The Cancer Imaging Archive

ReMIND | The Brain Resection Multimodal Imaging Database

DOI: 10.7937/3RAG-D070 | Data Citation Required | 1.7k Views | Image Collection

Summary

Introduction:

The Brain Resection Multimodal Imaging Database (ReMIND) contains pre- and intra-operative data collected on 114 consecutive patients who were surgically treated with image-guided tumor resection between 2018 and 2022. For all patients, preoperative MRI, 3D intraoperative ultrasound series, and intraoperative MRI are available. Additionally, each case typically contains segmentations, including the preoperative tumor, the pre-resection cerebrum, the previous resection cavity derived from the preoperative MRI (if applicable), and any residual tumor identified on the intraoperative MRI. In total, this collection contains 369 preoperative MRI series, 320 3D intraoperative ultrasound series, 301 intraoperative MRI series, and 356 segmentations. 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 intraoperative MRI.

Imaging data:

Preoperative MRI comprises four structural MRI sequences: native T1-weighted (T1), contrast-enhanced T1-weighted (ceT1), native T2-weighted (T2), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR). These scans were acquired before surgery using various scanners at multiple institutions, making their acquisition parameters heterogeneous. Unlike preoperative MRI, all intraoperative MRI were acquired using a 3T wide-bore (70 cm) MRI scanner. All iUS series were acquired using a tracked 2D neuro-cranial curvilinear transducer. The transducer was swept unidirectionally through the craniotomy at a slow, consistent speed. This specific motion, in conjunction with the tracking, enabled the reconstruction of a 3D volume from the tracked 2D sweeps.

Segmentation data:

Various segmentations were created to assist the surgical resection. These include manual segmentations of the preoperative whole tumor, preoperative tumor target (i.e., the radiologically identifiable tumor specifically targeted for resection), resection cavity resulting from prior surgery (i.e., in case of reoperation), intraoperative residual tumor, and the automatic segmentations of cerebrum and ventricles (Brainlab AG, Munich, Germany). Only structures deemed necessary for the surgical resection by the attending neurosurgeon were segmented. Specifically, segmentations of the manual preoperative whole tumor (113 cases), preoperative tumor target segmentations (3 cases), manual previous resection cavity segmentations (21 cases), residual tumor segmentations (58 cases), and automated segmentations of the cerebrum (89 cases) and ventricles (54 cases). All cerebrum, ventricle, and tumor segmentations were created preoperatively during the surgical planning stage. In contrast, residual tumor segmentations were created intraoperatively from iMRI.

Clinical metadata:

Demographic information, including age, sex, and ethnicity, was obtained from the corresponding patient medical records. The age range of the included population was 20–76. The ratio of male:female was equal to 61:53. Moreover, clinico-pathologic data such as the tumor type, tumor grade, radiological characteristics upon contrast administration, tumor location, and the reoperation status were assessed by the treating neurosurgeons. Tumor type and grade were specified according to the World Health Organization (WHO) 2021 Classification of Tumors of the Central Nervous System. Additionally, tumors were classified into one of 3 categories based on proximity to the functional cortex (non-eloquent, near eloquent, and eloquent). 

Pre-processing:

The MRI and ultrasound images are provided in DICOM format. Segmentation files are provided in NRRD format (original format) and DICOM SEG (converted from NRRD). Data was converted from NRRD format to DICOM format using 3D Slicer (MR data), dicom3tools software (iUS data) and dcmqi (segmentation data). All MRI images were defaced using automatic affine registration or manual landmark registration with NiftyReg and the template and face mask provided in pydeface. The code of the algorithm is publicly available. 

Data Access

Version 1: Updated 2023/09/26

Additional Resources for this Dataset

The following external resources have been made available by the data submitters.  These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.

• ReMIND2Reg dataset – The goal of the ReMIND2Reg dataset is to address the registration problem of multi-parametric pre-operative MRI and intra-operative 3D ultrasound (iUS) images. Specifically, we focus on the challenging problem of pre-operative to post-resection registration, requiring the estimation of large deformations and tissue resections.

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.

• Imaging Data Commons (IDC) (Imaging Data)

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier: