sets[END_REF][START_REF] Kumar | Automatic image alignment and stitching of medical images with seam blending[END_REF][START_REF] Yang | Improvement of automated image stitching system for DR X-ray images[END_REF][START_REF] Vela | Digitalization of X-ray films with image stitching[END_REF][START_REF] Supakul | Diagnostic errors from digital stitching of scoliosis images -the importance of evaluating the source images prior to making a final diagnosis[END_REF][START_REF] Yaniv | Long bone panoramas from fluoroscopic X-ray images[END_REF].

In the category of feature-based methods, Paudel et al. [START_REF] Paudel | Stitching of X-ray Images[END_REF] used SIFT, SURF and Harris keypoints to stitch chest X-ray images, taking advantage of the amount of strong features present on chest images. Other methods employed additional radio-opaque materials. Yaniv and Joskowicz [START_REF] Yaniv | Long bone panoramas from fluoroscopic X-ray images[END_REF] created long bone panorama from fluoroscopic X-ray images using a ruler alongside the long bone, similarly to Chen et al. [START_REF] Chen | Ruler Based Automatic C-Arm Image Stitching Without Overlapping Constraint[END_REF]. Another similar method employed an absolute reference panel with coordinates places under the bones [START_REF] Messmer | Image Fusion for Intraoperative Control of Axis in Long Bone Fracture Treatment[END_REF]. Goossen et al. [START_REF] Gooßen | Automatic Stitching of Digital Radiographies using Image Interpretation[END_REF] presented an automatic algorithm based on a ruler to perform the first feature-based registration, which is followed by a content-based registration to match anatomy. Wang et al. [START_REF] Wang | Parallax-Free Long Bone X-ray Image Stitching[END_REF][START_REF] Wang | Parallax-free intra-operative X-ray image stitching[END_REF] proposed a parallax-free panoramic X-ray image that preserves the linear perspective projection property. They made use of a visual marker pattern along with a camera augmented mobile C-arm. If feature-based methods present some advantages, it requires supplementary materials for extracting relevant visual features from the scene.

To overcome the drawback of feature-based methods, Kumar et al. [START_REF] Kumar | Automatic image alignment and stitching of medical images with seam blending[END_REF] proposed a method for stitching hand Xray images using histogram matching and mutual information. Samsudin et al. [START_REF] Samsudin | Development of automated image stitching system for radiographic images[END_REF] utilizes minimum average correlation energy filters to merge hand X-ray image pairs. With chest X-ray images, Vela et al. [START_REF] Vela | Digitalization of X-ray films with image stitching[END_REF] used image stitching with classical sum-of-square difference, while Yang et al. [START_REF] Yang | Improvement of automated image stitching system for DR X-ray images[END_REF] proposed an automated image stitching method using an improved phase correlation algorithm. Finally, using lower limb X-ray images, Capek et al. [START_REF] Capek | A Fully Automatic Stitching of 2D Medical Data Sets[END_REF] utilized a point matching method together with the normalized correlation coefficient (NCC) to evaluate a similarity measure (SM) of the X-ray image. The survey from the prior art shows that panorama creation from X-ray images has focused on orthopedics applications. Moreover, there is still no study focusing on the best SM to be used, and proposing a complete validation scheme.

Image stitching

The objective was to register 2D fluoroscopic images with limited overlapping portions to find the best match in each adjacent pair. The minimal overlap ratio needed between image pair was set to 20%. The problem was expressed as a succession of 2D rigid registration problems, with two translation parameters and potential slight inplane rotations induced by the position of the C-arm. The main challenges are the limited overlapping areas, and the lack of strong features in lower limb images. To reduce calculation time, a multi-resolution scheme with progressive refinement was proposed with multiple optimization phases from coarse to fine levels. During each phase, a reduced exhaustive search is performed by decomposing the transformations. An optimization by exhaustive search consists in sampling the parameter space of the transformation, and calculating a SM for each combination of parameters to finally keep the combination of parameters with the best SM. The obtained parameters are used as the initial solution for the next resolution level. The center of rotation was defined as the center of the image. The registration method finally relies on three optimization phases (Table .1).

SM can be divided into two main categories, the global similarity and the local similarity measures. Generally, it has been shown that the local SMs present good accuracy but a narrow capture range, and conversely for the global SMs [START_REF] Penney | A comparison of similarity measures for use in 2D-3D medical image registration[END_REF][START_REF] Kim | Comparison of similarity measures for rigid-body CT/Dual X-ray image registrations[END_REF]. Based on the literature review on image stitching using X-ray images, we applied the protocol to four global SMs (Sum of Square Difference (SSD), mutual information (MI), entropy correlation coefficient (ECC), normalized cross-correlation (NCC)) and two local SMs (the gradient difference (GD) and the gradient information (GI)). GI has been previously used for 3D/2D registration between CTA and X-ray image with great success [START_REF] Duménil | A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm[END_REF]. As global SM generally don't contain spatial information, combination of global and local SM were also tested [START_REF] Pluim | Image registration by maximization of combined mutual information and gradient information[END_REF].

As the performance of a registration method is mainly influenced by SMs [START_REF] Markelj | A review of 3D/2D registration methods for image-guided interventions[END_REF], we followed the evaluation protocol proposed by Skerl et al. [START_REF] Skerl | A protocol for evaluation of similarity measures for rigid registration[END_REF] to assess their robustness. The protocol evaluates the criteria along N randomly oriented line segments (profiles) passing through the gold standard (GS) point in the K-dimensional parameter space. It generates M sampling points equally distributed on each line. Sampling points lie inside of a hypersphere of radius R in the parametric space. Several measures of the criterion performance can be calculated, among which the accuracy (ACC), the distinctiveness of the optimum (DO), the number of minima (NOM), the capture range (CR) and the risk of non-convergence (RON). The measures of the properties stabilize when using ~50 profiles

Seamless blending

The seam between the stitched images should be invisible for visualization purposes first, but especially to have a smooth image to be used for the synchronization step. To remove the seam on the panoramic view and avoid unwanted image gradients, we used a triangulation averaging algorithm [START_REF] Kumar | Automatic image alignment and stitching of medical images with seam blending[END_REF]. Triangulation averaging is applied on the overlapped area of the images, with linearly decreasing values in the overlapping range, as shown in Fig. 2. The seamless blending is only applied after a first verification step, where the user can adjust the result of the registration if necessary without any blending process.

Registration of the live fluoroscopy to the panorama

After the diagnosis phase, the surgeon successively acquires live fluoroscopic images to treat lesions from the most distal to the most proximal during the interventional phase. These successive images, sometimes with different zoom levels, can be directly synchronized on the panorama similar to a template matching process. Compared to the rigid registration problem from subsection 2.1.2, the scale parameter is added to take into account potential C-arm zoom actions or table height changes. The minimal overlap ratio between the template and the panorama was set to 80%, assuming that all lesions treated during the interventional phase have been previously detected and located during the initial diagnosis phase.

Roadmapping using angiographic panorama

During the diagnosis phase, stepping angiographies are acquired along with fluoroscopic images. Relying on the spatial correspondence between angiography and fluoroscopy, and using the registration transformations computed for the fluoroscopic panorama, an angiographic panorama representing the arterial tree is created with similar blending process. With the live image being automatically synchronized on the fluoroscopic panorama, overlay of vascular structures on the live images is made available to the surgeon. Overlay of vascular structures on fluoroscopic image is of great interest for assisting the operator for catheterization or prosthesis deployment procedures. However, one major issue remains the parallax effect, which occurs when objects are not localized on the same plane. It is the case here, because the registration is performed using bones information, while useful information to be displayed is the arterial tree (e.g. femoral or popliteal arteries) from the angiographic panorama (Fig. 3.). b) the ruler is positioned at the same level.

Roadmapping using 3D pre-interventional CTA

Similarly to 2D/2D registration methods used in 2.1 and 2.2, 3D/2D registration methods are divided into feature-based and intensity-based approaches. Intensity-based approaches do not require segmentation of particular structures, representing an advantage given the diversity of intra-interventional scenes. From the extended set of publications on 3D/2D registration methods, interest has been given to optimization methods [START_REF] Haque | A fast and robust technique for 3D-2D registration of CT to single plane X-ray fluoroscopy[END_REF], to accelerate the generation of DRRs using CPU [START_REF] Rohlfing | Progressive attenuation fields: fast 2D-3D image registration without precomputation[END_REF] or GPU [START_REF] Kubias | 2D/3D image registration on the GPU[END_REF] computing strategies or to automate the initialization [START_REF] Varnavas | Fully automated initialisation of 2D #x2013; 3D image registration[END_REF].

While extensive strategies have been proposed for many different fields, including radiotherapy [START_REF] Sarrut | Geometrical transformation approximation for 2D/3D intensity-based registration of portal images and CT scan[END_REF] or radiosurgery [START_REF] Fu | A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery[END_REF], only Penney et al. [START_REF] Penney | An Image-Guided Surgery System to Aid Endovascular Treatment of Complex Aortic Aneurysms: Description and Initial Clinical Experience[END_REF], Duménil et al. [START_REF] Duménil | A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm[END_REF] and Mitrovic et al. [START_REF] Mitrović | Simultaneous 3D-2D image registration and C-arm calibration: Application to endovascular image-guided interventions[END_REF] proposed registration strategies for endovascular procedures.

Our 3D fusion system relies on previous work on an versatile rigid 3D/2D registration [START_REF] Duménil | A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm[END_REF] and will not be detailed. The 3D CTA can be registered either directly to the final panorama or to a single fluoroscopic acquisition. The registration must be initialized with C-arm angulation and with a rough alignment of the bony structures. The iconic 3D/2D registration is then employed with a computation time that never exceeds 20s.

Clinical study -Proof of concept

Beyond the scientific challenges, the clinical benefits also need to be assessed. As part of an on-going clinical study, the registration capabilities were integrated into a prototype navigation system, which was routinely used during endovascular procedures of PAD for 22 patients using a Siemens Cios Alpha flat-panel detector. No 3D pre-interventional information was used for these procedures, only 2D roadmapping was tested. Intrainterventional information about the procedure were recorded following the clinical protocol, including total volume of contrast media injected to the patient, fluoroscopy time, dose area product (DAP), cumulative air kerma (CAK) and time of procedure. These measures were compared with a control group of 40 patients also recently operated for a PAD with the exact same flat panel detector and same operators. Non-parametric statistical analysis was preferred for comparing means of both groups.

3

Experiments and results

Creation of the fluoroscopic panorama

The evaluation of the SMs was performed on a dataset of 30 patients, all treated for a PAD at the Rennes University Hospital, France. Fifteen were operated in a conventional OR using a flat-panel detector (Cios Alpha, Siemens Healthcare), 10 with an X-ray image intensifier (OEC 9800, GE healthcare), and 5 in a hybrid OR (Artis Zeego, Siemens Healthcare). From this dataset, a total of 65 pairs of X-ray fluoroscopic images were acquired. Images coming from the X-ray image intensifier were undistorted based on the OpenCV calibration procedure using a beam stop phantom (QRM GmbH, Germany). As performed during clinical routine, all pairs of images were acquired using same table and C-arm configurations, including same C-arm orientations (0 CRA-CAU, 0 LAO-RAO for 62 out of 65 pairs), same C-arm field-of-view (large FOV) and same table height. These parameters are requirements that allow us to optimize only the two in-plane translations and the in-plan rotation.

For each image pair, the GS was determined by a single expert and used as a reference for the evaluation protocol. In addition, the intra-expert variability was estimated with three GS processes on the entire set of 65 image pairs. ACC, RON, NOM, CR and DO properties were evaluated on 10 SMs (SSD, ECC, MI, NCC, GD, GI, GDxMI, GDxNCC, GIxMI, GIxNCC) for the registration of the 65 fluoroscopic image pairs. Using MI x GI, only 5 registrations out of 65 image pairs showed registration errors higher than 5mm. For three of these failures the overlap ratio was very close to 20%, and no strong feature was present. The last two failures were explained by the presence of multiple tools and very poor contrast. The five failures were observed with images coming from the X-ray image intensifier (GE OEC device). The average computation time for one image pair was 0.8 s on a standard machine. Finally, the intra-expert variability showed a precision of 0.87 +/-0.91 mm.

Synchronization of the current live image

In order to find the best synchronization algorithm, we tested the same similarity measures from the prior evaluation protocol. Only the average accuracy was considered for evaluating the different methods. The validation dataset was composed of 15 patients (5 operated with the Cios Alpha, 5 with the Artis Zeego and 5 with the GE OEC) for whom a complete fluoroscopic panorama composed of at least 3 images was automatically created and manually adjusted, if needed. From the 15 patients, a total of 80 live images acquired during the interventional phase (i.e. not employed for the panorama creation) were used for validating the synchronization process. From the 80 images, 32 were located at the level of the hip (with both landmarks from the hip and the femur), 28 at the level of femur (without any hip of knee landmarks) and 20 at the level of the knee (with femur and knee landmarks). Similarly to the panorama creation, the GS was determined by an expert and used as a reference for computing synchronization accuracies.

GI and GI x MI showed very good performances (accuracies of 2.8 +/-1.9 mm and 2.6 +/-1.4 mm, respectively), whereas other tested SMs showed limited performances with accuracies above 4mm. Overall, using the GI x MI SM, only 6 images failed to be located (showing an error > 5mm), 4 being images from the image intensifier. In addition, 6 other images needed to be slightlty manually corrected by less than 5mm. Average processing time was 1.6 s on a standard machine.

Roadmapping using angiographic panorama

To estimate the parallax error, a dataset of 12 patients was used. Fluoroscopic panoramas were first created and manually validated, and ostia of collaterals were annotated by an expert on angiographic images. If the same landmark was annotated in different views, an Euclidean distance between both landmark positions can be calculated on the angiographic panorama. In order to estimate errors with respect to the anatomical position of arteries and the bony structure, 3 anatomical zones were defined along the femoro-popliteal axis: the iliac, the femoral and the popliteal zones (Fig. 5.). Estimated parallax errors showed large heterogeneity among anatomical zones. Errors were calculated as 1.2 mm +/-0.4 mm, 1.9 .mm +/-0.7 mm, and 4.2 mm +/-1.1 mm for the iliac, the femoral and the popliteal stages respectively. These values are only suitable for this configuration without any C-arm angles, and with an overlap ratio of ~40%. Thanks to the prior synchronization, the surgeon can visualize vascular structures without additional contrast media, either in overlay or directly with the corresponding ROI (Fig. 6).

Roadmapping with 3D information

To evaluate the algorithm for this specific application, a dataset of 12 patients was collected (all operated with the C-arm Cios Alpha). For each patient, a pre-interventional CT-scan with contrast injection was performed before the operation with a 64 slice CT-scan (LightSpeed16, GE Healthcare). Intra-operatively, a fluoroscopic panorama comprising at least 3 images was also automatically created (and manually adjusted, if needed). 100 registrations with random initial transformations were performed for each patient. The ranges of initial transformations for each degree of freedom were estimated based on the manual registration initializations performed by 3 surgeons (one experienced and two residents) on 10 patients using our system. Registration error was computed as the average distance between the projection of a set of 10 anatomical keypoints situated along the bony structures with the transformation obtained by registration and that performed with the reference transformation obtained by an expert.

Registration using the full leg panorama leads to more precise registration than when using a single fluoroscopic image only (1.5 +/-0.7 mm Vs 2.4 +/-1.2 mm, respectively). Registration showed high accuracy (average registration error of 1.5 +/-0.7mm) and did not appear to be disturbed by the presence of endovascular tools such as catheter, balloon, or rigid guidewire. Once registered, the corresponding ROI of the C-arm field-of-view can be made available to the surgeon (Fig. 6.), and qualitative information of the arterial wall quality, such as calcifications or precise stenosis/thrombosis information are available. Those information are not available using the 2D angiographic panorama solely and are real added-value for the surgeon. The visualization of the lesion in 3D might also reduce the risk of under or over-estimation of the stenosis.

Clinical added-value -Proof of concept

During all interventions, we observed that our approach was compatible with clinical workflow. First results of the clinical study showed a significant reduction for important intra-interventional parameters, including volume of contrast media injected to the patient and major irradiation parameters (DAP and CAK). Total fluoroscopy time and procedure time didn't show any significant reduction (Tab. 2.).

Tab. 2.

Comparison of intra-interventional parameters between the groups with/without 2D roadmapping.

Discussion

A complete image-guided system for endovascular procedures of PAD is presented in this paper, incorporating 3D and 2D information from pre-interventional and intra-interventional imaging, respectively.

Thanks to a multi-resolution scheme, the panorama calculation time was drastically reduced to less than 1s per image pairs, making it compatible with clinical workflow. With an average registration error of 1.24 mm, close to the intra-expert variability value (0.87 mm), and overall excellent performances for all other indicators, MI x GI was found to be the best SM. Our results are consistent with recent studies on X-ray images registrations [START_REF] Penney | An Image-Guided Surgery System to Aid Endovascular Treatment of Complex Aortic Aneurysms: Description and Initial Clinical Experience[END_REF] demonstrating the superiority of the GI metric over all other conventional measures, but we also highlighted the necessity of using a combination of global and local similarity measures [START_REF] Pluim | Image registration by maximization of combined mutual information and gradient information[END_REF] to increase performance. It is less sensitive to low sampling resolution, do not contain incorrect global maxima that are sometimes found in the mutual information function, and interpolation-induced local minima can be reduced. These characteristics yield a robust registration method which can be used in conjunction with different types of C-arm exhibiting variations in image quality, including both flat-panel and image intensifier based systems. The error rate of 20% for image intensifier systems can be explained by the loss of image quality compared to flat panel detector, and also by the distortions inherent to image intensifier, whilst a distortion correction is systematically applied to those images. Indeed, the image distortions are varying according to a number of parameters (C-arm configuration, table height, temperature of the OR, etc..) and distortions can still be present. Our registration method has been developed to work fine for overlapping area larger than 20% of total size of image.

For the synchronization phase, experiments were also performed using feature-based methods (e.g. using SIFT and SURF features) but turned out to be inadequate. It is most likely due to the lack of distinct and strong features on bone structure (e.g. hip or knee). In contrast, a standard intensity-based registration approach gave robust results, even when we used 4 degrees of freedom. From the validation dataset of 80 images, 8 were acquired with a different C-arm field-of-view, all showing robust synchronization on the panorama. This important result demonstrated that our approach can be applied to different zoom level and both steps demonstrated their precision. Then, the parallax error needs to be taken into account. This error can reach up to 5mm in the popliteal artery area, where the distance between the artery and the bone is maximal. If the user wants to lower the parallax error for gaining precision, he has the possibility to acquire more images to create the panorama [START_REF] Verdonck | Image quality and X-ray dose for translation reconstruction overview imaging of the spine, colon and legs[END_REF]. Put end to end, these different errors can reach up to 10mm in worse configurations without manual adjustment. Classical complications due to bad catheter positioning include deep vein thrombosis and occlusive malfunction. However, the acceptable precision for endovascular procedures of PAD is much lower than for AAA, and errors below 10mm can be considered as clinically acceptable.

Having in mind these potential sources of error, our image-guided system can have a strong clinical added-value, in particular regarding contrast and radiation exposure. In the era of minimally invasive techniques, the rise of endovascular therapies and the increasing complexity of the gestures achieved, these objectives constitute real clinical challenges. The aim is also to provide the practitioner with a tool that makes it possible to secure the surgical procedure and increase its efficiency. First results of our clinical study showed very impressive results in term of reduction of X-ray exposure time and quantity of contrast agent injected, making it very promising for the future of PAD procedures. These results have to be put into perspective because this is not a randomized blinded study, whilst the same C-arm and the same operators were involved in both groups. Other expected performances of the system, which cannot be quantifiable, include but are not limited to the better navigation thanks to available augmented information (e.g. better target delineation) or the better guidance of endovascular tools (e.g. better device localization). Once the surgeons will be used to the system, we would also expect a reduction in total procedure time.

Conclusion

This paper presents a novel and hybrid image-guided system for endovascular intervention of PAD, which fully exploits 3D and 2D information from pre-interventional and intra-interventional imaging, respectively. Different experiments conducted on heterogeneous datasets demonstrated the precision and robustness of the system. In addition to its accuracy, it is fully integrated within the clinical workflow and doesn't require many interactions. This first image fusion system for endovascular procedures of PAD is very promising, with reduction of important intra-interventional parameters impacting the patient and the surgical staff. Additionally, combining image fusion from different 2D and 3D image sources is novel in the field of endovascular procedures.

Fig. 1 .

 1 Fig. 1. Overall intra-interventional workflow of the image fusion system

Tab. 1 .

 1 Parameters used during the optimization phases of the panorama creation (N = total number of pixels contained in the region of interest in the intra-interventional image, r = resolution of the discretized range of the parameters).

Fig. 2 .

 2 Fig. 2. Fluoroscopic panorama without (left) and with (right) seamless blending.

Fig. 3 .

 3 Fig. 3. Example of parallax effect using a bone phantom and a ruler. a) the ruler is positioned below the phantom.

Fig. 4 .

 4 Fig. 4. Example of an optimization curve obtained using GI.

Fig. 5 .

 5 Fig. 5. Definition of the three anatomical zones for parallax error estimation.

Fig. 5 .

 5 Fig. 5. Visualization of the navigation prototype UI. Left: overlay of the angiographic panorama on the live image. Upper right: enhanced angiographic panorama, with the corresponding ROI. Bottom right: information from the pre-interventional CTA, where the ROI of the 3D volume corresponds to the ROI of the live acquisition.

  table height modification during the operation, whilst further experiments are needed to validate this robustness. Again, processing time makes it compatible with clinical workflow.

	Intra-interventionalparameters	Without 2D roadmapping	With 2D roadmapping	p-value
		(n = 40)	(n = 22)
	Volume of contrast media (𝑚𝑚𝑚𝑚)	75.8 +/-37.9	43.7 +/-20.7	0.023
	Fluoroscopy time (𝑚𝑚𝑚𝑚𝑚𝑚)	19.7 +/-14.4	14.9 +/-5.9	0.696
	DAP (𝑢𝑢𝑢𝑢𝑢𝑢. 𝑚𝑚 2 )	3708 +/-3294	1568 +/-734	0.012
	CAK (𝑚𝑚𝑢𝑢𝑢𝑢)	162.8 +/-145.7	48.4 +/-19.8	0.008
	Procedure time (𝑚𝑚𝑚𝑚𝑚𝑚)	79.3 +/-35.4	71.9 +/-22.3	0.678

Augmented information can then be made available thanks to the angiographic panorama and the preinterventional 3D CTA. It nonetheless presents intrinsic limitations, mainly induced by the geometric errors arising from different sources. The first errors come from the panorama creation and synchronization, where the registration between image pairs and synchronization processes are not always perfect, even if experiments for

Conflict of Interest:

The authors declare that they have no conflict of interest.

Ethical approval:

For this type of study formal consent is not required.

Informed consent: Informed consent was obtained from all individual participants included in the study.