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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: Oct 18, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Fast data-driven learning of parallel MRI sampling patterns for large scale problems.

Marcelo V W Zibetti¹, Gabor T Herman^2,3, Ravinder R Regatte²

¹Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA. Marcelo.WustZibetti@nyulangone.org.

Scientific Reports

|September 30, 2021

Summary

This summary is machine-generated.

A new method called bias-accelerated subset selection (BASS) quickly finds optimal sampling patterns for faster MRI scans. This approach significantly improves image reconstruction quality and can reduce scan times by up to 50%.

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Area of Science:

Medical Imaging
Magnetic Resonance Imaging (MRI)
Computational Science

Background:

Parallel MRI accelerates image acquisition but requires efficient sampling patterns (SPs) for high-quality reconstruction.
Current methods for optimizing SPs can be computationally intensive and slow.

Purpose of the Study:

To introduce a fast, data-driven optimization approach, bias-accelerated subset selection (BASS), for learning efficacious SPs.
To reduce scan time in large-dimensional parallel MRI while maintaining or improving reconstruction quality.

Main Methods:

BASS utilizes fully-sampled k-space data and specified reconstruction methods to define SP efficacy.
It iteratively generates SPs to find one of a target size with near-optimal efficacy.
Tested with five reconstruction methods (low-rankness, sparsity) and three datasets (brain, knee).

Main Results:

BASS achieved SPs up to 50 times faster than greedy approaches.
Reconstruction quality improved by up to 45% compared to variable density and Poisson disk SPs.
Scan time could be nearly halved without compromising reconstruction quality.

Conclusions:

BASS is a computationally efficient and fast-converging method for learning effective SPs in parallel MRI.
It enables better selection of sampling-reconstruction pairs for diverse MRI applications.
Demonstrates significant improvements in quantitative and prospective accelerated MRI.