Florent Lalys

Objective. Selective internal radiation therapy delivers Y‐90 microspheres through the hepatic arterial system to achieve tumoricidal absorbed dose while limiting normal‐tissue irradiation. Current pre‐treatment dosimetry requires angiography and 99 mTc‐MAA imaging, resulting in an invasive and resource‐intensive workflow. This study evaluates the feasibility of estimating post‐therapy Y‐90 dose distribution directly from diagnostic multiphasic CT using an automated deep‐learning pipeline, without requiring angiography or surrogate‐particle imaging. Approach. In 152 treatments from the multicenter PROACTIF registry, arterial‐ and portal‐phase CT, together with clinically documented perfusion territories and injected activities, were processed through automated segmentation, rigid registration, differential CT generation, and a generative model synthesizing a PET‐like Y‐90 activity map. Absorbed‐dose maps were computed using the Local Dose Deposition method, and predictive accuracy was assessed using tumor dose–volume metrics and voxelwise spatial agreement. Main results. For the best‐performing configuration, the median tumor D70 error was 23 Gy, with substantial inter‐patient variability reflecting the challenges of CT‐only dose prediction. High‐dose coverage (V200) showed a similar pattern. In contrast, spatial agreement was consistently strong, with perfused‐volume gamma passing rates around 93% and tumor‐level rates around 85%, indicating that the macro‐scale distribution of microspheres can be approximated from diagnostic CT. The full pipeline was fully automated and produced predictive dosimetry in under 30 S. Significance. Pre‐angiography CT contains exploitable information about arterial enhancement and perfusion patterns that enables feasible CT‐only prediction of Y‐90 spatial distribution. While tumor‐level absorbed‐dose metrics remain variable, the strong spatial agreement suggests potential utility as an early planning tool to support patient selection or guide catheterization strategy prior to angiography.

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. 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. 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. 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. 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

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.

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.

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. Graphical Abstract

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.