Zhang Xinxin, Xu Xiaojuan, Wang Yichen, Zhang Jie, Hu Mancang, Zhang Jin, Zhang Lianyu, Wang Sicong, Li Yi, Zhao Xinming, Chen Yan
Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
GE Healthcare, MR Research China, Daxing district, Tongji south road No1, Beijing, 100176, China.
Insights Imaging. 2024 Jun 9;15(1):139. doi: 10.1186/s13244-024-01686-9.
To investigate whether reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with deep learning reconstruction (DLR) can improve the accuracy of evaluating muscle invasion using VI-RADS.
Eighty-six bladder cancer participants who were evaluated by conventional full field-of-view (fFOV) DWI, standard rFOV (rFOV) DWI, and fast rFOV with DLR (rFOV) DWI were included in this prospective study. Tumors were categorized according to the vesical imaging reporting and data system (VI-RADS). Qualitative image quality scoring, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and ADC value were evaluated. Friedman test with post hoc test revealed the difference across the three DWIs. Receiver operating characteristic analysis was performed to calculate the areas under the curve (AUCs).
The AUC of the rFOV DWI and rFOV DWI were higher than that of fFOV DWI. rFOV DWI reduced the acquisition time from 5:02 min to 3:25 min, and showed higher scores in overall image quality with higher CNR and SNR, compared to rFOV DWI (p < 0.05). The mean ADC of all cases of rFOV DWI and rFOV DWI was significantly lower than that of fFOV DWI (all p < 0.05). There was no difference in mean ADC value and the AUC for evaluating muscle invasion between rFOV DWI and rFOV DWI (p > 0.05).
rFOV DWI with DLR can improve the diagnostic accuracy of fFOV DWI for evaluating muscle invasion. Applying DLR to rFOV DWI reduced the acquisition time and improved overall image quality while maintaining ADC value and diagnostic accuracy.
The diagnostic performance and image quality of full field-of-view DWI, reduced field-of-view (rFOV) DWI with and without DLR were compared. DLR would benefit the wide clinical application of rFOV DWI by reducing the acquisition time and improving the image quality.
Deep learning reconstruction (DLR) can reduce scan time and improve image quality. Reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with DLR showed better diagnostic performances than full field-of-view DWI. There was no difference of diagnostic accuracy between rFOV DWI with DLR and standard rFOV DWI.
探讨采用深度学习重建(DLR)的缩小视野(rFOV)扩散加权成像(DWI)能否提高使用膀胱影像报告和数据系统(VI-RADS)评估肌肉浸润的准确性。
本前瞻性研究纳入了86例接受传统全视野(fFOV)DWI、标准rFOV DWI以及采用DLR的快速rFOV DWI评估的膀胱癌患者。根据膀胱影像报告和数据系统(VI-RADS)对肿瘤进行分类。评估定性图像质量评分、信噪比(SNR)、对比噪声比(CNR)和表观扩散系数(ADC)值。采用Friedman检验及事后检验揭示三种DWI之间的差异。进行受试者操作特征分析以计算曲线下面积(AUC)。
rFOV DWI和采用DLR的rFOV DWI的AUC高于fFOV DWI。与采用DLR的rFOV DWI相比,rFOV DWI将采集时间从5分02秒缩短至3分25秒,且在整体图像质量方面得分更高,CNR和SNR更高(p<0.05)。采用DLR的rFOV DWI和rFOV DWI所有病例的平均ADC均显著低于fFOV DWI(所有p<0.05)。采用DLR的rFOV DWI和rFOV DWI在评估肌肉浸润的平均ADC值和AUC方面无差异(p>0.05)。
采用DLR的rFOV DWI可提高fFOV DWI评估肌肉浸润的诊断准确性。将DLR应用于rFOV DWI可缩短采集时间并改善整体图像质量,同时保持ADC值和诊断准确性。
比较了全视野DWI、有无DLR的缩小视野(rFOV)DWI的诊断性能和图像质量。DLR通过缩短采集时间和改善图像质量,将有利于rFOV DWI在临床上的广泛应用。
深度学习重建(DLR)可缩短扫描时间并改善图像质量。采用DLR的缩小视野(rFOV)扩散加权成像(DWI)的诊断性能优于全视野DWI。采用DLR的rFOV DWI与标准rFOV DWI的诊断准确性无差异。