Improved Breast Cancer Characterization with High-Resolution 3D T2WI-SPACE at Equivalent Scan Times to Conventional 2D FSE-T2WI

Motivation: There are no reports comparing 3D T2WI optimized through phantom studies with 2D T2WI for breast MRI. Goal(s): To optimize 3D T2WI in a phantom study and compare the image quality of the optimized 3D T2WI with 2D T2WI in clinical breast MRI. Approach: A Phantom was scanned for the optimization of 3D T2WI as for contrast ratio and resolution. This IRB-approved retrospective study demonstrated the quantitative and qualitative analysis between optimized 3D T2WI and 2D T2WI. Results: The optimized 3D T2WI with variable refocusing flip angle demonstrated excellent conspicuity with higher resolution compared to 2D T2WI, while maintaining the same scanning time. Impact: Our findings strongly support the feasibility of high-resolution 3D T2WI for implementation in clinical settings, offering valuable information to contrast-enhanced MRI for breast cancer characterization. This technique would show promise for improving lesion differentiation, prognosis prediction, and treatment efficacy assessment.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1994
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improved Breast Cancer Characterization with High-Resolution 3D T2WI-SPACE at Equivalent Scan Times to Conventional 2D FSE-T2WI

Yutaka Kato, Shinji Naganawa, Satoko Ishigaki, Mami Iima et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

Improved Breast Cancer Characterization with High-Resolution 3D T2WI-SPACE at Equivalent Scan Times to Conventional 2D FSE-T2WI

Yutaka Kato, Shinji Naganawa, Satoko Ishigaki, Mami Iima, Hiroko Satake, Kazushige Ichikawa
article en

Abstract

Motivation: There are no reports comparing 3D T2WI optimized through phantom studies with 2D T2WI for breast MRI. Goal(s): To optimize 3D T2WI in a phantom study and compare the image quality of the optimized 3D T2WI with 2D T2WI in clinical breast MRI. Approach: A Phantom was scanned for the optimization of 3D T2WI as for contrast ratio and resolution. This IRB-approved retrospective study demonstrated the quantitative and qualitative analysis between optimized 3D T2WI and 2D T2WI. Results: The optimized 3D T2WI with variable refocusing flip angle demonstrated excellent conspicuity with higher resolution compared to 2D T2WI, while maintaining the same scanning time. Impact: Our findings strongly support the feasibility of high-resolution 3D T2WI for implementation in clinical settings, offering valuable information to contrast-enhanced MRI for breast cancer characterization. This technique would show promise for improving lesion differentiation, prognosis prediction, and treatment efficacy assessment.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 63%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.