Development of a MR Training System for Living Donor Liver Transplantation Using Simulated Liver Phantom and ICP Tracking Technology | IEEE Journals & Magazine | IEEE Xplore

Development of a MR Training System for Living Donor Liver Transplantation Using Simulated Liver Phantom and ICP Tracking Technology


Abstract:

Living donor liver transplantation (LT) is a curative treatment for decompensation liver cirrhosis, some metabolic diseases, and acute liver failure. For specific conditi...Show More

Abstract:

Living donor liver transplantation (LT) is a curative treatment for decompensation liver cirrhosis, some metabolic diseases, and acute liver failure. For specific conditions of hepatocellular carcinoma, LT provides a better prognosis than other known treatments do. During living donor LT, recognition and preservation of the middle hepatic vein (MHV) and its main branch are extremely important and closely related to the outcomes for the donor and recipient. Currently, preoperative computed tomography (CT) scans and intraoperative ultrasound are used to evaluate the location of the MHV; however, the information from CT scans and ultrasound is two-dimensional and lacks specific perception data. To achieve better MHV tracking during surgery, this work presents a mixed-reality (MR) training system for open liver LT surgery, which uses a simulated elastic liver phantom and iterative closest point (ICP) tracking technology. We created a three-dimensional (3-D) liver reconstruction model based on CT images from 20 patients and produced a series of equal-sized elastic liver phantoms with soft vessels inside. The ICP algorithm was used to track the liver phantom with the MR system, and the 3-D reconstruction model was superimposed on the phantom. The experimental results revealed that the registration error was <4 mm. The feedback from ten novice surgeons who practiced with the proposed system was positive. It is expected that the proposed system for LT could enhance the overall effectiveness of surgeon training and serve as a reference for other applications in the future.
Published in: IEEE Transactions on Human-Machine Systems ( Volume: 54, Issue: 6, December 2024)
Page(s): 678 - 687
Date of Publication: 10 September 2024

ISSN Information:

Funding Agency:


Contact IEEE to Subscribe

References

References is not available for this document.