Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images

[11] Adeyinka A(2022)Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography Jpn J Radiol 40 476-83

[12] Ogunniyi A(2018)Super-resolution musculoskeletal MRI using deep learning Magn Reson Med 80 2139-2154

[13] Butts K(2020)Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers J Magn Reson Imaging. 51 768-79

[14] Riederer SJ(2019)Multiscale brain MRI super-resolution using deep 3D convolutional networks Comput Med Imaging Graph 77 101647-741

[15] Ehman RL(1994)Reduction of partial-volume artifacts with zero-filled interpolation in three-dimensional MR angiography J Magn Reson Imaging 4 733-280

[16] Thompson RM(2001)Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy J Magn Reson Imaging 14 270-254

[17] Jack CR(1994)Estimation of the effective self-diffusion tensor from the NMR spin echo J Magn Reson B 103 247-43

[18] Porter DA(2018)Magnetic resonance cholangiopancreatography with GRASE sequence at 3.0T: does it improve image quality and acquisition time as compared with 3D TSE? Eur Radiol 28 2436-230

[19] Heidemann RM(2022)Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges J Digit Imaging 36 204-160

[20] Morelli J(2022)A generative adversarial network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images Magn Reson Imaging 1 153-4164

[11] Adeyinka A(2022)Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography Jpn J Radiol 40 476-83

[12] Ogunniyi A(2018)Super-resolution musculoskeletal MRI using deep learning Magn Reson Med 80 2139-2154

[13] Butts K(2020)Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers J Magn Reson Imaging. 51 768-79

[14] Riederer SJ(2019)Multiscale brain MRI super-resolution using deep 3D convolutional networks Comput Med Imaging Graph 77 101647-741

[15] Ehman RL(1994)Reduction of partial-volume artifacts with zero-filled interpolation in three-dimensional MR angiography J Magn Reson Imaging 4 733-280

[16] Thompson RM(2001)Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy J Magn Reson Imaging 14 270-254

[17] Jack CR(1994)Estimation of the effective self-diffusion tensor from the NMR spin echo J Magn Reson B 103 247-43

[18] Porter DA(2018)Magnetic resonance cholangiopancreatography with GRASE sequence at 3.0T: does it improve image quality and acquisition time as compared with 3D TSE? Eur Radiol 28 2436-230

[19] Heidemann RM(2022)Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges J Digit Imaging 36 204-160

[20] Morelli J(2022)A generative adversarial network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images Magn Reson Imaging 1 153-4164