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

Accepted version
Peer-reviewed

Repository DOI


Type

Article

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Authors

van der Hoorn, Anouk 
Yan, Jiun-Lin 
Larkin, Timothy J 
Boonzaier, Natalie R 

Abstract

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 T1 -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 © 2016 John Wiley & Sons, Ltd.

Description

Keywords

MRI, brain tumors, high-grade gliomas, linear co-registration, non-linear co-registration, structural similarity, treatment response, validation, Adult, Aged, Algorithms, Brain Neoplasms, Female, Follow-Up Studies, Glioblastoma, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Treatment Outcome

Journal Title

NMR Biomed

Conference Name

Journal ISSN

0952-3480
1099-1492

Volume Title

Publisher

Wiley
Sponsorship
This research was funded by a National Institute of Health Clinician Scientist Fellowship [SJP], a Remmert Adriaan Laan Fund [AH], a René Vogels Fund [AH] and a grant from the Chang Gung Medical Foundation and Chang Gung Memorial Hospital, Keelung [JLY]. None of the authors have financial of other conflict of interest related to the work presented in this paper. This paper presents independent research funded by the UK National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the UK NHS, the UK NIHR or the UK Department of Health.