Validation of a semi-automatic co-registration of MRI scans in patients with brain tumors during treatment follow-up.
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.
Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.|Department of Radiology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.|Department of Radiology (EB44), University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands.
Anouk
A
van der Hoorn
Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.|Department of Neurosurgery, Chang Gung Memorial Hospital, Taiwan.|Department of Neurosurgery, Chang Gung University College of Medicine, Taiwan.
Jiun-Lin
JL
Yan
Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
Timothy J
TJ
Larkin
Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
Natalie R
NR
Boonzaier
Department of Radiology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
Tomasz
T
Matys
Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
Stephen J
SJ
Price
D000328:Adult; D000368:Aged; D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000601:surgery*; D005260:Female; D005500:Follow-Up Studies; D005909:Glioblastoma / Q000000981:diagnostic imaging* / Q000473:pathology / Q000628:therapy*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D016896:Treatment Outcome
MRI; brain tumors; high-grade gliomas; linear co-registration; non-linear co-registration; structural similarity; treatment response; validation