Purpose: To propose a self-supervised deep learning-based compressed sensing
MRI (DL-based CS-MRI) method named "Adaptive Self-Supervised Consistency Guided
Diffusion Model (ASSCGD)" to accelerate data acquisition without requiring
fully sampled datasets. Materials and Methods: We used the fastMRI multi-coil
brain axial T2-weighted (T2-w) dataset from 1,376 cases and single-coil brain
quantitative magnetization prepared 2 rapid acquisition gradient echoes
(MP2RAGE) T1 maps from 318 cases to train and test our model. Robustness
against domain shift was evaluated using two out-of-distribution (OOD)
datasets: multi-coil brain axial postcontrast T1 -weighted (T1c) dataset from
50 cases and axial T1-weighted (T1-w) dataset from 50 patients. Data were
retrospectively subsampled at acceleration rates R in {2x, 4x, 8x}. ASSCGD
partitions a random sampling pattern into two disjoint sets, ensuring data
consistency during training. We compared our method with ReconFormer
Transformer and SS-MRI, assessing performance using normalized mean squared
error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity
index (SSIM). Statistical tests included one-way analysis of variance (ANOVA)
and multi-comparison Tukey's Honesty Significant Difference (HSD) tests.
Results: ASSCGD preserved fine structures and brain abnormalities visually
better than comparative methods at R = 8x for both multi-coil and single-coil
datasets. It achieved the lowest NMSE at R in {4x, 8x}, and the highest PSNR
and SSIM values at all acceleration rates for the multi-coil dataset. Similar
trends were observed for the single-coil dataset, though SSIM values were
comparable to ReconFormer at R in {2x, 8x}. These results were further
confirmed by the voxel-wise correlation scatter plots. OOD results showed
significant (p << 10^-5 ) improvements in undersampled image quality after
reconstruction.

基于自监督深度学习的压缩感知磁共振成像方法 “自适应自监督一致性引导扩散模型 (ASSCGD)” 提出，用于加速数据采集而无需完全采样的数据集。