论文标题
高空间分辨率的基于内存有效的模型的深度学习重建3D非现行收购
Memory Efficient Model Based Deep Learning Reconstructions for High Spatial Resolution 3D Non-Cartesian Acquisitions
论文作者
论文摘要
目的：基于模型的深度学习（MBDL），由于极端的GPU内存需求（使用传统的反向传播> 250 GB> 250 GB），主要是因为该模型中嵌入的数据符合性步骤所需的整个卷都需要全部挑战。这项工作的目的是开发和应用一种称为块智能学习的内存有效方法，该方法将梯度检查点与贴片训练相结合，以允许使用MBDL快速和高质量的3D非现行式重建。方法：将块的学习应用于单个展开，将输入音量分解为较小的补丁，每个补丁梯度检查点，通过神经网络正常化程序将每个补丁传递，然后从这些输出补丁中重建完整的卷以获得数据持续性。在培训期间，该方法在跨透中应用。通过将GPU存储器与用户选择的补丁程序大小而不是完整卷一起使用，可以大大降低内存需求。该算法用于训练MBDL体系结构，以重建高度不足，1.25毫米各向同性的肺磁共振血管造影量，其基质大小在单个GPU上的300-450 x 200-300 x 300-450变化。我们比较了块的学习重建与L1小波压缩的重建和代理地面真相图像。主要结果：相对于L1小波压缩传感，具有块良好学习的MBDL显着提高了图像质量，同时减少了平均重建时间38倍。意义：方块学习允许将MBDL应用于高空间分辨率，3D非现行数据集，相对于传统迭代方法的图像质量提高了，重建时间的显着减少和大量缩短
Objective: Model based deep learning (MBDL) has been challenging to apply to the reconstruction of 3D non-Cartesian MRI acquisitions due to extreme GPU memory demand (>250 GB using traditional backpropagation) primarily because the entire volume is needed for data-consistency steps embedded in the model. The goal of this work is to develop and apply a memory efficient method called block-wise learning that combines gradient checkpointing with patch-wise training to allow for fast and high-quality 3D non-Cartesian reconstructions using MBDL. Approach: Block-wise learning applied to a single unroll decomposes the input volume into smaller patches, gradient checkpoints each patch, passes each patch iteratively through a neural network regularizer, and then rebuilds the full volume from these output patches for data-consistency. This method is applied across unrolls during training. Block-wise learning significantly reduces memory requirements by tying GPU memory to user selected patch size instead of the full volume. This algorithm was used to train a MBDL architecture to reconstruct highly undersampled, 1.25mm isotropic, pulmonary magnetic resonance angiography volumes with matrix sizes varying from 300-450 x 200-300 x 300-450 on a single GPU. We compared block-wise learning reconstructions against L1 wavelet compressed reconstructions and proxy ground truth images. Main results: MBDL with block-wise learning significantly improved image quality relative to L1 wavelet compressed sensing while simultaneously reducing average reconstruction time 38x. Significance: Block-wise learning allows for MBDL to be applied to high spatial resolution, 3D non-Cartesian datasets with improved image quality and significant reductions in reconstruction time relative to traditional iterative methods
