Keywords: Other AI/ML, Machine Learning/Artificial Intelligence, imaging transformer, generalization
Motivation: MR denoising using the CNN models often requires abundant high quality data for training. In many applications, such as higher acceleration and low field, high quality data is not available. This study overcome this limitation by developing a SNR unit based training scheme and a novel imaging transformer (imformer) architecture.
Goal(s): To develop and validate a novel imformer model for MR denoising, enabling generalization across field-strengths, imaging contrasts, and anatomy.
Approach: SNR unit training scheme and imaging transformer architecture
Results: Imformer models outperformed CNNs and conventional transformer. The SNR training enables storng generalization.
Impact: Recovery high-fidelity MR signal from very low SNR inputs; Enable 0.55T MRI model training.
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