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深度学习增强的1.5T乳腺MRI T1加权成像

Deep Learning-Enhanced T1-Weighted Imaging for Breast MRI at 1.5T.

作者信息

Olthof Susann-Cathrin, Nickel Marcel Dominik, Weiland Elisabeth, Leyhr Daniel, Afat Saif, Nikolaou Konstantin, Preibsch Heike

机构信息

Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany.

Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, 91052 Erlangen, Germany.

出版信息

Diagnostics (Basel). 2025 Jul 1;15(13):1681. doi: 10.3390/diagnostics15131681.

DOI:10.3390/diagnostics15131681
PMID:40647680
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12248570/
Abstract

: Assessment of a novel deep-learning (DL)-based T1w volumetric interpolated breath-hold (VIBE) sequence in breast MRI in comparison with standard VIBE (VIBE) for image quality evaluation. : Prospective study of 52 breast cancer patients examined at 1.5T breast MRI with T1w VIBE and T1 VIBE sequence. T1w VIBE was integrated as an additional early non-contrast and a delayed post-contrast scan. Two radiologists independently scored T1w VIBE sequences both pre- and post-contrast and their calculated subtractions (SUBs) for image quality, sharpness, (motion)-artifacts, perceived signal-to-noise and diagnostic confidence with a Likert-scale from 1: Non-diagnostic to 5: Excellent. Lesion diameter was evaluated on the SUB for T1w VIBE/. All lesions were visually evaluated in T1w VIBE/ pre- and post-contrast and their subtractions. Statistics included correlation analyses and paired t-tests. : Significantly higher Likert scale values were detected in the pre-contrast T1w VIBE compared to the T1w VIBE for image quality (each < 0.001), image sharpness ( < 0.001), SNR ( < 0.001), and diagnostic confidence ( < 0.010). Significantly higher values for image quality ( < 0.001 in each case), image sharpness ( < 0.001), SNR ( < 0.001), and artifacts ( < 0.001) were detected in the post-contrast T1w VIBE and in the SUB. SUB provided superior diagnostic certainty compared to SUB in one reader ( = 0.083 or = 0.004). : Deep learning-enhanced T1w VIBE at 1.5T breast MRI offers superior image quality compared to T1w VIBE.

摘要

在乳腺MRI中，评估基于深度学习（DL）的新型T1加权容积内插屏气（VIBE）序列与标准VIBE序列相比的图像质量。：对52例乳腺癌患者进行前瞻性研究，在1.5T乳腺MRI上采用T1加权VIBE序列和T1 VIBE序列进行检查。T1加权VIBE序列作为额外的早期非增强和延迟增强扫描。两名放射科医生独立对T1加权VIBE序列在增强前和增强后的图像质量、清晰度、（运动）伪影、感知信噪比和诊断信心进行评分，采用李克特量表，从1：非诊断到5：优秀。在T1加权VIBE序列的减影图像上评估病变直径。所有病变在T1加权VIBE序列增强前和增强后的图像及其减影图像上进行视觉评估。统计分析包括相关性分析和配对t检验。：与T-weighted VIBE序列相比，在增强前的T1加权VIBE序列中，图像质量（均P<0.001）、图像清晰度（P<0.001）、信噪比（P<0.001）和诊断信心（P<0.010）的李克特量表值显著更高。在增强后的T1加权VIBE序列和减影图像中，图像质量（每种情况P<0.001）、图像清晰度（P<0.001）、信噪比（P<0.001）和伪影（P<0.001）的值显著更高。在一名读者中，减影图像提供了比另一种减影图像更高的诊断确定性（P=0.083或P=0.004）。：在1.5T乳腺MRI中，深度学习增强的T1加权VIBE序列与T1加权VIBE序列相比具有更高的图像质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fe1/12248570/4992b0ad6e0b/diagnostics-15-01681-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fe1/12248570/0e4465d0fc23/diagnostics-15-01681-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fe1/12248570/578301944235/diagnostics-15-01681-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fe1/12248570/749753f25b63/diagnostics-15-01681-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fe1/12248570/4992b0ad6e0b/diagnostics-15-01681-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fe1/12248570/0e4465d0fc23/diagnostics-15-01681-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fe1/12248570/578301944235/diagnostics-15-01681-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fe1/12248570/749753f25b63/diagnostics-15-01681-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fe1/12248570/4992b0ad6e0b/diagnostics-15-01681-g004.jpg

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本文引用的文献

[1]
Accelerated High-resolution T1- and T2-weighted Breast MRI with Deep Learning Super-resolution Reconstruction.

Acad Radiol. 2025-6

[2]
Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis.

Breast Cancer Res. 2024-9-20

[3]
Prospective Deployment of Deep Learning Reconstruction Facilitates Highly Accelerated Upper Abdominal MRI.

Acad Radiol. 2024-12

[4]
Enhancing gadoxetic acid-enhanced liver MRI: a synergistic approach with deep learning CAIPIRINHA-VIBE and optimized fat suppression techniques.

Eur Radiol. 2024-10

[5]
Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images.

Diagnostics (Basel). 2023-11-14

[6]
Deep learning enabled fast 3D brain MRI at 0.055 tesla.

Sci Adv. 2023-9-22

[7]
Novel deep-learning-based diffusion weighted imaging sequence in 1.5 T breast MRI.

Eur J Radiol. 2023-9

[8]
Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.

J Cancer Res Clin Oncol. 2023-9

[9]
Clinical Impact of Deep Learning Reconstruction in MRI.

Radiographics. 2023-6

[10]
Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction.

Cancers (Basel). 2023-1-18

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