Oh Gaeun, Paik Seungyoon, Jo Sang Won, Choi Hye Jeong, Yoo Roh-Eul, Choi Seung Hong
Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Kangdong Sacred Heart Hospital, Seoul, Republic of Korea.
Eur Radiol. 2025 Aug 8. doi: 10.1007/s00330-025-11920-7.
To evaluate the utility of a deep learning (DL)-based image enhancement for improving the image quality and diagnostic performance of 3D contrast-enhanced T1-weighted black blood (BB) MR imaging for brain metastases.
This retrospective study included 126 patients with and 121 patients without brain metastasis who underwent 3-T MRI examinations. Commercially available DL-based MR image enhancement software was utilized for image post-processing. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of enhancing lesions were measured. For qualitative assessment and diagnostic performance evaluation, two radiologists graded the overall image quality, noise, and artifacts of each image and the conspicuity of visible lesions. The Wilcoxon signed-rank test and regression analyses with generalized estimating equations (GEEs) were used for statistical analysis.
For MR images that were not previously processed using other DL-based methods, SNR and CNR were higher in the DL-enhanced images than in the standard images (438.3 vs. 661.1, p < 0.01; 173.9 vs. 223.5, p < 0.01). Overall image quality and noise were improved in the DL images (p < 0.01, average score-5 proportion 38% vs. 65%; p < 0.01, 43% vs. 74%), whereas artifacts did not significantly differ (p ≥ 0.07). Sensitivity was increased after post-processing from 79 to 86% (p = 0.02), especially for lesions smaller than 5 mm (69 to 78%, p = 0.03), and changes in specificity (p = 0.24) and average false-positive (FP) count (p = 0.18) were not significant.
DL image enhancement improves the image quality and diagnostic performance of 3D contrast-enhanced T1-weighted BB MR imaging for the detection of small brain metastases.
Question Can deep learning (DL)-based image enhancement improve the image quality and diagnostic performance of 3D contrast-enhanced T1-weighted black blood (BB) MR imaging for brain metastases? Findings DL-based image enhancement improved image quality of thin slice BB MR images and sensitivity for brain metastasis, particularly for lesions smaller than 5 mm. Clinical relevance DL-based image enhancement on BB images may assist in the accurate diagnosis of brain metastasis by achieving better sensitivity while maintaining comparable specificity.
评估基于深度学习（DL）的图像增强技术对提高三维对比增强T1加权黑血（BB）磁共振成像检测脑转移瘤的图像质量和诊断性能的效用。
这项回顾性研究纳入了126例有脑转移瘤的患者和121例无脑转移瘤的患者，他们均接受了3-T磁共振成像检查。使用市售的基于DL的磁共振图像增强软件进行图像后处理。测量强化病灶的信噪比（SNR）和对比噪声比（CNR）。为了进行定性评估和诊断性能评估，两名放射科医生对每张图像的整体图像质量、噪声、伪影以及可见病灶的清晰度进行评分。采用Wilcoxon符号秩检验和广义估计方程（GEE）进行回归分析。
对于之前未使用其他基于DL的方法进行处理的磁共振图像，DL增强图像的SNR和CNR高于标准图像（438.3对661.1，p<0.01；173.9对223.5，p<0.01）。DL图像的整体图像质量和噪声得到改善（p<0.01，平均评分-5比例38%对65%；p<0.01，43%对74%），而伪影无显著差异（p≥0.07）。后处理后敏感性从79%提高到86%（p=0.02），尤其是对于小于5mm的病灶（69%至78%，p=0.03），特异性（p=0.24）和平均假阳性（FP）计数（p=0.18）的变化不显著。
DL图像增强可提高三维对比增强T1加权BB磁共振成像检测小脑转移瘤的图像质量和诊断性能。
问题基于深度学习（DL）的图像增强能否提高三维对比增强T1加权黑血（BB）磁共振成像检测脑转移瘤的图像质量和诊断性能？发现基于DL的图像增强改善了薄层BB磁共振图像的质量和对脑转移瘤的敏感性，尤其是对于小于5mm的病灶。临床意义基于DL的BB图像增强可在保持可比特异性的同时提高敏感性，有助于脑转移瘤的准确诊断。
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