Background: Diffusion-weighted images (DWI) obtained by echo-planar imaging (EPI) are frequently degraded by susceptibility artifacts. It has been suggested that DWI obtained by fast advanced spin-echo (FASE) or reconstructed with deep learning reconstruction (DLR) could be useful for image quality improvements. The purpose of this investigation using in vitro and in vivo studies was to determine the influence of sequence difference and of DLR for DWI on image quality, apparent diffusion coefficient (ADC) evaluation, and differentiation of malignant from benign head and neck tumors. Methods: For the in vitro study, a DWI phantom was scanned by FASE and EPI sequences and reconstructed with and without DLR. Each ADC within the phantom for each DWI was then assessed and correlated for each measured ADC and standard value by Spearman’s rank correlation analysis. For the in vivo study, DWIs obtained by EPI and FASE sequences were also obtained for head and neck tumor patients. Signal-to-noise ratio (SNR) and ADC were then determined based on ROI measurements, while SNR of tumors and ADC were compared between all DWI data sets by means of Tukey’s Honest Significant Difference test. Results: For the in vitro study, all correlations between measured ADC and standard reference were significant and excellent (0.92 ≤ ρ ≤ 0.99,p< 0.0001). For the in vivo study, the SNR of FASE with DLR was significantly higher than that of FASE without DLR (p= 0.02), while ADC values for benign and malignant tumors showed significant differences between each sequence with and without DLR (p< 0.05). Conclusion: In comparison with EPI sequence, FASE sequence and DLR can improve image quality and distortion of DWIs without significantly influencing ADC measurements or differentiation capability of malignant from benign head and neck tumors.
背景：通过回波平面成像（EPI）获得的扩散加权图像（DWI）常因磁敏感伪影而质量下降。已有研究表明，采用快速高级自旋回波（FASE）序列获取或通过深度学习重建（DLR）技术重建的DWI可能有助于改善图像质量。本研究通过体外与体内实验，旨在探讨序列差异及DLR对DWI图像质量、表观扩散系数（ADC）评估以及头颈部良恶性肿瘤鉴别能力的影响。方法：在体外实验中，采用FASE和EPI序列对DWI体模进行扫描，并分别进行DLR重建与非重建处理。通过斯皮尔曼等级相关分析，评估各DWI图像中体模区域的ADC测量值，并将其与标准参考值进行相关性分析。在体内实验中，对头颈部肿瘤患者分别采集EPI和FASE序列的DWI图像。基于感兴趣区域测量计算信噪比（SNR）和ADC值，并采用Tukey诚实显著性检验比较所有DWI数据集间肿瘤SNR及ADC值的差异。结果：体外实验显示，所有ADC测量值与标准参考值之间均存在显著且高度相关性（0.92 ≤ ρ ≤ 0.99, p < 0.0001）。体内实验表明，采用DLR的FASE序列SNR显著高于未使用DLR的FASE序列（p = 0.02）；且无论是否采用DLR，各序列间良恶性肿瘤的ADC值均存在显著差异（p < 0.05）。结论：与EPI序列相比，FASE序列结合DLR技术可在不影响ADC测量准确性及头颈部良恶性肿瘤鉴别能力的前提下，有效改善DWI的图像质量并减少畸变。
Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors
肿瘤(癌症)患者之家