| MS Seminar

Name of the Speaker: Ms. Kompella Subha Gayatri (EE16S070)
Guide: Dr. Mohanasankar Sivaprakasam
Online meeting link: https://meet.google.com/dqe-arwn-gki
Date/Time: January 13th 2023 (Friday), 3:00 PM
Title: Automatic 3D Registration of MRI-US for Knee Arthroscopy Guidance

Abstract

Knee arthroscopy is a complex procedure, mostly due to the 2D limited field of view provided by the arthroscope, the strong hand-eye coordination required of the operator, and poor ergonomics. In many cases, it may lead to unintended injuries to the patient and/or postoperative complications. Employing a real-time guidance imaging, such as ultrasound (US), could bring a significant improvement in the outcome. Registration of partial view intra-operative US to pre-operative MRI is an essential step for such image-guided minimally invasive surgeries. In this work, we present an automatic, landmark-free 3D multimodal registration of pre-operative MRI to 4D US (high-refresh-rate 3D-US) for enabling guidance in knee arthroscopy. We focus on the problem of initializing registration in the case of partial views.

The proposed method utilizes a pre-initialization step of using the automatically segmented structures from both modalities to achieve a global geometric initialization. From the US volumes, the required structures are segmented using a pre-trained Mask R-CNN deep network. For MRI, the segmentations are achieved using deformable registration of an open-source SPL knee atlas with the acquired MRI volumes using ANTs (Advanced Normalization Tools). This is followed by computing distance maps of the procured segmentations for registration in the distance space. Following that, the final local refinement between the MRI-US volumes is achieved using the LC2 (Linear correlation of linear combination) metric.

The method is evaluated on 11 cases spanning six subjects, with four levels of knee flexion. The errors obtained through the developed registration algorithm and inter-observer difference values are found to be comparable. We have shown that the proposed algorithm is simple, robust and allows for the automatic global registration of 3D US and MRI that can enable US-based image guidance in minimally invasive procedures.