  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.
