用深度学习提升乳腺DWI图像质量，不增加扫描时间。
Comparing DWI image quality of deep-learning-reconstructed EPI with RESOLVE in breast lesions at 3.0T: a pilot study
- 用深度学习重建EPI序列，降低噪声提升分辨率。
- RESOLVE序列在信噪比和对比度上显著优于EPI DL（p<0.01）。
- 适合关注乳腺DWI成像质量的临床医生与影像科研人员。
深度学习可有效改善扩散加权成像（DWI）的空间分辨率，在不延长扫描时间的前提下降低噪声。本研究对比了基于深度学习重建的回波平面成像（EPI DL）DWI序列与临床常用的同时多层采集（SMS）RESOLVE序列在乳腺病变中的图像质量。对20名受试者的乳腺图像进行定性评估，并计算高b值（b800）图像及表观弥散系数（ADC）图的信噪比（SNR）和对比噪声比（CNR）。结果显示，在手动勾画区域中，RESOLVE的b800图像（p=0.006）与ADC图（p=0.001）SNR均显著高于EPI DL；圆形勾画区域中亦呈显著差异（p=0.001）。深度学习重建可能在不牺牲扫描时间与图像质量的前提下，改善乳腺DWI的低空间分辨率问题。结合读出分段与SMS技术，有望进一步提升乳腺DWI的临床价值。
原文摘要 · Abstract (English)
The challenging spatial resolution of DWI could be addressed by deep learning based image reconstruction, by reducing noise without increasing acquisition time. To compare the image quality of the Echo Planar Imaging Deep Learning (EPI DL) DWI sequence with the clinically used simultaneous multi slice (SMS) RESOLVE in breast lesions. EPI DL and RESOLVE breast images from 20 participants were qualitatively evaluated. Quantitative image quality metrics of SNR and CNR on both high b-value (b800) images and ADC maps were calculated. SNR in RESOLVE vs. EP DL differed statistically significantly in manually delineations for b800 (p=0.006), ADC maps (p=0.001), and in ADC circularly delineations (0.001). DWI DL reconstruction may be clinically useful for addressing low-spatial resolution without compromising acquisition time and image quality. Such benefits coupled with the available methods of readout segmentation and SMS acquisitions may further enhance the value of DWI in breast imaging.
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