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Purpose To introduce two novel learning‐based motion artifact removal networks (LEARN) for the estimation of quantitative motion‐ and B0‐inhomogeneity‐corrected R2∗ maps from motion‐corrupted multi‐Gradient‐Recalled Echo (mGRE) MRI data. Methods We train two convolutional neural networks (CNNs) to correct motion artifacts for high‐quality estimation of quantitative B0‐inhomogeneity‐corrected R2∗ maps from mGRE sequences. The first CNN, LEARN‐IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high‐quality motion‐free quantitative R2∗ (and any other mGRE‐enabled) maps using the standard voxel‐wise analysis or machine learning‐based analysis. The second CNN, LEARN‐BIO, is trained to directly generate motion‐ and B0‐inhomogeneity‐corrected quantitative R2∗ maps from motion‐corrupted magnitude‐only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay. Results We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative R2∗ maps. Significant reduction of motion artifacts on experimental in vivo motion‐corrupted data has also been achieved by using our trained models. Conclusion Both LEARN‐IMG and LEARN‐BIO can enable the computation of high‐quality motion‐ and B0‐inhomogeneity‐corrected R2∗ maps. LEARN‐IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of R2∗ maps, while LEARN‐BIO directly performs motion‐ and B0‐inhomogeneity‐corrected R2∗ estimation. Both LEARN‐IMG and LEARN‐BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN‐BIO is an advantage that can lead to a broader clinical application.

We evaluated the impact of PET respiratory motion correction (MoCo) in a phantom and patients. Moreover, we proposed and examined a PET MoCo approach using motion vector fields (MVFs) from a deep-learning reconstructed short MRI scan. Methods:The evaluation of PET MoCo was performed in a respiratory motion phantom study with varying lesion sizes and tumor to background ratios (TBRs) using a static scan as the ground truth. MRI-based MVFs were derived from either 2000 spokes (MoCo 2000 , 5-6 min acquisition time) using a Fourier transform reconstruction or 200 spokes (MoCo P2P200 , 30-40 s acquisition time) using a deep-learning Phase2Phase (P2P) reconstruction and then incorporated into PET MoCo reconstruction. For six patients with hepatic lesions, the performance of PET MoCo was evaluated using quantitative metrics (SUV max , SUV peak , SUV mean , lesion volume) and a blinded radiological review on lesion conspicuity.Results: MRI-assisted PET MoCo methods provided similar results to static scans across most lesions with varying TBRs in the phantom. Both MoCo 2000 and MoCo P2P200 PET images had significantly higher SUV max , SUV peak , SUV mean and significantly lower lesion volume than non-motion-corrected (non-MoCo) PET images. There was no statistical difference between MoCo 2000 and MoCo P2P200 PET images for SUV max , SUV peak , SUV mean or lesion volume. Both radiological reviewers found that MoCo 2000 and MoCo P2P200 PET significantly improved lesion conspicuity. Conclusion: An MRI-assisted PET MoCo method was evaluated using the ground truth in a phantom study. In patients with hepatic lesions, PET MoCo images improved quantitative and qualitative metrics based on only 30-40 s of MRI motion modeling data.