MR Clinical Solutions and Research Collaborations, GE Healthcare, Houston, TX, USA.
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, 1515 Holcombe Blvd., Houston, TX, USA.
Abdom Radiol (NY). 2021 Jul;46(7):3378-3386. doi: 10.1007/s00261-021-02964-6. Epub 2021 Feb 12.
Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, "DLR" in improving image quality and mitigating artifacts, which is now commercially available as AIR Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts.
This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERC, ERC, Non-ERC, and Non-ERC. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor.
The Non-ERC scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor.
Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.
磁共振成像（MRI）在评估前列腺癌患者方面发挥了越来越重要的作用，尽管前列腺 MRI 存在一些技术挑战。新的技术，如深度学习（DL），已经应用于医学成像，从而提高了图像质量。我们的目标是评估一种新的基于深度学习的重建方法“DLR”在提高图像质量和减轻伪影方面的性能，该方法现在已作为商业产品 AIR Recon DL（GE Healthcare，Waukesha，WI）提供。我们假设将 DLR 应用于前列腺的 T2WI 图像可以提供更好的图像质量并减少伪影。
本研究共纳入 31 例有前列腺癌病史的患者，他们接受了 1.5T 或 3.0T 直肠内线圈（ERC）的前列腺多参数 MRI。总共生成了四组 T2 加权图像：一组 ERC 信号开启（ERC），另一组 ERC 信号关闭（Non-ERC）。然后，使用两种不同的重建方法分别对每组图像进行重建：常规重建（Conv）和深度学习重建（DLR）：ERC、ERC、Non-ERC 和 Non-ERC。三位放射科医生独立对四组图像进行了（i）图像质量、（ii）伪影和（iii）解剖标志和肿瘤可视化的评估和评分。
非 ERC 组在（i）整体图像质量（p<0.001）、（ii）减少伪影（p<0.001）和（iii）解剖标志和肿瘤可视化方面的评分最高。
无直肠内线圈的前列腺成像可能受益于深度学习重建，这在前列腺 T2 加权成像 MRI 评估中得到了证实。
