Dataset Information

ReMIND: The Brain Resection Multimodal Imaging Database.

ABSTRACT: The standard of care for brain tumors is maximal safe surgical resection as the first step. Neuronavigation augments the surgeon's ability to achieve this but loses validity due to brain shift as surgery progresses. Moreover, many gliomas are difficult to distinguish from adjacent healthy brain tissue. Intraoperative MRI (iMRI) is a useful surgical adjunct that can be used to visualize the residual tumor and brain shift. Intraoperative ultrasound (iUS) serves a similar purpose, while also being faster and easier to incorporate into the workflow. However, it provides lower contrast between tumor tissue and normal brain tissue as compared to intraoperative MRI. With the success of data-hungry Artificial Intelligence (AI)/Machine Learning (ML) algorithms in advancing the state of the art in medical image analysis, the benefits of sharing well-curated data can not be overstated. To this end, we provide here the largest publicly-available MRI and intraoperative ultrasound imaging database of surgically treated brain tumors, including gliomas (n=92), metastases (n=11), and others (n=11). This collection contains 369 preoperative MRI series, 320 3D intraoperative ultrasound series, 301 intraoperative MRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. We expect this data to be a resource for computational research in brain shift and image analysis as well as for neurosurgical training in the interpretation of intraoperative ultrasound and iMRI.

SUBMITTER: Juvekar P

PROVIDER: S-EPMC10516086 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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ReMIND: The Brain Resection Multimodal Imaging Database.

Juvekar Parikshit P Dorent Reuben R Kögl Fryderyk F Torio Erickson E Barr Colton C Rigolo Laura L Galvin Colin C Jowkar Nick N Kazi Anees A Haouchine Nazim N Cheema Harneet H Navab Nassir N Pieper Steve S Wells William M WM Bi Wenya Linda WL Golby Alexandra A Frisken Sarah S Kapur Tina T

medRxiv : the preprint server for health sciences 20240408

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumor ...[more]

PMID: 37745329

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Project description:Aim. In recent years, connectivity studies using neuroimaging data have increased the understanding of the organization of large-scale structural and functional brain networks. However, data analysis is time consuming as rigorous procedures must be assured, from structuring data and pre-processing to modality specific data procedures. Until now, no single toolbox was able to perform such investigations on truly multimodal image data from beginning to end, including the combination of different connectivity analyses. Thus, we have developed the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox with the goal of diminishing time waste in data processing and to allow an innovative and comprehensive approach to brain connectivity. Materials and Methods. The MIBCA toolbox is a fully automated all-in-one connectivity toolbox that offers pre-processing, connectivity and graph theoretical analyses of multimodal image data such as diffusion-weighted imaging, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). It was developed in MATLAB environment and pipelines well-known neuroimaging softwares such as Freesurfer, SPM, FSL, and Diffusion Toolkit. It further implements routines for the construction of structural, functional and effective or combined connectivity matrices, as well as, routines for the extraction and calculation of imaging and graph-theory metrics, the latter using also functions from the Brain Connectivity Toolbox. Finally, the toolbox performs group statistical analysis and enables data visualization in the form of matrices, 3D brain graphs and connectograms. In this paper the MIBCA toolbox is presented by illustrating its capabilities using multimodal image data from a group of 35 healthy subjects (19-73 years old) with volumetric T1-weighted, diffusion tensor imaging, and resting state fMRI data, and 10 subjets with 18F-Altanserin PET data also. Results. It was observed both a high inter-hemispheric symmetry and an intra-hemispheric modularity associated with structural data, whilst functional data presented lower inter-hemispheric symmetry and a high inter-hemispheric modularity. Furthermore, when testing for differences between two subgroups (<40 and >40 years old adults) we observed a significant reduction in the volume and thickness, and an increase in the mean diffusivity of most of the subcortical/cortical regions. Conclusion. While bridging the gap between the high numbers of packages and tools widely available for the neuroimaging community in one toolbox, MIBCA also offers different possibilities for combining, analysing and visualising data in novel ways, enabling a better understanding of the human brain.

Project description:Background and purposeContrast-enhanced noninvasive angiography and perfusion imaging are recommended to identify eligible patients for endovascular therapy (EVT) in extended time windows (>6 hours or wake-up). If eligible, additional intraarterial contrast exposure will occur during EVT. We aimed to study the renal safety in the DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke) population, selected with contrast-enhanced multimodal Imaging and randomized to EVT versus medical management.MethodsIn the randomized DEFUSE 3 trial population, we compared changes in serum creatinine between baseline (before randomization) and 24 hours later. The primary outcome was the relative change in creatinine level between baseline and 24 hours in the EVT versus medical arm. The secondary outcome was a comparison between computed tomography (CT) versus magnetic resonance imaging selection in the EVT arm. The safety outcome was a comparison of the proportion of patients with criteria for contrast-associated kidney injury in the EVT versus medical arm and a comparison between CT versus magnetic resonance imaging selection in the EVT arm.ResultsIn the DEFUSE 3 population (n=182, age 69±13, 51% female), mean creatinine decreased from a baseline of 0.98±0.33 mg/dL to 0.88±0.28 mg/dL at 24 hours (P<0.001). There was no difference in change between treatment groups: relative to baseline, there was a 6.3% reduction in the EVT group versus 9.2% in the medical group, P=0.294. Absolute decrease -0.08±0.18 in EVT versus -0.12±0.18 in medical, P=0.135; Among patients treated with EVT, there was no difference in 24-hour creatinine level changes between patients who were selected with CT angiography/CT perfusion (-0.08±0.18) versus magnetic resonance imaging (-0.07±0.19), P=0.808 or 6.8% reduction versus 4.8%, P=0.696. In the EVT arm, contrast-associated kidney injury was encountered in 4 out of 91 (4.4%) versus 2/90 (2.2%) in the medical arm P=0.682. In the EVT arm, contrast-associated kidney injury was evenly distributed between magnetic resonance imaging (1/22, 4.6%) versus CT 3 out of 69 (4.4%), P=1.0.ConclusionsPerfusion imaging before EVT was not associated with evidence of decline in renal function. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02586415.

Dataset Information

ReMIND: The Brain Resection Multimodal Imaging Database.

Publications

ReMIND: The Brain Resection Multimodal Imaging Database.

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