Abstract
The visual comparison of MRI images obtained before and after neurosurgical procedures is useful to identify tumor recurrences in postoperative images. Image registration algorithms are utilized to establish precise correspondences between subsequent acquisitions. The changes observable in subsequent MRI acquisitions can be tackled by methods combining rigid and non-rigid registration. A rigid step is useful to accommodate global transformations, due, for example, to the different positions and orientations of the patient’s head within the scanning device. Furthermore, brain shift caused by tumor resection can only be tackled by non-rigid approaches. In this work, we propose an automatic iterative method to register pre- and postoperative MRI acquisitions. First, the solution rigidly registers two subsequent images. Then, a deformable registration is computed. The T1-CE and T2 MRI sequences are used to guide the registration process. The method is proposed as a solution to the BraTS-Reg challenge. The method improves the average median absolute error from 7.8 mm to 1.98 mm in the validation set.
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Funding
This work was funded by the H2020 Marie-Curie ITN TRABIT (765148) project. LC is supported by the Translational Brain Imaging Training Network (TRABIT) under the European Union’s ‘Horizon 2020’ Research and Innovation Program (Grant agreement ID: 765148).
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Canalini, L., Klein, J., Gerken, A., Heldmann, S., Hering, A., Hahn, H.K. (2023). Iterative Method to Register Longitudinal MRI Acquisitions in Neurosurgical Context. In: Bakas, S., et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2022. Lecture Notes in Computer Science, vol 13769. Springer, Cham. https://doi.org/10.1007/978-3-031-33842-7_23
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