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Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy

Authors :
Kompella, Gayatri
Singarayan, Jeevakala
Antico, Maria
Sasazawa, Fumio
Yu, Takeda
Ram, Keerthi
Pandey, Ajay K.
Fontanarosa, Davide
Sivaprakasam, Mohanasankar
Kompella, Gayatri
Singarayan, Jeevakala
Antico, Maria
Sasazawa, Fumio
Yu, Takeda
Ram, Keerthi
Pandey, Ajay K.
Fontanarosa, Davide
Sivaprakasam, Mohanasankar
Source :
Applied Sciences
Publication Year :
2022

Abstract

Registration of partial view intra-operative ultrasound (US) to pre-operative MRI is an essential step in image-guided minimally invasive surgery. In this paper, 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. 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. A best-case error of 1.41 mm and 2.34∘ and an average registration error of 3.45 mm and 7.76∘ is achieved in translation and rotation, respectively. An inter-observer variability study is performed, and a mean difference of 4.41 mm and 7.77∘ is reported. 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.

Details

Database :
OAIster
Journal :
Applied Sciences
Notes :
application/pdf
Publication Type :
Electronic Resource
Accession number :
edsoai.on1343977844
Document Type :
Electronic Resource