A High-Fidelity Phantom for the Simulation and Quantitative Evaluation of Transurethral Resection of the Prostate.
Eunjin ChoiFabian AdamsStefano PalagiAnina GengenbacherDaniel SchlagerPhilippe-Fabian MüllerChristian GratzkeArkadiusz MiernikPeer FischerTian QiuPublished in: Annals of biomedical engineering (2019)
Transurethral resection of the prostate (TURP) is a minimally invasive endoscopic procedure that requires experience and skill of the surgeon. To permit surgical training under realistic conditions we report a novel phantom of the human prostate that can be resected with TURP. The phantom mirrors the anatomy and haptic properties of the gland and permits quantitative evaluation of important surgical performance indicators. Mixtures of soft materials are engineered to mimic the physical properties of the human tissue, including the mechanical strength, the electrical and thermal conductivity, and the appearance under an endoscope. Electrocautery resection of the phantom closely resembles the procedure on human tissue. Ultrasound contrast agent was applied to the central zone, which was not detectable by the surgeon during the surgery but showed high contrast when imaged after the surgery, to serve as a label for the quantitative evaluation of the surgery. Quantitative criteria for performance assessment are established and evaluated by automated image analysis. We present the workflow of a surgical simulation on a prostate phantom followed by quantitative evaluation of the surgical performance. Surgery on the phantom is useful for medical training, and enables the development and testing of endoscopic and minimally invasive surgical instruments.
Keyphrases
- minimally invasive
- benign prostatic hyperplasia
- prostate cancer
- robot assisted
- endothelial cells
- image quality
- high resolution
- coronary artery bypass
- virtual reality
- magnetic resonance
- dual energy
- ultrasound guided
- induced pluripotent stem cells
- monte carlo
- magnetic resonance imaging
- pluripotent stem cells
- healthcare
- mental health
- machine learning
- lymph node
- coronary artery disease
- high throughput
- physical activity
- mass spectrometry