Abstract:

Stereotactic arrhythmia radioablation (STAR) represents an emerging non-invasive treatment for therapy-refractory ventriculartachycardia. Yet, planning remains challenged by multimodal cardiac imaging integration, electroanatomical mapping (EAM) transfer,and cardiorespiratory motion effects on dose delivery. Current radiotherapy (RT) planning systems offer mainly static visualizationand limited access to intramural myocardial structures, hindering communication between cardiology and radiation oncology teams.We present a novel extended reality (XR) simulator designed to dynamically visualize STAR-relevant imaging and planning data. Thesystem integrates diastolic cardiac CT, respiratory-binned 4DCT, anatomical segmentations, EAM data, and phase-recomputed RTdose distributions within an XR environment. Cardiac structures are propagated across respiratory phases using deformable registration,while dose distributions are recomputed on each respiratory-binned CT, enabling phase-specific inspection of dose conformalityfor both planning target volumes (PTVs) and cardiac target volumes (CardTVs). The resulting time-resolved volumetric dataset is renderedin XR, allowing clinicians to explore cardiac motion, visualize intramural dose deposition, and jointly assess target and organ-at-risk dynamics. This supports qualitative evaluation of dose-motion interplay and interdisciplinary interpretation of intramural targets.The system was tested on three STAR patients enrolled in the RAVENTA trial. Motion analysis revealed PTV centroid displacementamplitudes over the breathing cycle of up to 17.5, 10.2, and 8.9 mm for patients 1, 2, and 3, respectively, with conformity numbervariations of 0.38, 0.34, and 0.19. Expert evaluation showed positive perceived utility for target-anatomy-dose understanding, motioninterpretation, and multidisciplinary communication. This proof-of-concept demonstrates the feasibility and potential clinical valueof XR-based motion-aware dose visualization for STAR planning.

Abstract:

Mitral regurgitation is a structural heart disease characterized by dysfunction of the mitral valve. It can be treated through minimally invasive transcatheter edge-to-edge repair procedures, such as those involving the MitraClip system. Despite its clinical efficacy, the procedure poses technical challenges, requiring interventional cardiologists to undergo multiple procedures to achieve instrumentation proficiency. Previous studies have demonstrated the efficacy of extended reality simulators in improving surgical skill training. The purpose of this study is to evaluate the effectiveness of visuo-haptic feedback within an extended reality simulator to accelerate and enhance the learning process for the MitraClip procedure, potentially offering a valuable tool in surgical training. In this study, based on the prototype of our previous simulator, we further implemented a novel cardiac dynamic environment, a visual feedback interface, an innovative haptic armband, and a new model of the catheter to enhance the simulator’s fidelity and educational usability. Experiment results demonstrated that users benefiting from visuo-haptic feedback exhibited significantly reduced completion times, reduced deviation from predefined trajectories, and presented safer navigation results. Moreover, users preferred the visuo-haptic feedback experience, highlighting its perceived value in enhancing training. These findings underscore the potential of adding visuo-haptic feedback to extended reality simulators in surgical training for complex procedures, improving proficiency and patient surgery outcomes.

Abstract:

Mitral regurgitation is a structural heart condition affecting the mitral valve that can be treated with the MitraClip system ™, a device that allows a percutaneous intervention for the deployment of a catheter-embedded clip on the valve leaflets to prevent blood-backflow from the left ventricle to the left atrium. Despite its efficacy, the procedure presents technical challenges, relying on fluoroscopy guidance and surgeon expertise. In the context of human-machine interface for autonomous robotic catheter cardiac intervention, this study aims to evaluate the effectiveness of Augmented Reality (AR) for training surgeons in the MitraClip procedure. Users using an AR Interface demonstrated better performance compared to those using an interface emulating traditional visualization methodologies (fluoroscopy and transesophageal echocardiogra-phy). AR-based training offers a more engaging and effective learning experience, leading to improved surgical dexterity and safety in the procedure.

Abstract:

Stereoelectroencephalography is a minimally invasive neurosurgical technique used to lo-calize the epileptogenic zone (region responsible for seizure generation) in patients withdrug-resistant epilepsy. The procedure requires the placement of multiple intracranial elec-trodes along carefully planned trajectories that reach predefined brain targets while avoidingcritical structures such as blood vessels. Planning these trajectories is complex and timeconsuming, relying heavily on manual inspection of medical images and surgical expertise.Consequently, there is increasing interest in computer-assisted approaches that can supportclinicians during the planning process.This thesis investigates the feasibility of using deep reinforcement learning to automatethe trajectory generation step of the SEEG planning workflow. The proposed frameworkintegrates medical imaging, vascular modeling, and a reinforcement learning agent into amodular pipeline. The trajectory planning task is then formulated as a sequential decision-making problem in which an agent iteratively adjusts electrode entry and target points. Inorder to approximate the action-value function and guide the search through the anatomicalspace, a Deep Q-Network is used, together with a reward formulation that incorporates safetyand geometric constraints, particularly vessel avoidance.Experimental evaluation using patient imaging data demonstrates that the RL agent cangenerate candidate electrode trajectories that reach predefined targets while maintaining safedistances from vascular structures. In this work, 2000 candidate trajectories were generatedand evaluated, with feasibility rates ranging from about 26% to 52% depending on anatomicalcomplexity. Generation of trajectories requires only a few seconds per electrode, indicatingthe potential for substantial reductions in planning time.Overall, the results demonstrate that reinforcement learning can effectively automate thegeneration of candidate SEEG trajectories and provide a foundation for future computer-assisted surgical planning systems.

Abstract:

Accurate pelvic alignment is essential for assessing femoroacetabular impingement (FAI) when using CT-based three-dimensional bone models, as the acquisition is performed in a supine rather than a standing position, which does not accurately reflect the patients kinematics. This study investigated whether reliable pelvic alignment could be achieved using a reduced, unilateral field of view (FOV) based solely on treatment-side anatomical landmarks,with the goal of reducing both radiation exposure and segmentation time.Correlation analyses of landmarks, found on the pelvis and femora, showed strong interdependence, with Pearson correlation coefficients reaching up to 97 %, enabling the selection of the femoral head center (FHC), anterior inferior iliac spine (AIIS), and pubic tubercle (PT) for alignment. Regression models were trained on these landmarks to accurately predict both treatment-side and contralateral anterior superior iliac spine (ASIS) positions, which are required for the standard alignment method, achieving mean absolute errors between 2.39 mm and 6.03 mm across all three cartesian axes. Error propagation analyses indicated that the alignment differences resulting from the predictions averaged by 2.8 →, which was expected to influence only the average anteversion measurement in less than 15 % of the cases. Isolated outliers were observed for Center Edge and Tönnigs Angle.Reducing the superior scan limit, from the ASIS to the AIIS, allowed for an average reduction of 21 % in scan length, translating into a proportional decrease in radiation exposure. Furthermore, the findings of this study support the hypothesis that unilateral landmark information is sufficient for reliable pelvic alignment and FAI evaluation, offering a practical approach to optimize segmentation time and reduce patient dose. Further validation on larger datasets is recommended to ensure robustness and generalizability.