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Magnetic Resonance Imaging

Magnetic Resonance Imaging

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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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Deep learning based multi-shot breast diffusion MRI: Improving imaging quality and reduced distortion.

Ning Chien¹, Yi-Hsuan Cho¹, Ming-Yang Wang²

¹Department of Medical Imaging, National Taiwan University Cancer Center, Taipei, Taiwan.

European Journal of Radiology

|September 20, 2025

Summary

Deep-learning reconstruction for multiplexed sensitivity encoding (MUSE DL) significantly improves breast MRI image quality by enhancing signal-to-noise ratio and reducing distortion. This advanced technique maintains diagnostic accuracy and apparent diffusion coefficient values compared to standard single-shot diffusion-weighted imaging.

Keywords:

Apparent diffusion coefficient Breast MRI Deep-learning reconstruction Diffusion Magnetic Resonance Imaging Multiplexed sensitivity encoding (MUSE)

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Single-shot diffusion-weighted imaging (SS-DWI) is a standard technique for breast MRI.
Multiplexed sensitivity encoding (MUSE) aims to improve image quality by acquiring data in multiple shots.
Deep learning (DL) reconstruction offers potential for further enhancement of MRI techniques.

Purpose of the Study:

To compare the imaging performance of deep-learning reconstructed MUSE (MUSE DL) with SS-DWI for breast MRI.
To evaluate image quality metrics including signal-to-noise ratio (SNR) and image distortion.
To assess the impact on apparent diffusion coefficient (ADC) values and diagnostic accuracy.

Main Methods:

Prospective study involving 61 female participants with 65 breast lesions.
Acquisition of both SS-DWI and multi-shot MUSE DWI data on a 3T MRI scanner.
Quantitative analysis of SNR, ADC values, and Hausdorff distance (HD) for distortion assessment.
Subjective qualitative analysis using Likert scale.

Main Results:

MUSE DL significantly improved SNR in fibroglandular tissue compared to non-DL MUSE (2-shot DL: 207.8%, 4-shot DL: 175.1%).
Significantly reduced image distortion was observed with MUSE DL (2-shot: 3.11 mm, 4-shot: 2.58 mm) compared to SS-DWI (4.15 mm).
No significant difference in ADC values was found between MUSE, MUSE DL, and SS-DWI for benign or malignant tumors.

Conclusions:

MUSE DL enhances image quality in breast MRI by improving SNR and minimizing distortion.
The technique preserves the diagnostic accuracy of lesion characterization and ADC values.
MUSE DL represents a promising advancement for breast DWI.