Petkovska Iva, Alus Or, Rodriguez Lee, El Homsi Maria, Golia Pernicka Jennifer S, Fernandes Maria Clara, Zheng Junting, Capanu Marinela, Otazo Ricardo
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Department of Medical Physics, Memorial Sloan Kettering Cancer Cencer, New York, NY, USA.
Eur J Radiol. 2024 Dec;181:111802. doi: 10.1016/j.ejrad.2024.111802. Epub 2024 Oct 24.
To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagnostic accuracy and image quality in restaging rectal MRI following total neoadjuvant therapy (TNT).
This retrospective single-center study included patients with locally advanced rectal cancer who underwent restaging rectal MRI between December 30, 2021, and June 1, 2022, following TNT. A convolutional neural network trained with DWI data was employed to denoise accelerated DWI acquisitions (i.e., acquisitions performed with a reduced number of repetitions compared to standard acquisitions). Image characteristics and residual disease were independently assessed by two radiologists across original and denoised images. Statistical analyses included the Wilcoxon signed-rank test to compare image quality scores across denoised and original images, weighted kappa statistics for inter-reader agreement assessment, and the calculation of measures of diagnostic accuracy.
In 46 patients (median age, 60 years [IQR: 47-72]; 37 men and 9 women), 8- and 16-fold accelerated images maintained or exhibited enhanced lesion visibility and image quality compared with original images that were performed 16 repetitions. Denoised images maintained diagnostic accuracy, with conditional specificities of up to 96 %. Moderate-to-high inter-reader agreement indicated reliable image and diagnostic assessment. The overall test yield for denoised DWI reconstructions ranged from 76-98 %, demonstrating a reduction in equivocal interpretations.
Applying a denoising network to accelerate rectal DWI acquisitions can reduce scan times and enhance image quality while maintaining diagnostic accuracy, presenting a potential pathway for more efficient rectal cancer management.
评估深度学习去噪方法在加速扩散加权成像（DWI）中的有效性，从而提高全新辅助治疗（TNT）后直肠癌重新分期MRI的诊断准确性和图像质量。
这项回顾性单中心研究纳入了在2021年12月30日至2022年6月1日期间接受TNT后进行直肠癌重新分期MRI检查的局部晚期直肠癌患者。使用经DWI数据训练的卷积神经网络对加速DWI采集（即与标准采集相比重复次数减少的采集）进行去噪。两名放射科医生对原始图像和去噪图像的图像特征和残留疾病进行独立评估。统计分析包括Wilcoxon符号秩检验，以比较去噪图像和原始图像的图像质量评分；加权kappa统计，用于评估阅片者间的一致性；以及计算诊断准确性指标。
46例患者（中位年龄60岁[四分位间距：47 - 72岁]；37例男性和9例女性），与进行16次重复的原始图像相比，8倍和16倍加速图像保持或显示出病变可见性和图像质量增强。去噪图像保持了诊断准确性，条件特异性高达96%。阅片者间的中度至高度一致性表明图像和诊断评估可靠。去噪DWI重建的总体检测率为76% - 98%，表明模棱两可的解释减少。
应用去噪网络加速直肠癌DWI采集可减少扫描时间，提高图像质量，同时保持诊断准确性，为更高效的直肠癌管理提供了一条潜在途径。
