Faster Acquisition and Improved Image Quality of T2-Weighted Dixon Breast MRI at 3T Using Deep Learning: A Prospective Study.

IF 4.4 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Korean Journal of Radiology Pub Date : 2025-01-01 DOI:10.3348/kjr.2023.1303
Caroline Wilpert, Hannah Schneider, Alexander Rau, Maximilian Frederic Russe, Benedict Oerther, Ralph Strecker, Marcel Dominic Nickel, Elisabeth Weiland, Alexa Haeger, Matthias Benndorf, Thomas Mayrhofer, Jakob Weiss, Fabian Bamberg, Marisa Windfuhr-Blum, Jakob Neubauer
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Abstract

Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2DL) and a conventional T2-w FSE Dixon sequence (T2STD) for breast magnetic resonance imaging (MRI).

Materials and methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner. Both T2DL and T2STD sequences were acquired for each patient. Quantitative analysis was based on region-of-interest (ROI) measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed independently by two radiologists using Likert scales to evaluate various image quality features, morphology, and diagnostic confidence for cysts and breast cancers. Reader preference between T2DL and T2STD was assessed via side-by-side comparison, and inter-reader reliability was also analyzed.

Results: Total of 151 women were enrolled, with 140 women (mean age: 52 ± 14 years; 85 cysts and 31 breast cancers) included in the final analysis. The acquisition time was 110 s ± 0 for T2DL compared to 266 s ± 0 for T2STD. SNR and CNR were significantly higher in T2DL (P < 0.001). T2DL was associated with higher image quality scores, reduced noise, and fewer artifacts (P < 0.001). All evaluated anatomical regions (breast and axilla), breast implants, and bone margins were rated higher in T2DL (P ≤ 0.008), except for bone marrow, which scored higher in T2STD (P < 0.001). Scores for conspicuity, sharpness/margins, and microstructure of cysts and breast cancers were higher in T2DL (P ≤ 0.002). Diagnostic confidence for cysts was improved with T2DL (P < 0.001). Readers significantly preferred T2DL over T2STD in side-by-side comparisons (P < 0.001).

Conclusion: T2DL effectively corrected for SNR loss caused by accelerated image acquisition and provided a 58% reduction in acquisition time compared to T2STD. This led to fewer artifacts and improved overall image quality. Thus, T2DL is feasible and has the potential to replace conventional T2-w sequences for breast MRI examinations.

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基于深度学习的3T t2加权Dixon乳腺MRI更快的采集和更好的图像质量:一项前瞻性研究
目的:本研究的目的是比较快速深度学习(DL)重建t2 -加权(T2-w)超分辨率快速自旋回波(FSE) Dixon序列(T2DL)和传统T2-w FSE Dixon序列(T2STD)用于乳房磁共振成像(MRI)的图像质量特征和病变特征。材料和方法:本前瞻性研究于2022年11月至2023年4月期间使用3T扫描仪进行。获得每位患者的T2DL和T2STD序列。定量分析基于感兴趣区域(ROI)测量的信噪比(SNR)和噪声对比比(CNR)。定性分析由两名放射科医生独立进行,使用李克特量表评估囊肿和乳腺癌的各种图像质量特征、形态学和诊断置信度。通过并排比较评估T2DL和T2STD之间的读者偏好,并分析读者间信度。结果:共入组151例女性,其中140例女性(平均年龄:52±14岁;85个囊肿和31个乳腺癌)纳入最终分析。T2DL采集时间为110 s±0,T2STD为266 s±0。T2DL患者的SNR、CNR均显著增高(P < 0.001)。T2DL与更高的图像质量评分、更低的噪声和更少的伪影相关(P < 0.001)。所有被评估的解剖区域(乳房和腋窝)、乳房植入物和骨缘在T2DL中的评分都较高(P≤0.008),除了骨髓在T2STD中的评分较高(P < 0.001)。T2DL患者囊肿和乳腺癌的显著性、锐度/边缘、显微结构评分较高(P≤0.002)。T2DL患者对囊肿的诊断可信度提高(P < 0.001)。在并排比较中,读者明显倾向于T2DL而不是T2STD (P < 0.001)。结论:T2DL有效地纠正了加速图像采集造成的信噪比损失,与T2STD相比,采集时间减少了58%。这减少了伪影,提高了整体图像质量。因此,T2DL是可行的,有可能取代传统的T2-w序列进行乳腺MRI检查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Korean Journal of Radiology
Korean Journal of Radiology 医学-核医学
CiteScore
10.60
自引率
12.50%
发文量
141
审稿时长
1.3 months
期刊介绍: The inaugural issue of the Korean J Radiol came out in March 2000. Our journal aims to produce and propagate knowledge on radiologic imaging and related sciences. A unique feature of the articles published in the Journal will be their reflection of global trends in radiology combined with an East-Asian perspective. Geographic differences in disease prevalence will be reflected in the contents of papers, and this will serve to enrich our body of knowledge. World''s outstanding radiologists from many countries are serving as editorial board of our journal.
期刊最新文献
Clinical Efficacy of Ultrafast Dynamic Contrast-Enhanced MRI Using Compressed Sensing in Distinguishing Benign and Malignant Soft-Tissue Tumors. Establishment of Local Diagnostic Reference Levels for Pediatric Neck CT at Nine University Hospitals in South Korea. Faster Acquisition and Improved Image Quality of T2-Weighted Dixon Breast MRI at 3T Using Deep Learning: A Prospective Study. Letter to the Editor "Ten-Year Outcomes of Radiofrequency Ablation for Locally Recurrent Papillary Thyroid Cancer". Prospective Evaluation of Accelerated Brain MRI Using Deep Learning-Based Reconstruction: Simultaneous Application to 2D Spin-Echo and 3D Gradient-Echo Sequences.
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