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Background: The aim of this study was to evaluate a new measurement tool for the preoperative computed tomography (CT) scan to estimate the endograft apposition surface on the proximal aortic neck of the aneurysm that would predict the risk of late type IA endoleak (EL1A)., Methods: From databases of 4 high-volume centers between 2004 and 2020, all patients who underwent Endovascular Aneuruysm Repair (EVAR) with the possibility of analyzing pre and post-EVAR scans and who presented late (>2 years) EL1A in follow-up were included retrospectively (EL1A group). A control group of randomly selected EL1A-free patients were included (control group) and followed-up beyond 4 years. The measurement tool (Endosize, Therenva) was used to model a virtual cylinder constrained by the centreline whose diameter was determined from the diameter of the implanted endograft. The apposition surface was calculated by determining the areas of the virtual cylinder mesh outside the mesh of the neighboring portion of the segmented aorta by an intersection calculation of surface normals. This apposition surface and the usual anatomical data of the proximal neck were compared between the groups with univariate and multivariate analyses. Intra and interobserver variability in the measurement of apposition surface was analyzed using the intraclass correlation coefficient (ICC) and the method of Bland and Altman., Results: Sixty-six comparable patients were included as follows: 33 in the EL1A group and 33 in the control group. The mean time to EL1A onset was 50.2 ± 24.6 months in the EL1A group, and the mean follow-up time was 79.8 ± 26.8 months in the control group. In univariate analysis, only the endograft apposition surface ratio on the proximal neck was significantly lower in the EL1A group compared with the control group (50.3% vs. 75.5%; P < 0.0001). There was no significant difference in classic anatomical neck characteristics. There was no difference in proximal endograft oversizing or in the length from the lowest renal artery to the beginning of the covered portion of the endograft. In multivariate analysis, only the apposition surface ratio was independently correlated with the presence of EL1A (P < 0.0001). The estimation of the intra and interobserver variability indicates excellent reliability (ICC = 0.992; ICC = 0.956)., Conclusions: The method estimating the preoperative endograft apposition surface on the proximal aortic neck appears to be a reliable measuring technique in predicting the risk of occurrence of late EL1A after EVAR., (Copyright © 2024. Published by Elsevier Inc.)

Purpose: Endovascular revascularization is becoming the established first-line treatment of peripheral artery disease (PAD). Ultrasound (US) imaging is used pre-operatively to make the first diagnosis and is often followed by a CT angiography (CTA). US provides a non-invasive and non-ionizing method for the visualization of arteries and lesion(s). This paper proposes to generate a 3D stretched reconstruction of the femoral artery from a sequence of 2D US B-mode frames., Methods: The proposed method is solely image-based. A Mask-RCNN is used to segment the femoral artery on the 2D US frames. In-plane registration is achieved by aligning the artery segmentation masks. Subsequently, a convolutional neural network (CNN) predicts the out-of-plane translation. After processing all input frames and re-sampling the volume according to the vessel's centerline, the whole femoral artery can be visualized on a single slice of the resulting stretched view., Results: 111 tracked US sequences of the left or right femoral arteries have been acquired on 18 healthy volunteers. fivefold cross-validation was used to validate our method and achieve an absolute mean error of 0.28 ± 0.28 mm and a median drift error of 8.98%., Conclusion: This study demonstrates the feasibility of freehand US stretched reconstruction following a deep learning strategy for imaging the femoral artery. Stretched views are generated and can give rich diagnosis information in the pre-operative planning of PAD procedures. This visualization could replace traditional 3D imaging in the pre-operative planning process, and during the pre-operative diagnosis phase, to identify, locate, and size stenosis/thrombosis lesions., (© 2022. CARS.)

Introduction: Intra-arterial therapy is an effective way of performing chemotherapy or radiation therapy in patients with primary liver cancer (i.e. hepatocellular carcinoma). Although this minimally invasive approach is now an established treatment option, support tools for pre-operative planning and intra-operative assistance might be helpful., Material and Methods: We developed an approach for semi-automatic segmentation of computed tomography angiography images of the main arterial branches (required for access path to the treatment site), automatic segmentation of the liver, arterial and venous tree, and interactive segmentation of the tumors (required for procedure-specific planning). This approach was then integrated into a liver-specific workflow within EndoSize ® solution, a planning software for endovascular procedures. The main branches extraction approach was qualitatively evaluated inside the software, while the automatic segmentation methods were quantitatively assessed., Results: Main branches extraction provides a success rate of 85% (i.e. all arteries correctly extracted) in a dataset of 172 patients. On public databases, a mean DICE of 0.91, 0.47 and 0.92 was obtained for liver, venous and arterial trees segmentation, respectively., Conclusions: This pipeline is suitable for directly accessing the treatment site, giving anatomic measurements, and visualizing the hepatic trees, liver, and surrounding arteries during the pre-operative planning., Abbreviations: HCC: hepatocellular carcinoma; TACE: transarterial chemoembolization; SIRT: selective internal radiation therapy; CT: computed tomography; CTA: computed tomography angiography; AMS: superior mesenteric artery; LGA: left gastric artery; RHA: right hepatic artery; LHA: left hepatic artery; rbHA: right branch of the hepatic artery; lbHA: left branch of the hepatic artery; GDA: gastroduodenal artery; VOI: volume of interest; SD: standard deviation; MICCAI: medical image computing and computer assisted interventions; MR: magnetic resonance.

The aim of this study is to develop an automated deep-learning-based whole heart segmentation of ECG-gated computed tomography data. After 21 exclusions, CT acquired before transcatheter aortic valve implantation in 71 patients were reviewed and randomly split in a training (n = 55 patients), validation (n = 8 patients), and a test set (n = 8 patients). A fully automatic deep-learning method combining two convolutional neural networks performed segmentation of 10 cardiovascular structures, which was compared with the manually segmented reference by the Dice index. Correlations and agreement between myocardial volumes and mass were assessed. The algorithm demonstrated high accuracy (Dice score = 0.920; interquartile range: 0.906-0.925) and a low computing time (13.4 s, range 11.9-14.9). Correlations and agreement of volumes and mass were satisfactory for most structures. Six of ten structures were well segmented. Deep-learning-based method allowed automated WHS from ECG-gated CT data with a high accuracy. Challenges remain to improve right-sided structures segmentation and achieve daily clinical application., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Endovascular treatment has become the first-line strategy for peripheral arterial disease (PAD). Given the number of procedures required, any technology associated with a reduction in radiation exposure and contrast volume is highly relevant. In the present study, we evaluated whether two-dimensional (2D) fusion imaging could reduce the radiation exposure and contrast volume during endovascular treatment of occlusive PAD., Methods: Our consecutive, retrospective, single-center, nonrandomized comparative trial included patients with PAD at the femoral, popliteal, and/or tibial level, at any clinical stage, if they were candidates for endovascular revascularization. Patients were treated with or without the EndoNaut 2D fusion imaging system (Therenva, Rennes, France) in a nonhybrid room with the same Cios Alpha mobile C-arm (Siemens, Munich, Germany). The indirect dose-area product and contrast medium volume were recorded., Results: Between March 2018 and April 2020, 255 patients underwent endovascular femoropopliteal revascularization with (n = 124) or without (n = 131) 2D fusion imaging. The volume of injected contrast medium (34.7 ± 13.8 mL vs 51.3 ± 26.7 mL; P < .001) and dose-area product (8.9 ± 9.9 Gy/cm 2 vs 13.5 ± 14.0 Gy/cm 2 ; P = .003) were significantly lower for the 2D fusion imaging group than for the control group. A subgroup analysis of complex (TransAtlantic Inter-Society Consensus for the Management of Peripheral Arterial Disease C/D) lesions showed similar results. Stratification of the fusion imaging group into three subgroups, according to the procedure dates, showed no effect of a potential learning curve on the operative parameters., Conclusions: The results from the present study showed a significant reduction in the contrast volume and radiation dose for endovascular treatment of PAD when applying 2D fusion imaging technology. Overall, a reduction of >30% was observed for both operative parameters, without excessive training requirements, highlighting the potential benefits of using 2D fusion imaging when performing endovascular revascularization for PAD., (Copyright © 2021 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Background: Fusion imaging makes it possible to improve endovascular procedures and is mainly used in hybrid rooms for aortic procedures. The objective of this study was to evaluate the feasibility of fusion imaging for femoropopliteal endovascular procedures with a mobile flat plane sensor and dedicated software to assist endovascular navigation., Materials and Methods: Between May and December 2017, 41 patients requiring femoropopliteal endovascular revascularization were included. Interventions were carried out in a conventional surgical room equipped with a mobile plane sensor (Cios Alpha, Siemens). The numerical video stream was transmitted to an angionavigation station (EndoNaut (EN), Therenva). The software created an osseous and arterial panorama of the treated limb from the angiographies carried out at the beginning of procedure. After each displacement of the table, the software relocated the current image on the osseous panorama, with 2D-2D resetting, and amalgamated the mask of the arterial panorama. The success rates of creation of osseous and arterial panorama and the success of relocation were evaluated. The data concerning irradiation, the volume of contrast (VC) injected, and operative times were recorded., Results: Osseous panoramas could be automatically generated for the 41 procedures, without manual adjustment in 33 cases (80.5%). About 35 relocations based on a 2D-2D resetting could be obtained in the 41 procedures, with a success rate of 85%. The causes of failure were a change in table height or arch angulation. The average duration of intervention was 74.5 min. The irradiation parameters were duration of fluoroscopy 17.8 ± 13.1 min, air kerma 80.5 ± 68.4 mGy, and dose area product 2140 ± 1599 μGy m 2 . The average VC was 24.5 ± 14 mL., Conclusions: This preliminary study showed that fusion imaging is possible in a nonhybrid room for peripheral procedures. Imagery of mobile C-arms can be improved for femoropopliteal endovascular procedures without heavy equipment. These imagery tools bring an operative comfort and could probably reduce irradiation and the injected VC. The clinical benefit must be evaluated in more patients in a randomized comparative study with a rigorous methodology., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: The fusion of pre/intraoperative images may improve catheter manipulation during radioembolization (RE) interventions by adding relevant information. The objective of this work is to propose and evaluate the performance of a RE guidance strategy relying on structure-driven intensity-based registration between preoperative CTA and intraoperative X-ray images., Methods: The navigation strategy is decomposed into three image fusion steps, supporting the catheter navigation from the femoral artery till reaching the injection site (IS). During the pretreatment assessment intervention, the aorta and the origins of its side branches are projected on the intraoperative 2D fluoroscopy following a 3D/2D bone-based registration process, to assist the celiac trunk access. Subsequently, a similar approach consisting in projecting the hepatic vasculature on intraoperative DSA through 3D/2D vessel-based registration is performed to assist the IS location. Lastly, the selected IS is reproduced during the treatment intervention by employing 2D/2D image-based registration between pretreatment and treatment fluoroscopic images., Results: The three fusion steps were independently evaluated on subsets of 20, 19 and 5 patient cases, respectively. Best results were obtained with gradient difference as similarity measure and with a delimited preoperative vascular structure for vessel-based registration. The approach resulted in qualitatively appropriate anatomical correspondences when projecting the preoperative structures on intraoperative images. With the best configuration, the registration steps showed accuracy and feasibility in aligning data, with global mean landmarks errors of 1.59 mm, 2.32 mm and 2.17 mm, respectively, a computation time that never exceeded 5 s, 25 s and 11 s, respectively, and a user interaction limited to manual initialization of the 3D/2D registration., Conclusion: An image fusion-based approach has been specifically proposed for RE procedures guidance. The catheter manipulation strategy based on the fusion of pre- and intraoperative images has the potential to support different steps of the RE clinical workflow and to guide the overall procedure.

In a clinical decision support system, the purpose of case-based reasoning is to help clinicians make convenient decisions for diagnoses or interventional gestures. Past experience, which is represented by a case-base of previous patients, is exploited to solve similar current problems using four steps-retrieve, reuse, revise, and retain. The proposed case-based reasoning has been focused on transcatheter aortic valve implantation to respond to clinical issues pertaining vascular access and prosthesis choices. The computation of a relevant similarity measure is an essential processing step employed to obtain a set of retrieved cases from a case-base. A hierarchical similarity measure that is based on a clinical decision tree is proposed to better integrate the clinical knowledge, especially in terms of case representation, case selection and attributes weighting. A case-base of 138 patients is used to evaluate the case-based reasoning performance, and retrieve- and reuse-based criteria have been considered. The sensitivity for the vascular access and the prosthesis choice is found to 0.88 and 0.94, respectively, with the use of the hierarchical similarity measure as opposed to 0.53 and 0.79 for the standard similarity measure. Ninety percent of the suggested solutions are correctly classified for the proposed metric when four cases are retrieved. Using a dedicated similarity measure, with relevant and weighted attributes selected through a clinical decision tree, the set of retrieved cases, and consequently, the decision suggested by the case-based reasoning are substantially improved over state-of-the-art similarity measures., Competing Interests: The authors have declared that no competing interests exist. FL as a commercial affiliation with Therenva. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: During endovascular aneurysm repair (EVAR), complex iliac anatomy is a source of complications such as unintentional coverage of the hypogastric artery. The aim of our study was to evaluate ability to predict coverage of the hypogastric artery using a biomechanical model simulating arterial deformations caused by the delivery system., Methods: The biomechanical model of deformation has been validated by many publications. The simulations were performed on 38 patients included retrospectively, for a total of 75 iliac arteries used for the study. On the basis of objective measurements, two groups were formed: one with "complex" iliac anatomy (n = 38 iliac arteries) and the other with "simple" iliac anatomy (n = 37 iliac arteries). The simulation enabled measurement of the lengths of the aorta and the iliac arteries once deformed by the device. Coverage of the hypogastric artery was predicted if the deformed renal/iliac bifurcation length (L pre ) was less than the length of the implanted device (L stent -measured on the postoperative computed tomography [CT]) and nondeformed L pre was greater than L stent ., Results: Nine (12%) internal iliac arteries were covered unintentionally. Of the coverage attributed to perioperative deformations, 1 case (1.3%) occurred with simple anatomy and 6 (8.0%) with complex anatomy (P = 0.25). All cases of unintentional coverage were predicted by the simulation. The simulation predicted hypogastric coverage in 35 cases (46.7%). There were therefore 26 (34.6%) false positives. The simulation had a sensitivity of 100% and a specificity of 60.6%. On multivariate analysis, the factors significantly predictive of coverage were the iliac tortuosity index (P = 0.02) and the predicted margin between the termination of the graft limb and the origin of the hypogastric artery in nondeformed (P = 0.009) and deformed (P = 0.001) anatomy., Conclusions: Numerical simulation is a sensitive tool for predicting the risk of hypogastric coverage during EVAR and allows more precise preoperative sizing. Its specificity is liable to be improved by using a larger cohort., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Purpose: To quantify the displacement of the vascular structures after insertion of stiff devices during endovascular aneurysm repair (EVAR) of abdominal aortic aneurysm and to identify potential parameters influencing this displacement., Materials and Methods: A total of 50 patients from a single center undergoing EVAR were prospectively enrolled between January 2016 and December 2017. Fusion imaging was employed using the EndoNaut (Therenva, Rennnes, France) station through a 3-dimensional (3D)/2-dimensional (2D) technology synchronizing the 3D computed tomography scan to the live intraoperative fluoroscopy. The accuracy of the fusion roadmap was evaluated before deployment by conventional digital subtraction angiogram on a single plane (with different C-arm incidences)., Results: The mean displacement error of the ostium of the lowest renal artery was 4.1 ± 2.4 mm (range, 0-11.7 mm), with a left/right displacement of 1.6 ± 1.7 mm (range, 0-6.9 mm) and a craniocaudal displacement of 3.5 ± 2.4 mm (range, 0-11.3 mm). The correction required for the ostium of the lower renal artery was mostly cranial and to the left. Multiple linear regression analysis revealed only the sharpest angle between the aneurysm neck and sac as the factor influencing the accuracy of fusion imaging. All other parameters did not show any correlation., Conclusions: This study identified the sources of fusion error after insertion of rigid material during EVAR. As the sharpest angulation between aneurysm neck and sac increases, the overall accuracy of the fusion might be affected., (Copyright © 2019 SIR. Published by Elsevier Inc. All rights reserved.)

Purpose: Minimally invasive trans-catheter aortic valve implantation (TAVI) has emerged as a treatment of choice for high-risk patients with severe aortic stenosis. However, the planning of TAVI procedures would greatly benefit from automation to speed up, secure and guide the deployment of the prosthetic valve. We propose a hybrid approach allowing the computation of relevant anatomical measurements along with an enhanced visualization., Material and Methods: After an initial step of centerline detection and aorta segmentation, model-based and statistical-based methods are used in combination with 3 D active contour models to exploit the complementary aspects of these methods and automatically detect aortic leaflets and coronary ostia locations. Important anatomical measurements are then derived from these landmarks., Results: A validation on 50 patients showed good precision with respect to expert sizing for the ascending aorta diameter calculation (2.2 ± 2.1 mm), the annulus diameter (1.31 ± 0.75 mm), and both the right and left coronary ostia detection (1.96 ± 0.87 mm and 1.80 ± 0.74 mm, respectively). The visualization is enhanced thanks to the aorta and aortic root segmentation, the latter showing good agreement with manual expert delineation (Jaccard index: 0.96 ± 0.03)., Conclusion: This pipeline is promising and could greatly facilitate TAVI planning.

Background: Fusion imaging is a technique that facilitates endovascular navigation but is only available in hybrid rooms. The goal of this study was to evaluate the feasibility of fusion imaging with a mobile C-arm in a conventional operating room through the use of an angionavigation station., Methods: From May 2016 to June 2017, the study included all patients who underwent an aortic stent graft procedure in a conventional operating room with a mobile flat-panel detector (Cios Alpha, Siemens) connected to an angionavigation station (EndoNaut, Therenva). The intention was to perform preoperative 3D computerized tomography/perioperative 2D fluoroscopy fusion imaging using an automatic registration process. Registration was considered successful when the software was able to correctly overlay preoperative 3D vascular structures onto the fluoroscopy image. For EVAR, contrast dose, operation time, and fluoroscopy time (FT) were compared with those of a control group drawn from the department's database who underwent a procedure with a C-arm image intensifier., Results: The study included 54 patients, and the procedures performed were 49 EVAR, 2 TEVAR, 2 IBD, and 1 FEVAR. Of the 178 registrations that were initialized, it was possible to use the fusion imaging in 170 cases, that is, a 95.5% success rate. In the EVAR comparison, there were no difference with the control group (n = 103) for FT (21.9 ± 12 vs. 19.5 ± 13 min; P = 0.27), but less contrast agent was used in the group undergoing a procedure with the angionavigation station (42.3 ± 22 mL vs. 81.2 ± 48 mL; P < 0.001), and operation time was shorter (114 ± 44 vs. 140.8 ± 38 min; P < 0.0001)., Conclusions: Fusion imaging is feasible with a mobile C-arm in a conventional operating room and thus represents an alternative to hybrid rooms. Its clinical benefits should be evaluated in a randomized series, but our study already suggests that EVAR procedures might be facilitated with an angionavigation system., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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 semiautomatic 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 and [Formula: see text] mm, respectively), similarly to the registration on the 3D CTA (errors of [Formula: see text] mm), with minimal user interaction and very low computation time. First results of an on-going clinical study highlighted its major clinical added value on intraoperative parameters., Conclusion: 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.

Purpose: Abdominal aortic aneurysm (AAA) is a localized, permanent and irreversible enlargement of the artery, with the formation of thrombus into the inner wall of the aneurysm. A precise patient-specific segmentation of the thrombus is useful for both the pre-operative planning to estimate the rupture risk, and for post-operative assessment to monitor the disease evolution. This paper presents a generic approach for 3D segmentation of thrombus from patients suffering from AAA using computed tomography angiography (CTA) scans., Methods: A fast and versatile thrombus segmentation approach has been developed. It is composed of initial centerline detection and aorta lumen segmentation, an optimized pre-processing stage and the use of a 3D deformable model. The approach has been designed to be very generic and requires minimal user interaction. The proposed method was tested on different datasets with 145 patients overall, including pre- and post-operative CTAs, abdominal aorta and iliac artery sections, different calcification degrees, aneurysm sizes and contrast enhancement qualities., Results: The thrombus segmentation approach showed very accurate results with respect to manual delineations for all datasets ([Formula: see text] and [Formula: see text] for abdominal aorta sections on pre-operative CTA, iliac artery sections on pre-operative CTAs and aorta sections on post-operative CTA, respectively). Experiments on the different patient and image conditions showed that the method was highly versatile, with no significant differences in term of precision. Comparison with the level-set algorithm also demonstrated the superiority of the 3D deformable model. Average processing time was [Formula: see text]., Conclusion: We presented a near-automatic and generic thrombus segmentation algorithm applicable to a large variability of patient and imaging conditions. When integrated in an endovascular planning system, our segmentation algorithm shows its compatibility with clinical routine and could be used for pre-operative planning and post-operative assessment of endovascular procedures.

Objective: Sac shrinkage is considered a reliable surrogate marker of success after endovascular aneurysm repair (EVAR). Whereas sac shrinkage is the best expected outcome, predictive factors of sac shrinkage remain unclear. The aim of this study was to identify the role of preoperative and postoperative influencing factors of sac reduction after EVAR., Methods: Online searches across MEDLINE, Embase, and Cochrane Library medical databases were simultaneously performed. Study effects were pooled using a random-effects model, and forest plots were generated for every potential influencing factor., Results: A total of 24 studies with 14,754 patients were included (mean age, 73.4 years; 76% male). At a mean follow-up of 24 months, the pooled shrinkage proportion was 47%. Random-effects meta-analysis revealed that renal impairment (odds ratio [OR], 0.74; 95% confidence interval [CI], 0.57-0.96), type I endoleaks (OR, 0.17; 95% CI, 0.08-0.39), type II endoleaks (OR, 0.21; 95% CI, 0.14-0.33), and combined type I and type II endoleaks (OR, 0.32; 95% CI, 0.22-0.47) were found to prevent sac shrinkage, whereas hypercholesterolemia (OR, 1.24; 95% CI, 1.02-1.51) and smoking (OR, 1.32; 95% CI, 1.17-1.49) have a significant positive impact on sac shrinkage. In addition, there was a trend toward the association between shrinkage and statin therapy (OR, 4.07; 95% CI, 1.02-16.32) and nearly significant negative impacts of coronary artery disease (OR, 0.84; 95% CI, 0.70-1.01), diabetes (OR, 0.79; 95% CI, 0.60-1.04), and sac thrombus (OR, 0.88; 95% CI, 0.77-1.01) on sac shrinkage., Conclusions: In this large meta-analysis of patients undergoing EVAR, we found that several comorbidity and postoperative factors were associated with postoperative sac shrinkage. These findings may contribute to a better understanding of the shrinkage process of patients undergoing EVAR., (Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Background: Type II endoleaks (T2Es) remain the Achilles heel of endovascular aneurysm repair (EVAR), involving a close follow-up and sometimes leading to reintervention. Identifying risk factors impacting T2Es is of concern to improve decision making and optimize follow-up. However, it has led to contradictory results, with supporting evidence for the majority of factors being weak., Methods: A systematic review and meta-analysis was conducted to study risk factors of T2Es following EVAR to identify risk factors and measure their dedicated strength of association. Using a literature search of MEDLINE, EMBASE, and the Cochrane Library, 31 retrospective studies including a total of 15,793 patients were identified and fulfilled the strict specified inclusion criteria. Random-effects meta-analysis was conducted for each factor to combine effect estimate across studies. A total of 21 factors related to demography, preoperative treatment, comorbidity, and morphology were statistically pooled., Results: On the basis of the pooled odds ratios and their 95% confidence intervals, patency of aortic side branches, represented by the patency of the inferior mesenteric artery, lumbar arteries, or total number of aortic side branches, were found to be significant harmful risk factors of T2Es. Women were also found to have nearly significant higher risk of developing T2Es than men. On the contrary, the following were found to have a significant protective role: smoking, peripheral artery disease, and thrombus load, represented by the maximum thickness at the maximum aneurysm diameter, the presence of circumferential thrombus, or the presence of thrombus at the level of inferior mesenteric artery., Conclusion: Identifying significant risk factors of development of T2Es is mandatory to improve decision making and optimize surveillance planning in EVAR., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: The risk of long-term complications after endovascular aneurysm repair (EVAR) is still higher than open surgery and is a critical issue. This study aims to make available reliable statistical predictive models of complications after EVAR., Methods: Two hundred and thirteen patients who underwent EVAR between 2002 and 2012 were included in this study. The preoperative computed tomography scans were analyzed with a dedicated workstation to provide spatially correct 3-dimensional data. Age, gender, operation-related factors, and 21 morphologic variables were measured and included in the analyses. Five postoperative outcomes were studied. After an initial selection of predictors based on univariate analysis, binomial logistic regression models were proposed for each outcome. The ability to predict each outcome was assessed with receiver operating characteristic curves considering that an area under the curve (AUC) > 0.70 is generally considered sufficiently accurate., Results: The mean age was 74.8 ± 8.6 years with a mean follow-up of 43.8 ± 22.1 months. Respectively, rates and risk factors of each outcome were 25.3% (n = 51) for abdominal aortic aneurysm (AAA) enlargement (age, number of patent sac branches, iliac calcifications and tortuosity, aneurysmal thrombus), 7% (n = 15) for type IA endoleak (neck calcification and AAA diameter), 3.7% (n = 8) for type IB endoleak (iliac tortuosity, AAA diameter, neck thrombus), 19.8% (n = 40) for type II endoleak (female, number of patent sac branches), and 25.9% (n = 55) for reintervention from any cause (neck calcification). The risk associated to each outcome can be calculated with a combination of these different preoperative variables. AUC for each outcome were 79.6% for AAA enlargement, 70.4% for reintervention, 81.3% for type IA endoleak, 92.3% for type IB endoleak, 70.6% for type II endoleak., Conclusions: This study shows that an exhaustive description of the preoperative anatomy before EVAR is a powerful and reliable tool to predict the risk of developing the most common complications after EVAR., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: Deep brain stimulation (DBS) is a surgical procedure for treating motor-related neurological disorders. DBS clinical efficacy hinges on precise surgical planning and accurate electrode placement, which in turn call upon several image processing and visualization tasks, such as image registration, image segmentation, image fusion, and 3D visualization. These tasks are often performed by a heterogeneous set of software tools, which adopt differing formats and geometrical conventions and require patient-specific parameterization or interactive tuning. To overcome these issues, we introduce in this article PyDBS, a fully integrated and automated image processing workflow for DBS surgery., Methods: PyDBS consists of three image processing pipelines and three visualization modules assisting clinicians through the entire DBS surgical workflow, from the preoperative planning of electrode trajectories to the postoperative assessment of electrode placement. The system's robustness, speed, and accuracy were assessed by means of a retrospective validation, based on 92 clinical cases., Results: The complete PyDBS workflow achieved satisfactory results in 92 % of tested cases, with a median processing time of 28 min per patient., Conclusion: The results obtained are compatible with the adoption of PyDBS in clinical practice.

Several hypotheses have been put forward to explain weight gain after deep brain stimulation (DBS), but none provides a fully satisfactory account of this adverse effect. We analyzed the correlation between changes in brain metabolism (using positron emission tomography [PET] imaging) and weight gain after bilateral subthalamic nucleus DBS in patients with Parkinson's disease. Body mass index was calculated and brain activity prospectively measured using 2-deoxy-2[18F]fluoro-D-glucose 3 months before and 4 months after the start of subthalamic nucleus deep brain stimulation in 23 patients with Parkinson's disease. Motor complications (United Parkinson's Disease Rating Scale [UPDRS]-IV scores) and dopaminergic medication were included in the analysis to control for their possible influence on brain metabolism. Mean ± standard deviation (SD) body mass index increased significantly by 0.8 ± 1.5 kg/m(2) (P = 0.03). Correlations were found between weight gain and changes in brain metabolism in limbic and associative areas, including the orbitofrontal cortex (Brodmann areas [BAs] 10 and 11), lateral and medial parts of the temporal lobe (BAs 20, 21, 22,39 and 42), anterior cingulate cortex (BA 32), and retrosplenial cortex (BA 30). However, we found no correlation between weight gain and metabolic changes in sensorimotor areas. These findings suggest that changes in associative and limbic processes contribute to weight gain after subthalamic nucleus DBS in Parkinson's disease., (© 2014 International Parkinson and Movement Disorder Society.)

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective surgical therapy to treat Parkinson's disease (PD). Conventional methods employ standard atlas coordinates to target the STN, which, along with the adjacent red nucleus (RN) and substantia nigra (SN), are not well visualized on conventional T1w MRIs. However, the positions and sizes of the nuclei may be more variable than the standard atlas, thus making the pre-surgical plans inaccurate. We investigated the morphometric variability of the STN, RN and SN by using label-fusion segmentation results from 3T high resolution T2w MRIs of 33 advanced PD patients. In addition to comparing the size and position measurements of the cohort to the Talairach atlas, principal component analysis (PCA) was performed to acquire more intuitive and detailed perspectives of the measured variability. Lastly, the potential correlation between the variability shown by PCA results and the clinical scores was explored., (Copyright © 2014 Wiley Periodicals, Inc.)

Purpose: Surgery is continuously subject to technological and medical innovations that are transforming daily surgical routines. In order to gain a better understanding and description of surgeries, the field of surgical process modelling (SPM) has recently emerged. The challenge is to support surgery through the quantitative analysis and understanding of operating room activities. Related surgical process models can then be introduced into a new generation of computer-assisted surgery systems., Methods: In this paper, we present a review of the literature dealing with SPM. This methodological review was obtained from a search using Google Scholar on the specific keywords: "surgical process analysis", "surgical process model" and "surgical workflow analysis"., Results: This paper gives an overview of current approaches in the field that study the procedural aspects of surgery. We propose a classification of the domain that helps to summarise and describe the most important components of each paper we have reviewed, i.e., acquisition, modelling, analysis, application and validation/evaluation. These five aspects are presented independently along with an exhaustive list of their possible instantiations taken from the studied publications., Conclusion: This review allows a greater understanding of the SPM field to be gained and introduces future related prospects.

Purpose: Deep brain stimulation (DBS) surgery is used to reduce motor symptoms when movement disorders are refractory to medical treatment. Post-operative brain morphology can induce electrode deformations as the brain recovers from an intervention. The inverse brain shift has a direct impact on accuracy of the targeting stage, so analysis of electrode deformations is needed to predict final positions., Methods: DBS electrode curvature was evaluated in 76 adults with movement disorders who underwent bilateral stimulation, and the key variables that affect electrode deformations were identified. Non-linear modelling of the electrode axis was performed using post-operative computed tomography (CT) images. A mean curvature index was estimated for each patient electrode. Multivariate analysis was performed using a regression decision tree to create a hierarchy of predictive variables. The identification and classification of key variables that determine electrode curvature were validated with statistical analysis., Results: The principal variables affecting electrode deformations were found to be the date of the post-operative CT scan and the stimulation target location. The main pathology, patient's gender, and disease duration had a smaller although important impact on brain shift., Conclusions: The principal determinants of electrode location accuracy during DBS procedures were identified and validated. These results may be useful for improved electrode targeting with the help of mathematical models., Competing Interests: The authors declare that they have no conflict of interest.

Surgical Process Modelling (SPM) was introduced to improve understanding the different parameters that influence the performance of a Surgical Process (SP). Data acquired from SPM methodology is enormous and complex. Several analysis methods based on comparison or classification of Surgical Process Models (SPMs) have previously been proposed. Such methods compare a set of SPMs to highlight specific parameters explaining differences between populations of patients, surgeons or systems. In this study, procedures performed at three different international University hospitals were compared using SPM methodology based on a similarity metric focusing on the sequence of activities occurring during surgery. The proposed approach is based on Dynamic Time Warping (DTW) algorithm combined with a clustering algorithm. SPMs of 41 Anterior Cervical Discectomy (ACD) surgeries were acquired at three Neurosurgical departments; in France, Germany, and Canada. The proposed approach distinguished the different surgical behaviors according to the location where surgery was performed as well as between the categorized surgical experience of individual surgeons. We also propose the use of Multidimensional Scaling to induce a new space of representation of the sequences of activities. The approach was compared to a time-based approach (e.g. duration of surgeries) and has been shown to be more precise. We also discuss the integration of other criteria in order to better understand what influences the way the surgeries are performed. This first multi-site study represents an important step towards the creation of robust analysis tools for processing SPMs. It opens new perspectives for the assessment of surgical approaches, tools or systems as well as objective assessment and comparison of surgeon's expertise., (Copyright © 2013 Elsevier Inc. All rights reserved.)

For patients suffering from Parkinson's disease with severe movement disorders, functional surgery may be required when medical therapy is not effective. In Deep Brain Stimulation (DBS), electrodes are implanted within the brain to stimulate deep structures such as SubThalamic Nucleus (STN). The quality of patient surgical outcome is generally related to the accuracy of nucleus targeting during surgery. In this paper, we focused on identifying optimum sites for STN DBS by studying symptomatic motor improvement along with neuropsychological side effects. We described successive steps for constructing digital atlases gathering patient's location of electrode contacts automatically segmented from postoperative images, and clinical scores. Three motor and five neuropsychological scores were included in the study. Correlations with active contact locations were carried out using an adapted hierarchical ascendant classification. Such analysis enabled the extraction of representative clusters to determine the optimum site for therapeutic STN DBS. For each clinical score, we built an anatomo-clinical atlas representing its improvement or deterioration in relation with the anatomical location of electrodes and from a population of implanted patients. To the best of our knowledge, we reported for the first time a discrepancy between a very good motor improvement by targeting the postero-superior region of the STN and an inevitable deterioration of the categorical and phonemic fluency in the same region. Such atlases and associated analysis may help better understanding of functional mapping in deep structures and may help pre-operative decision-making process and especially targeting., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Purpose: Surgical process models (SPMs) have recently been created for situation-aware computer-assisted systems in the operating room. One important challenge in this area is the automatic acquisition of SPMs. The purpose of this study is to present a new method for the automatic detection of low-level surgical tasks, that is, the sequence of activities in a surgical procedure, from microscope video images only. The level of granularity that we addressed in this work is symbolized by activities formalized by triplets  ., Methods: Using the results of our latest work on the recognition of surgical phases in cataract surgeries, and based on the hypothesis that most activities occur in one or two phases only, we created a light-weight ontology, formalized as a hierarchical decomposition into phases and activities. Information concerning the surgical tools, the areas where tools are used and three other visual cues were detected through an image-based approach and combined with the information of the current surgical phase within a knowledge-based recognition system. Knowing the surgical phases before the activity, recognition allows supervised classification to be adapted to the phase. Multiclass Support Vector Machines were chosen as a classification algorithm., Results: Using a dataset of 20 cataract surgeries, and identifying 25 possible pairs of activities, a frame-by-frame recognition rate of 64.5 % was achieved with the proposed system., Conclusions: The addition of human knowledge to traditional bottom-up approaches based on image analysis appears to be promising for low-level task detection. The results of this work could be used for the automatic indexation of post-operative videos.

Purpose: The optimal electrode trajectory is needed to assist surgeons in planning Deep Brain Stimulation (DBS). A method for image-based trajectory planning was developed and tested., Methods: Rules governing the DBS surgical procedure were defined with geometric constraints. A formal geometric solver using multimodal brain images and a template built from 15 brain MRI scans were used to identify a space of possible solutions and select the optimal one. For validation, a retrospective study of 30 DBS electrode implantations from 18 patients was performed. A trajectory was computed in each case and compared with the trajectories of the electrodes that were actually implanted., Results: Computed trajectories had an average difference of 6.45° compared with reference trajectories and achieved a better overall score based on satisfaction of geometric constraints. Trajectories were computed in 2 min for each case., Conclusion: A rule-based solver using pre-operative MR brain images can automatically compute relevant and accurate patient-specific DBS electrode trajectories.

In the creation of new computer-assisted intervention systems, Surgical Process Models (SPMs) are an emerging concept used for analyzing and assessing surgical interventions. SPMs represent Surgical Processes (SPs) which are formalized as symbolic structured descriptions of surgical interventions using a pre-defined level of granularity and a dedicated terminology. In this context, one major challenge is the creation of new metrics for the comparison and the evaluation of SPs. Thus, correlations between these metrics and pre-operative data are used to classify surgeries and highlight specific information on the surgery itself and on the surgeon, such as his/her level of expertise. In this paper, we explore the automatic classification of a set of SPs based on the Dynamic Time Warping (DTW) algorithm. DTW is used to compute a similarity measure between two SPs that focuses on the different types of activities performed during surgery and their sequencing, by minimizing time differences. Indeed, it turns out to be a complementary approach to the classical methods that only focus on differences in the time and the number of activities. Experiments were carried out on 24 lumbar disk herniation surgeries to discriminate the surgeons level of expertise according to a prior classification of SPs. Supervised and unsupervised classification experiments have shown that this approach was able to automatically identify groups of surgeons according to their level of expertise (senior and junior), and opens many perspectives for the creation of new metrics for comparing and evaluating surgeries., (Copyright © 2011. Published by Elsevier Inc.)

In image-guided surgery, a new generation of Computer-Assisted-Surgical (CAS) systems based on information from the Operating Room (OR) has recently been developed to improve situation awareness in the OR. Our main project is to develop an application-dependant framework able to extract high-level tasks (surgical phases) using microscope videos data only. In this paper, we present two methods: one method to segment the pupil and one to extract and recognize surgical tools. We show how both methods improve the accuracy of the framework for analysis of cataract surgery videos, to detect eight surgical phases.

Surgical process analysis and modeling is a recent and important topic aiming at introducing a new generation of computer-assisted surgical systems. Among all of the techniques already in use for extracting data from the Operating Room, the use of image videos allows automating the surgeons' assistance without altering the surgical routine. We proposed in this paper an application-dependent framework able to automatically extract the phases of the surgery only by using microscope videos as input data and that can be adaptable to different surgical specialties. First, four distinct types of classifiers based on image processing were implemented to extract visual cues from video frames. Each of these classifiers was related to one kind of visual cue: visual cues recognizable through color were detected with a color histogram approach, for shape-oriented visual cues we trained a Haar classifier, for texture-oriented visual cues we used a bag-of-word approach with SIFT descriptors, and for all other visual cues we used a classical image classification approach including a feature extraction, selection, and a supervised classification. The extraction of this semantic vector for each video frame then permitted to classify time series using either Hidden Markov Model or Dynamic Time Warping algorithms. The framework was validated on cataract surgeries, obtaining accuracies of 95%.

New MR imaging protocols enable visualization of brain structures. However, for dedicated clinical applications such as targeting deep brain stimulation (DBS), a more accurate localization requires the use of atlases. We developed a three-dimensional digitized mono-subject anatomical template of the human brain based on 3-T magnetic resonance images (MRI). By averaging 15 registered T1 image acquisitions, we have shown that the final image corresponds to an optimal image, limited by the performance of the 3-T MR machine. We compared different preprocessing workflows for template construction. With the optimal strategy, along with validated existing processing methods, one T1 template and one T1-T2 mixing template were created in order to improve visualization of spatially complex deep structures. Reduction of voxel size to 0.25 mm(3) was also advantageous to observe fine structures and white matter/gray matter intensity crossings. Results demonstrated that such a template also improved inter-patient registration for population comparison in DBS. These MR templates are made freely available to our community (http://www.vmip.org/mritemplate) to serve as a reference for neuroimage processing methods.