4.4 Article

Validation of a semi-automatic co-registration of MRI scans in patients with brain tumors during treatment follow-up

Journal

NMR IN BIOMEDICINE
Volume 29, Issue 7, Pages 882-889

Publisher

WILEY
DOI: 10.1002/nbm.3538

Keywords

linear co-registration; non-linear co-registration; brain tumors; high-grade gliomas; MRI; treatment response; validation; structural similarity

Funding

  1. National Institute of Health Clinician Scientist Fellowship
  2. Remmert Adriaan Laan Fund
  3. Rene Vogels Fund
  4. Chang Gung Medical Foundation
  5. Chang Gung Memorial Hospital, Keelung, Taiwan
  6. UK National Institute for Health Research (NIHR)
  7. National Institute for Health Research [NIHR/CS/009/011] Funding Source: researchfish
  8. National Institutes of Health Research (NIHR) [NIHR/CS/009/011] Funding Source: National Institutes of Health Research (NIHR)

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There is an expanding research interest in high-grade gliomas because of their significant population burden and poor survival despite the extensive standard multimodal treatment. One of the obstacles is the lack of individualized monitoring of tumor characteristics and treatment response before, during and after treatment. We have developed a two-stage semi-automatic method to co-register MRI scans at different time points before and after surgical and adjuvant treatment of high-grade gliomas. This two-stage co-registration includes a linear co-registration of the semi-automatically derived mask of the preoperative contrast-enhancing area or postoperative resection cavity, brain contour and ventricles between different time points. The resulting transformation matrix was then applied in a non-linear manner to co-register conventional contrast-enhanced T-1-weighted images. Targeted registration errors were calculated and compared with linear and non-linear co-registered images. Targeted registration errors were smaller for the semi-automatic non-linear co-registration compared with both the non-linear and linear co-registered images. This was further visualized using a three-dimensional structural similarity method. The semi-automatic non-linear co-registration allowed for optimal correction of the variable brain shift at different time points as evaluated by the minimal targeted registration error. This proposed method allows for the accurate evaluation of the treatment response, essential for the growing research area of brain tumor imaging and treatment response evaluation in large sets of patients. Copyright (c) 2016 John Wiley & Sons, Ltd.

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