1 PERFORM Centre, Concordia University, Montreal, H4B 1R6, Canada.
2 Department of Electrical and Computer Engineering, Concordia University, Montreal, H3G 1M8, Canada.
3 Department of Neurosurgery, St. Olavs University Hospital, Trondheim, NO-7006, Norway.
4 Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, NO-7491, Norway.
5 Norwegian National Advisory Unit for Ultrasound and Image Guided Therapy, St. Olavs University Hospital, Trondheim, NO-7006, Norway.
6 Department of Medical Technology, SINTEF, Trondheim, NO-7465, Norway.

REtroSpective Evaluation of Cerebral Tumors (RESECT): A clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries.

Med Phys. 2017 Jul;44(7):3875-3882

Authors: Xiao Y, Fortin M, Unsgård G, Rivaz H, Reinertsen I

Abstract

PURPOSE: The advancement of medical image processing techniques, such as image registration, can effectively help improve the accuracy and efficiency of brain tumor surgeries. However, it is often challenging to validate these techniques with real clinical data due to the rarity of such publicly available repositories.

ACQUISITION AND VALIDATION METHODS: Pre-operative magnetic resonance images (MRI), and intra-operative ultrasound (US) scans were acquired from 23 patients with low-grade gliomas who underwent surgeries at St. Olavs University Hospital between 2011 and 2016. Each patient was scanned by Gadolinium-enhanced T1w and T2-FLAIR MRI protocols to reveal the anatomy and pathology, and series of B-mode ultrasound images were obtained before, during, and after tumor resection to track the surgical progress and tissue deformation. Retrospectively, corresponding anatomical landmarks were identified across US images of different surgical stages, and between MRI and US, and can be used to validate image registration algorithms. Quality of landmark identification was assessed with intra- and inter-rater variability.

DATA FORMAT AND ACCESS: In addition to co-registered MRIs, each series of US scans are provided as a reconstructed 3D volume. All images are accessible in MINC2 and NIFTI formats, and the anatomical landmarks were annotated in MNI tag files. Both the imaging data and the corresponding landmarks are available online as the RESECT database at https://archive.norstore.no (search for "RESECT").

POTENTIAL IMPACT: The proposed database provides real high-quality multi-modal clinical data to validate and compare image registration algorithms that can potentially benefit the accuracy and efficiency of brain tumor resection. Furthermore, the database can also be used to test other image processing methods and neuro-navigation software platforms.

PMID: 28391601 [PubMed - indexed for MEDLINE]