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Related Experiment Video

Updated: May 26, 2025

Intraoperative Ultrasound in Spinal Surgery

Intraoperative Ultrasound in Spinal Surgery

Published on: August 17, 2022

Deep learning-based Intraoperative MRI reconstruction.

Jon André Ottesen^1,2,3, Tryggve Storas⁴, Svein Are Sirirud Vatnehol^5,6,7

¹Computational Radiology & Artificial Intelligence (CRAI) Research Group, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway. jonakri@uio.no.

European Radiology Experimental

|February 25, 2025

Summary

This summary is machine-generated.

Deep learning (DL) shows promise for high-quality intraoperative MRI (iMRI) reconstructions during brain tumor surgery. While neuroradiologists favored DL, a neurosurgeon preferred conventional methods, indicating a need for further optimization.

Keywords:

Artifacts Brain neoplasms Deep learning Magnetic resonance imaging Neurosurgical procedures

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Last Updated: May 26, 2025

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

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Published on: August 12, 2019

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Neurosurgery

Background:

Intraoperative MRI (iMRI) is crucial for brain tumor surgery.
Accelerated iMRI protocols are used to reduce scan times.
Assessing the quality of deep learning (DL) reconstructions for accelerated iMRI is essential.

Purpose of the Study:

To evaluate the quality of DL reconstructions for accelerated intraoperative MRI (iMRI) during brain tumor resection.
To compare DL reconstructions against conventional compressed sensing (CS) methods.
To determine the diagnostic utility and preferred reconstruction method by clinicians.

Main Methods:

Retrospective analysis of accelerated iMRI data from 40 brain tumor surgery patients.
A DL model trained on the fastMRI neuro dataset was used for reconstruction.
Comparative evaluation of DL versus CS reconstructions by two neuroradiologists and one neurosurgeon using a Likert scale.

Main Results:

DL reconstructions were favored by neuroradiologists in a majority of cases (33/40 and 39/40).
DL reconstructions achieved higher scores for most image quality metrics according to neuroradiologists (72% of cases).
DL reconstructions showed striping artifacts and reduced signal; neurosurgeon preferred CS reconstructions.

Conclusions:

Deep learning demonstrates potential for high-quality iMRI reconstructions, improving perceived image quality for neuroradiologists.
Despite promise, DL reconstructions require further optimization due to artifacts and the challenging intraoperative environment.
Clinical preference varied, with neuroradiologists favoring DL and neurosurgeons favoring CS, highlighting the need for tailored solutions.