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Related Experiment Video

Updated: Oct 4, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

Generative adversarial network-based post-processed image super-resolution technology for accelerating brain MRI:

Wataru Ueki¹, Tatsuya Nishii¹, Kensuke Umehara^2,3,4

¹Department of Radiology, 13875National Cerebral and Cardiovascular Center, Suita, Osaka, Japan.

Acta Radiologica (Stockholm, Sweden : 1987)

|February 4, 2022

Summary

This summary is machine-generated.

Deep-learning super-resolution (SR) shows superior image similarity compared to compressed sensing (CS) for accelerating 2D brain MRI. However, CS offers advantages in 3D brain MRI acceleration.

Keywords:

Brain acceleration deep learning magnetic resonance imaging super resolution

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Area of Science:

Medical Imaging
Artificial Intelligence in Radiology
Magnetic Resonance Imaging Techniques

Background:

The potential of deep-learning super-resolution (SR) and compressed sensing (CS) to accelerate magnetic resonance imaging (MRI) remains unclear.
Evaluating advanced acceleration techniques is crucial for improving MRI efficiency and patient throughput.

Purpose of the Study:

To compare the image similarity of SR-accelerated MRI with CS-accelerated MRI.
To assess the performance of these acceleration techniques against reference 2D and 3D gradient-echo sequence (GRE) brain MRI scans.

Main Methods:

Prospective acquisition of 1.3× and 2.0× accelerated 2D and 3D GRE brain MRI in 20 volunteers.
Utilized generative adversarial network (GAN) for SR image reconstruction and varied CS factors for acceleration.
Quantified image similarity using structural similarity (SSIM) index and subjective image similarity (ISM) index assessed by a radiologist.

Main Results:

SR demonstrated significantly higher SSIM than CS for MRI acceleration.
In 2D GRE, SR images were harder to distinguish from reference images compared to CS images.
In 3D GRE, CS showed a higher ISM index than SR for 2.0× accelerated images, indicating better preservation of image quality.

Conclusions:

Generative adversarial network-based SR outperforms CS in terms of image similarity for 2D GRE MRI acceleration.
Compressed sensing (CS) is more advantageous than SR for 3D GRE MRI acceleration, offering better image quality preservation.
The choice between SR and CS for MRI acceleration depends on the specific imaging sequence (2D vs. 3D GRE) and desired balance between speed and image fidelity.