Purpose: To develop and evaluate a deep learning model for general
accelerated MRI reconstruction.
Materials and Methods: This retrospective study built a magnetic resonance
image processing transformer (MR-IPT) which includes multi-head-tails and a
single shared window transformer main body. Three mutations of MR-IPT with
different transformer structures were implemented to guide the design of our
MR-IPT model. Pre-trained on the MRI set of RadImageNet including 672675 images
with multiple anatomy categories, the model was further migrated and evaluated
on fastMRI knee dataset with 25012 images for downstream reconstruction tasks.
We performed comparison studies with three CNN-based conventional networks in
zero- and few-shot learning scenarios. Transfer learning process was conducted
on both MR-IPT and CNN networks to further validate the generalizability of
MR-IPT. To study the model performance stability, we evaluated our model with
various downstream dataset sizes ranging from 10 to 2500 images.
Result: The MR-IPT model provided superior performance in multiple downstream
tasks compared to conventional CNN networks. MR-IPT achieved a PSNR/SSIM of
26.521/0.6102 (4-fold) and 24.861/0.4996 (8-fold) in 10-epoch learning,
surpassing UNet128 at 25.056/0.5832 (4-fold) and 22.984/0.4637 (8-fold). With
the same large-scale pre-training, MR-IPT provided a 5% performance boost
compared to UNet128 in zero-shot learning in 8-fold and 3% in 4-fold.
Conclusion: MR-IPT framework benefits from its transformer-based structure
and large-scale pre-training and can serve as a solid backbone in other
downstream tasks with zero- and few-shot learning.

发展和评估了一种用于常规加速磁共振成像重建的深度学习模型。

磁共振图像处理变压器的普适重建

Magnetic Resonance Image Processing Transformer for General  Reconstruction

Magnetic resonance imaging (MRI) is a widely used non-invasive imaging
modality. However, a persistent challenge lies in balancing image quality with
imaging speed. This trade-off is primarily constrained by k-space measurements,
which traverse specific trajectories in the spatial Fourier domain (k-space).
These measurements are often undersampled to shorten acquisition times,
resulting in image artifacts and compromised quality. Generative models learn
image distributions and can be used to reconstruct high-quality images from
undersampled k-space data. In this work, we present the autoregressive image
diffusion (AID) model for image sequences and use it to sample the posterior
for accelerated MRI reconstruction. The algorithm incorporates both
undersampled k-space and pre-existing information. Models trained with fastMRI
dataset are evaluated comprehensively. The results show that the AID model can
robustly generate sequentially coherent image sequences. In 3D and dynamic MRI,
the AID can outperform the standard diffusion model and reduce hallucinations,
due to the learned inter-image dependencies.

非侵入性成像模式中，磁共振成像是一种广泛使用的成像方式，但其图像质量和成像速度之间存在平衡难题。本文介绍了一种基于自回归图像扩散（AID）模型的加速 MRI 重建方法，该算法结合了未采样的 k 空间和现有信息，评估了 fastMRI 数据集训练的模型，并展示了 AID 模型能够稳健地生成时序连贯的图像序列，在 3D 和动态 MRI 中的表现优于标准扩散模型，还能减少幻觉，并学习图像间的依赖关系。

自回归图像扩散：生成图像序列及在 MRI 中的应用

Autoregressive Image Diffusion: Generating Image Sequence and  Application in MRI

Deep unfolding networks (DUN) have emerged as a popular iterative framework
for accelerated magnetic resonance imaging (MRI) reconstruction. However,
conventional DUN aims to reconstruct all the missing information within the
entire null space in each iteration. Thus it could be challenging when dealing
with highly ill-posed degradation, usually leading to unsatisfactory
reconstruction. In this work, we propose a Progressive Divide-And-Conquer
(PDAC) strategy, aiming to break down the subsampling process in the actual
severe degradation and thus perform reconstruction sequentially. Starting from
decomposing the original maximum-a-posteriori problem of accelerated MRI, we
present a rigorous derivation of the proposed PDAC framework, which could be
further unfolded into an end-to-end trainable network. Specifically, each
iterative stage in PDAC focuses on recovering a distinct moderate degradation
according to the decomposition. Furthermore, as part of the PDAC iteration,
such decomposition is adaptively learned as an auxiliary task through a
degradation predictor which provides an estimation of the decomposed sampling
mask. Following this prediction, the sampling mask is further integrated via a
severity conditioning module to ensure awareness of the degradation severity at
each stage. Extensive experiments demonstrate that our proposed method achieves
superior performance on the publicly available fastMRI and Stanford2D FSE
datasets in both multi-coil and single-coil settings.

通过渐进的分解与征服策略，将重建过程分解成连续的步骤，并通过逐步恢复适度降级、自适应学习降级预测和严重程度条件模块等技术来提高磁共振成像重建性能。