🤖 AI Summary
Existing prostate phantoms lack dynamic volume modulation capability, limiting their ability to simulate both symmetric and asymmetric progression of benign prostatic hyperplasia (BPH). To address this, we propose a pneumatically actuated, multi-chamber prostate phantom with independent chamber control, integrating MRI-based morphological analysis, finite element modeling (FEM), and 3D reconstruction for high-fidelity, programmable volumetric dynamics. This design represents the first implementation of spatially differentiated inflation control across multiple anatomical regions, significantly enhancing physiological fidelity in BPH pathology simulation. Experimental validation demonstrates a forward-modeling average error of only 3.47% and an inverse-modeling error as low as 1.41%, confirming its high accuracy for robot-assisted surgical validation and medical training. The phantom bridges a critical gap in dynamic prostate simulation, enabling realistic, controllable, and quantitatively verifiable BPH progression modeling.
📝 Abstract
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.