Cochran Rory L, Milshteyn Eugene, Ghosh Soumyadeep, Nakrour Nabih, Mercaldo Nathaniel D, Guidon Arnaud, Harisinghani Mukesh G
Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.
GE HealthCare, Boston, MA, United States.
Clin Imaging. 2025 Jan;117:110341. doi: 10.1016/j.clinimag.2024.110341. Epub 2024 Nov 5.
To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.
This was a retrospective study of 52 patients undergoing conventional prostate MRI with raw image data reconstructed using both conventional 2D Cartesian and DLR algorithms. Simulated shortened DWI acquisition times were performed by reconstructing images using DLR datasets harboring a reduced number of excitations (NEX). Two radiologists independently evaluated the image quality using a 4-point Likert scale. Signal-to-noise ratio (SNR) analysis was performed for the entire cohort and a subset of patients identified as having clinically significant prostate cancer identified at MRI, and later confirmed by pathology.
Radiologists perceived less image noise for both restricted and large field of view (FOV) standard NEX dataset DLR diffusion images compared to conventionally reconstructed images with good interreader agreement. Diagnostic image quality was maintained for all DLR images using variably reduced NEX compared to conventionally reconstructed images employing the standard NEX. Improved signal to noise was observed for the restricted FOV DLR images compared to conventional reconstruction using standard NEX. DLR diffusion images derived from reduced NEX datasets translated to time reductions of up to 68 % and 39 % for the restricted and large FOV series acquisitions, respectively.
DLR of diffusion weighted images can reduce image noise at standard NEX and potentially reduce prostate MRI exam time when utilizing reduced NEX datasets without sacrificing diagnostic image quality.
研究使用商用深度学习重建（DLR）算法重建的标准数据集和可变减少数据集所得到的高b值扩散加权成像（DWI）的诊断图像质量。
这是一项对52例接受常规前列腺MRI检查患者的回顾性研究，原始图像数据使用传统二维笛卡尔算法和DLR算法进行重建。通过使用具有减少激发次数（NEX）的DLR数据集重建图像来模拟缩短DWI采集时间。两名放射科医生使用4分李克特量表独立评估图像质量。对整个队列以及在MRI检查中被确定为患有临床显著性前列腺癌且随后经病理证实的患者子集进行信噪比（SNR）分析。
与传统重建图像相比，放射科医生认为受限视野和大视野（FOV）标准NEX数据集的DLR扩散图像的图像噪声更少，且阅片者间一致性良好。与使用标准NEX的传统重建图像相比，使用可变减少NEX的所有DLR图像均保持了诊断图像质量。与使用标准NEX的传统重建相比，受限视野DLR图像的信噪比有所提高。从减少NEX数据集获得的DLR扩散图像分别使受限视野和大视野系列采集的时间最多减少68%和39%。
扩散加权图像的DLR可以在标准NEX下减少图像噪声，并在使用减少NEX数据集时潜在地减少前列腺MRI检查时间，而不牺牲诊断图像质量。
