From the Swiss Center for Musculoskeletal Imaging, Balgrist Campus AG, Zurich, Switzerland (A.A.M., C.v.D., S.S., D.N.); Department of Radiology, Balgrist University Hospital, Zurich, Switzerland (A.A.M., G.C.F., S.S.G., R.S.); Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Zurich, Switzerland (C.v.D., S.S.); and Medical Faculty, University of Zurich, Zurich, Switzerland (R.S., D.N.).
Invest Radiol. 2024 Dec 1;59(12):831-837. doi: 10.1097/RLI.0000000000001095. Epub 2024 Jul 4.
The aim of this study was to compare the image quality of 7 T turbo spin echo (TSE) knee images acquired with varying factors of parallel-imaging acceleration reconstructed with deep learning (DL)-based and conventional algorithms.
This was a prospective single-center study. Twenty-three healthy volunteers underwent 7 T knee magnetic resonance imaging. Two-, 3-, and 4-fold accelerated high-resolution fat-signal-suppressing proton density (PD-fs) and T1-weighted coronal 2D TSE acquisitions with an encoded voxel volume of 0.31 × 0.31 × 1.5 mm 3 were acquired. Each set of raw data was reconstructed with a DL-based and a conventional Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) algorithm. Three readers rated image contrast, sharpness, artifacts, noise, and overall quality. Friedman analysis of variance and the Wilcoxon signed rank test were used for comparison of image quality criteria.
The mean age of the participants was 32.0 ± 8.1 years (15 male, 8 female). Acquisition times at 4-fold acceleration were 4 minutes 15 seconds (PD-fs, Supplemental Video is available at http://links.lww.com/RLI/A938 ) and 3 minutes 9 seconds (T1, Supplemental Video available at http://links.lww.com/RLI/A939 ). At 4-fold acceleration, image contrast, sharpness, noise, and overall quality of images reconstructed with the DL-based algorithm were significantly better rated than the corresponding GRAPPA reconstructions ( P < 0.001). Four-fold accelerated DL-reconstructed images scored significantly better than 2- to 3-fold GRAPPA-reconstructed images with regards to image contrast, sharpness, noise, and overall quality ( P ≤ 0.031). Image contrast of PD-fs images at 2-fold acceleration ( P = 0.087), image noise of T1-weighted images at 2-fold acceleration ( P = 0.180), and image artifacts for both sequences at 2- and 3-fold acceleration ( P ≥ 0.102) of GRAPPA reconstructions were not rated differently than those of 4-fold accelerated DL-reconstructed images. Furthermore, no significant difference was observed for all image quality measures among 2-fold, 3-fold, and 4-fold accelerated DL reconstructions ( P ≥ 0.082).
This study explored the technical potential of DL-based image reconstruction in accelerated 2D TSE acquisitions of the knee at 7 T. DL reconstruction significantly improved a variety of image quality measures of high-resolution TSE images acquired with a 4-fold parallel-imaging acceleration compared with a conventional reconstruction algorithm.
本研究旨在比较不同并行成像加速因子下,基于深度学习(DL)和传统算法重建的 7T 涡轮自旋回波(TSE)膝关节图像的图像质量。
这是一项前瞻性单中心研究。23 名健康志愿者接受了 7T 膝关节磁共振成像检查。采集了二维高分辨率脂肪抑制质子密度(PD-fs)和 T1 加权冠状位 2D TSE 图像,并行加速因子分别为 2 倍、3 倍和 4 倍,编码体素体积为 0.31×0.31×1.5mm3。每组原始数据均采用基于 DL 的和传统的广义自校准部分并行采集(GRAPPA)算法进行重建。三位读者对图像对比度、锐度、伪影、噪声和整体质量进行评分。采用 Friedman 方差分析和 Wilcoxon 符号秩检验比较图像质量标准。
参与者的平均年龄为 32.0±8.1 岁(15 名男性,8 名女性)。4 倍加速采集时间分别为 4 分 15 秒(PD-fs,补充视频可在 http://links.lww.com/RLI/A938 获得)和 3 分 9 秒(T1,补充视频可在 http://links.lww.com/RLI/A939 获得)。在 4 倍加速时,基于 DL 的算法重建的图像对比度、锐度、噪声和整体质量评分明显优于相应的 GRAPPA 重建(P<0.001)。与 2 倍至 3 倍 GRAPPA 重建图像相比,4 倍加速的 DL 重建图像在图像对比度、锐度、噪声和整体质量方面评分显著更高(P≤0.031)。2 倍加速的 PD-fs 图像的图像对比度(P=0.087)、2 倍加速的 T1 加权图像的图像噪声(P=0.180)以及 2 倍和 3 倍加速的两种序列的图像伪影(P≥0.102)的 GRAPPA 重建评分与 4 倍加速的 DL 重建图像无显著差异。此外,在 2 倍、3 倍和 4 倍加速的 DL 重建中,所有图像质量测量值之间均无显著差异(P≥0.082)。
本研究探讨了基于 DL 的图像重建在 7T 膝关节 2D TSE 加速采集中的技术潜力。与传统重建算法相比,DL 重建显著提高了高分辨率 TSE 图像在 4 倍并行成像加速下的多种图像质量指标。