利用深度学习和压缩传感重建三维膝关节磁共振成像:对健康志愿者的验证研究

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Thomas Dratsch, Charlotte Zäske, Florian Siedek, Philip Rauen, Nils Große Hokamp, Kristina Sonnabend, David Maintz, Grischa Bratke, Andra Iuga
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引用次数: 0

摘要

背景研究将压缩传感(CS)和人工智能(AI),特别是深度学习(DL)相结合,加速膝关节三维磁共振成像(MRI)序列的潜力。方法使用 3-T 扫描仪对 20 名健康志愿者进行了检查,采用脂肪饱和三维质子密度序列,有四种不同的加速度(10、13、15 和 17)。所有序列均使用 CS 加速,并使用传统算法和基于 DL 的新算法(CS-AI)进行重建。主观图像质量由两名双盲读者使用 5 分李克特量表中的七项标准进行评估(总体印象、伪影、前交叉韧带、后交叉韧带、半月板、软骨和骨的划分)。使用混合模型,将所有 CS-AI 序列与临床标准(加速因子为 2 的感测序列)和具有相同加速因子的 CS 序列进行比较。结果与具有相同加速因子的 CS 重建序列相比,使用 CS-AI 重建的 3D 序列在主观图像质量方面取得了明显更好的数值(p ≤ 0.001)。使用 CS-AI 重建的图像显示,与参考序列相比,十倍的加速可能是可行的,且不会有明显的质量损失(p ≥ 0.999)。然而,这项研究的局限性在于样本量较小,而且只纳入了健康的志愿者,这表明需要对更多样、更大的样本进行进一步研究.试验注册DRKS00024156.相关性声明使用基于 DL 的算法,膝关节 3-T MRI 的三维序列图像采集时间可能会缩短 54%(178 秒对 384 秒)。要点--压缩传感和 DL 的结合提高了图像质量,使三维膝关节 MRI 的速度显著加快。--基于 DL 的算法比传统压缩传感获得了更好的主观图像质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers

Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers

Background

To investigate the potential of combining compressed sensing (CS) and artificial intelligence (AI), in particular deep learning (DL), for accelerating three-dimensional (3D) magnetic resonance imaging (MRI) sequences of the knee.

Methods

Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence with four different acceleration levels (10, 13, 15, and 17). All sequences were accelerated with CS and reconstructed using the conventional and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using seven criteria on a 5-point-Likert-scale (overall impression, artifacts, delineation of the anterior cruciate ligament, posterior cruciate ligament, menisci, cartilage, and bone). Using mixed models, all CS-AI sequences were compared to the clinical standard (sense sequence with an acceleration factor of 2) and CS sequences with the same acceleration factor.

Results

3D sequences reconstructed with CS-AI achieved significantly better values for subjective image quality compared to sequences reconstructed with CS with the same acceleration factor (p ≤ 0.001). The images reconstructed with CS-AI showed that tenfold acceleration may be feasible without significant loss of quality when compared to the reference sequence (p ≥ 0.999).

Conclusions

For 3-T 3D-MRI of the knee, a DL-based algorithm allowed for additional acceleration of acquisition times compared to the conventional approach. This study, however, is limited by its small sample size and inclusion of only healthy volunteers, indicating the need for further research with a more diverse and larger sample.

Trial registration

DRKS00024156.

Relevance statement

Using a DL-based algorithm, 54% faster image acquisition (178 s versus 384 s) for 3D-sequences may be possible for 3-T MRI of the knee.

Key points

• Combination of compressed sensing and DL improved image quality and allows for significant acceleration of 3D knee MRI.

• DL-based algorithm achieved better subjective image quality than conventional compressed sensing.

• For 3D knee MRI at 3 T, 54% faster image acquisition may be possible.

Graphical Abstract

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来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
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