Abstract：
Objective.In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration.Approach.To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness.Main results.Ablation experiments were conducted to ascertain the proposed method\'s capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%.Significance.The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at:https://github.com/sober235/DPP.
摘要：
目的：在具有虚拟通道扩展波编码的MR并行成像中，对全面和准确表征背景阶段的能力施加了限制。这些限制主要归因于校准过程仅依赖于中心低频ACS数据进行校准。
方法：为了应对在波编码中准确估计背景相位的挑战，提出了一种由深相先验（DPP）引导的新型深度神经网络模型，并带有集成虚拟共轭线圈（VCC）扩展。具体而言,在拟议的框架内，背景阶段通过采用精心设计的解码器卷积神经网络来隐含地表征，在变换域中利用相位平滑性和紧凑支持的固有特性。此外，所提出的具有波编码的模型受益于额外的先验，它结合了潜像的传输稀疏性和线圈灵敏度平滑性。
主要结果:进行消融实验以确定所提出的方法隐含地表示CSM和背景阶段的能力。随后，通过与竞争方法的置信度比较，证明了所提出方法的优越性，采用4倍和5倍加速实验。在实现4倍和5倍加速时，最佳
定量指标(PSNR/SSIM/NMSE)为44.1359dB/0.9863/0.0008(4倍)和41.2074/0.9846/0.0017(5倍)，分别。此外，通过对T1、T2、T2*、和各种欠采样模式。此外,通过＆＃xD;探索加速相敏SWI成像，DPP比传统方法提供了更好的性能。在SWI加速成像中，在(PSNR/SSIM/NMSE)方面，它也超过了最佳竞争方法，为0.096%/0.009%/0.0017%。＆＃xD;意义：所提出的方法能够在集成的VCC和波编码框架中精确表征背景相位，通过理论分析和实证结果得到支持。我们的代码可在以下网址获得：https://github.com/sober235/DPP。
