Abstract：
Objective.Respiratory motion, cardiac motion and inherently low signal-to-noise ratio (SNR) are major limitations ofin vivocardiac diffusion tensor imaging (DTI). We propose a novel enhancement method that uses unsupervised learning based invertible wavelet scattering (IWS) to improve the quality ofin vivocardiac DTI.Approach.Our method starts by extracting nearly transformation-invariant features from multiple cardiac diffusion-weighted (DW) image acquisitions using multi-scale wavelet scattering (WS). Then, the relationship between the WS coefficients and DW images is learned through a multi-scale encoder and a decoder network. Using the trained encoder, the deep features of WS coefficients of multiple DW image acquisitions are further extracted and then fused using an average rule. Finally, using the fused WS features and trained decoder, the enhanced DW images are derived.Main result.We evaluate the performance of the proposed method by comparing it with several methods on threein vivocardiac DTI datasets in terms of SNR, contrast to noise ratio (CNR), fractional anisotropy (FA), mean diffusivity (MD) and helix angle (HA). Comparing against the best comparison method, SNR/CNR of diastolic, gastric peristalsis influenced, and end-systolic DW images were improved by 1%/16%, 5%/6%, and 56%/30%, respectively. The approach also yielded consistent FA and MD values and more coherent helical fiber structures than the comparison methods used in this work.Significance.The ablation results verify that using the transformation-invariant and noise-robust wavelet scattering features enables us to effectively explore the useful information from the limited data, providing a potential mean to alleviate the dependence of the fusion results on the number of repeated acquisitions, which is beneficial for dealing with the issues of noise and residual motion simultaneously and therefore improving the quality ofinvivocardiac DTI. Code can be found inhttps://github.com/strawberry1996/WS-MCNN.
摘要：
客观
呼吸运动,心脏运动,和固有的低信噪比（SNR）是体内心脏扩散张量成像（DTI）的主要限制。我们提出了一种新颖的增强方法，该方法使用基于无监督学习的可逆小波散射（IWS）来改善体内心脏DTI的质量。＆＃xD;＆＃xD;方法＆＃xD;我们的方法首先使用多尺度小波散射（WS）从多个心脏扩散加权（DW）图像采集中提取几乎变换不变的特征。通过多尺度编码器和解码器网络来学习WS系数和DW图像之间的关系。使用经过训练的编码器，进一步提取多个DW图像采集的WS系数的深层特征，然后使用平均规则进行融合。最后,使用融合的WS特征和经过训练的解码器，导出增强的DW图像。＆＃xD;＆＃xD;主要结果＆＃xD;我们通过在SNR方面与三个体内心脏DTI数据集上的几种方法进行比较来评估所提出方法的性能，对比度噪声比(CNR)，分数各向异性（FA），平均扩散率（MD），和螺旋角（HA）。与最佳比较方法相比，舒张压的SNR/CNR，胃蠕动受影响，收缩末期DW图像改善了1%/16%，5%/6%，和56%/30%，分别。与这项工作中使用的比较方法相比，该方法还产生了一致的FA和MD值以及更连贯的螺旋纤维结构。＆＃xD;＆＃xD;意义＆＃xD;消融结果验证了使用变换不变和噪声鲁棒的小波散射特征可以从有限的数据中有效地探索有用的信息。这提供了一种潜在的手段来减轻融合结果对重复采集次数的依赖性，这有利于同时处理噪声和残余运动问题，从而提高体内心脏DTI的质量。
