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Nonlinear deformation of tractography in ultrasound-guided low-grade gliomas resection

Abstract


Purpose

In brain tumor surgeries, maximum removal of cancerous tissue without compromising normal brain functions can improve the patient’s survival rate and therapeutic benefits. To achieve this, diffusion MRI and intra-operative ultrasound (iUS) can be highly instrumental. While diffusion MRI allows the visualization of white matter tracts and helps define the resection plan to best preserve the eloquent areas, iUS can effectively track the brain shift after craniotomy that often renders the pre-surgical plan invalid, ensuring the accuracy and safety of the intervention. Unfortunately, brain shift correction using iUS and automatic registration has never been shown for brain tractography so far despite its rising significance in brain tumor resection.


Methods

We employed a correlation-ratio-based nonlinear registration algorithm to account for brain shift through MRI–iUS registration and used the recovered deformations to warp both the brain anatomy and tractography seen in pre-surgical plans. The overall technique was demonstrated retrospectively on four patients who underwent iUS-guided low-grade brain gliomas resection.


Results

Through qualitative and quantitative evaluations, the preoperative MRI and iUS scans were well realigned after nonlinear registration, and the deformed brain tumor volumes and white matter tracts showed large displacements away from the pre-surgical plans.


Conclusions

We are the first to demonstrate the technique to track nonlinear deformation of brain tractography using real clinical MRI and iUS data, and the results confirm the need for updating white matter tracts due to tissue shift during surgery.
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Category

Academic article

Language

English

Author(s)

Affiliation

  • Concordia University
  • Norwegian University of Science and Technology
  • St. Olavs Hospital, Trondheim University Hospital
  • SINTEF Digital / Health Research

Year

2018

Published in

International Journal of Computer Assisted Radiology and Surgery

ISSN

1861-6410

Publisher

Springer

Volume

13

Issue

3

Page(s)

457 - 467

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