Novel deep-learning-based diffusion weighted imaging sequence in 1.5 T breast MRI
Novel deep-learning-based diffusion weighted imaging sequence in 1.5 T breast MRI
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DOI：
10.1016/j.ejrad.2023.110948
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发表时间：
2023-07-21
影响因子：
3.3
通讯作者：
Preibsch, Heike
中科院分区：
文献类型：
--
作者：
Wessling, Daniel;Gassenmaier, Sebastian;Preibsch, Heike
目的：本研究旨在评估乳腺磁共振成像（MRI）扩散加权成像（DWI）中基于深度学习的新型重建算法的技术可行性、对图像质量的影响以及采集时间（TA）。方法：对 55 名接受 1.5 T 乳房 DWI 的女性患者进行回顾性分析。使用深度学习 (DL) 重建算法对获得的平均值的子集重建原始数据，从而减少 TA。比较了临床使用的标准 DWI 序列（DWIStd）和 DL 重建图像（DWIDL）。两位放射科医生使用 1 到 5 的李克特量表对 b800 和 ADC 图像的图像质量进行了评分，其中 5 被认为是完美的图像质量。通过将感兴趣区域 (ROI) 放置在两个序列中的相同位置来测量信号强度。结果：与 DWIStd 相比，DWIDL 的 TA 降低了 40%，DWIDL 改善了噪声和清晰度，同时保持了对比度、伪影水平和诊断置信度。与标准成像和 DL 成像相比，表观扩散系数 (ADC)（p = 0.955）、b50 值（p = 0.070）和 b800 值（p = 0.415）的信号强度值没有差异。病变评估显示 ADC 和 DWI 中的病变数量（均 p = 1.000）以及 DWI 中的病变直径（p = 0.961;0.972）和 ADC（p = 0.961;0.972）没有差异。结论：基于深度学习的新型重建算法显着降低了乳腺 DWI 的 TA，同时提高了清晰度，减少了噪声，并保持了相当水平的图像质量、伪影、对比度和诊断置信度。 DWIDL 不影响可量化参数。
Purpose: This study aimed to assess the technical feasibility, the impact on image quality, and the acquisition time (TA) of a new deep-learning-based reconstruction algorithm in diffusion weighted imaging (DWI) of breast magnetic resonance imaging (MRI). Methods: Retrospective analysis of 55 female patients who underwent breast DWI at 1.5 T. Raw data were reconstructed using a deep-learning (DL) reconstruction algorithm on a subset of the acquired averages, therefore a reduction of TA. Clinically used standard DWI sequence (DWIStd) and the DL-reconstructed images (DWIDL) were compared. Two radiologists rated the image quality of b800 and ADC images, using a Likert-scale from 1 to 5 with 5 being considered perfect image quality. Signal intensities were measured by placing a region of interest (ROI) at the same position in both sequences. Results: TA was reduced by 40 % in DWIDL, compared to DWIStd, DWIDL improved noise and sharpness while maintaining contrast, the level of artifacts, and diagnostic confidence. There were no differences regarding the signal intensity values of the apparent diffusion coefficient (ADC), (p = 0.955), b50-values (p = 0.070) and b800values (p = 0.415) comparing standard and DL-imaging. Lesion assessment showed no differences regarding the number of lesions in ADC and DWI (both p = 1.000) and regarding the lesion diameter in DWI (p = 0.961;0.972) and ADC (p = 0.961;0.972). Conclusions: The novel deep-learning-based reconstruction algorithm significantly reduces TA in breast DWI, while improving sharpness, reducing noise, and maintaining a comparable level of image quality, artifacts, contrast, and diagnostic confidence. DWIDL does not influence the quantifiable parameters.