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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Jun 26, 2025

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

ReMIND: The Brain Resection Multimodal Imaging Database.

Parikshit Juvekar¹, Reuben Dorent¹, Fryderyk Kögl^1,2

¹Brigham and Women's Hospital, Harvard Medical School, Boston, USA.

Scientific Data

|May 14, 2024

Summary

This summary is machine-generated.

This study introduces the largest public database of brain tumor imaging, combining MRI and intraoperative ultrasound (iUS). This resource aims to improve AI-driven analysis for neurosurgery, addressing challenges like brain shift and tissue differentiation.

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Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Area of Science:

Neurosurgery
Medical Imaging
Artificial Intelligence

Background:

Maximal safe surgical resection is standard for brain tumors, aided by neuronavigation.
Brain shift and poor tissue contrast (gliomas vs. healthy tissue) challenge surgical accuracy.
Intraoperative MRI (iMRI) and ultrasound (iUS) aid visualization, with iUS offering faster integration but lower contrast than iMRI.

Purpose of the Study:

To establish the largest publicly available database of intraoperative imaging for surgically treated brain tumors.
To support research in brain shift correction and AI-based medical image analysis.
To facilitate neurosurgical training in interpreting iUS and iMRI.

Main Methods:

Compiled a database from 114 consecutive patients at a single institution.
Included preoperative MRI, 3D intraoperative ultrasound (iUS), and intraoperative MRI (iMRI) series.
Collected 356 segmentations alongside 369 preoperative MRI, 320 iUS, and 301 iMRI series.

Main Results:

The database comprises data from 114 patients, including 92 gliomas, 11 metastases, and 11 other tumor types.
Contains a significant volume of multimodal imaging data: 369 preoperative MRI, 320 3D iUS, and 301 iMRI series.
Includes 356 corresponding segmentations for research and training.

Conclusions:

The presented database is the largest public resource for brain tumor intraoperative imaging.
It is expected to significantly advance AI research for brain shift and image analysis.
The database will be invaluable for neurosurgical training and improving surgical outcomes.