论文标题
多用途定量MRI的插件方法：使用预训练的深层DENOISER的图像重建
A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers
论文作者
论文摘要
当前的时空深度学习方法用于磁共振指纹（MRF）构建了针对特定K-Space子采样模式定制的伪影模型，该模型用于快速（压缩）采集。当在训练深度学习模型和/或测试期间的变化过程中，获取过程未知时，这可能没有用。本文提出了一种对MRF的迭代深度学习插件重建方法，该方法适应了前瞻性获取过程。时空图像先验是由图像Denoiser（即卷积神经网络（CNN））学习的，该卷积神经网络（CNN）经过训练，可以从数据中删除通用的白色高斯噪声（不是特定的子采样伪影）。然后，该CNN Denoiser被用作迭代重建算法中数据驱动的收缩式操作员。然后，该算法具有相同的DeNoiser模型，然后在两个具有不同亚采样模式的模拟采集过程上进行测试。结果表明，针对采集方案以及组织定量生物核心的准确映射，表现出一致的脱氧性能。可用的软件：https：//github.com/ketanfatania/qmri-pnp-recon-poc
Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes during testing time. This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process. Spatiotemporal image priors are learned by an image denoiser i.e. a Convolutional Neural Network (CNN), trained to remove generic white gaussian noise (not a particular subsampling artefact) from data. This CNN denoiser is then used as a data-driven shrinkage operator within the iterative reconstruction algorithm. This algorithm with the same denoiser model is then tested on two simulated acquisition processes with distinct subsampling patterns. The results show consistent de-aliasing performance against both acquisition schemes and accurate mapping of tissues' quantitative bio-properties. Software available: https://github.com/ketanfatania/QMRI-PnP-Recon-POC
