Abstract
In MRI practice, it is inevitable to appropriately balance between image resolution, signal-to-noise ratio (SNR), and scan time. It has been shown that super-resolution reconstruction (SRR) is effective to achieve such a balance, and has obtained better results than direct high-resolution (HR) acquisition, for certain contrasts and sequences. The focus of this work was on constructing images with spatial resolution higher than can be practically obtained by direct Fourier encoding. A novel learning approach was developed, which was able to provide an estimate of the spatial gradient prior from the low-resolution (LR) inputs for the HR reconstruction. By incorporating the anisotropic acquisition schemes, the learning model was trained over the LR images themselves only. The learned gradients were integrated as prior knowledge into a gradient-guided SRR model. A closed-form solution to the SRR model was developed to obtain the HR reconstruction. Our approach was assessed on the simulated data as well as the data acquired on a Siemens 3T MRI scanner containing 45 MRI scans from 15 subjects. The experimental results demonstrated that our approach led to superior SRR over state-of-the-art methods, and obtained better images at lower or the same cost in scan time than direct HR acquisition.
This work was supported in part by the National Institutes of Health (NIH) under grants R01 NS079788, R01 EB019483, R01 EB018988, R01 NS106030, IDDRC U54 HD090255; by a research grant from the Boston Children’s Hospital Translational Research Program; by a Technological Innovations in Neuroscience Award from the McKnight Foundation; by a research grant from the Thrasher Research Fund; and by a pilot grant from National Multiple Sclerosis Society under Award Number PP-1905-34002.
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Sui, Y., Afacan, O., Gholipour, A., Warfield, S.K. (2020). Learning a Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_14
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