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Jeremie Dequidt

Publications and source records attributed to Jeremie Dequidt.

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Compliance-Based Sensor Placement for Force Sensing on a Sensorized Prostate Phantom

This work presents a compliance-based sensor placement method for force sensing on a sensorized prostate phantom designed for Digital Rectal Examination training. The phantom combines three internal pneumatic chambers, used as intrinsic pressure sensors, with ten surface displacement markers. A finite-element simulation dataset is generated by applying external forces at sampled surface locations, from which a compliance matrix relating force inputs to pressure and displacement responses is constructed. Based on this matrix, we propose a weighted greedy selection strategy that maximizes local force reconstructability while prioritizing the clinically relevant posterior contact region and avoiding marker placement directly within the Region of Interest. Compared with a global QR-based placement strategy, the proposed method increases the mean reconstructability score in the target region by 22.5%. These results suggest that region-aware sparse sensor placement can improve force observability in soft robotic medical phantoms while maintaining a limited and practical sensing configuration.

cs.RO

Active Prostate Phantom with Multiple Chambers

Prostate cancer is a major global health concern, requiring advancements in robotic surgery and diagnostics to improve patient outcomes. A phantom is a specially designed object that simulates human tissues or organs. It can be used for calibrating and testing a medical process, as well as for training and research purposes. Existing prostate phantoms fail to simulate dynamic scenarios. This paper presents a pneumatically actuated prostate phantom with multiple independently controlled chambers, allowing for precise volumetric adjustments to replicate asymmetric and symmetric benign prostatic hyperplasia (BPH). The phantom is designed based on shape analysis of magnetic resonance imaging (MRI) datasets, modeled with finite element method (FEM), and validated through 3D reconstruction. The simulation results showed strong agreement with physical measurements, achieving average errors of 3.47% in forward modeling and 1.41% in inverse modeling. These results demonstrate the phantom's potential as a platform for validating robotic-assisted systems and for further development toward realistic simulation-based medical training.

physics.med-ph