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

[1] Kono K(2001)The role of diffusion-weighted imaging in patients with brain tumors AJNR Am J Neuroradiol 22 1081-1088

[2] Inoue Y(2015)Role of diffusion-weighted imaging in acute stroke management using low-field magnetic resonance imaging in resource-limited settings West Afr J Radiol 22 61-66

[3] Nakayama K(1994)Interleaved echo planar imaging on a standard MRI system Magn Reson Med 31 67-72

[4] Shakudo M(2009)High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-based reacquisition Magn Reson Med 62 468-475

[5] Morino M(2013)Clinical evaluation of single-shot and readout-segmented diffusion-weighted imaging in stroke patients at 3 T Acta Radiol 54 299-306

[6] Ohata K(2018)A comparison of readout segmented EPI and interleaved EPI in high-resolution diffusion weighted imaging Magn Reson Imaging 1 39-47

[7] Okorie CK(2020)Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers Magn Reson Med Sci 19 195-206

[8] Ogbole GI(2022)Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method Eur Radiol 32 4527-453

[9] Owolabi MO(2021)A preliminary study of deep learning-based reconstruction specialized for denoising in high-frequency domain: usefulness in high-resolution three-dimensional magnetic resonance cisternography of the cerebellopontine angle Neuroradiology 63 63-71

[10] Ogun O(2019)Improvement of image quality at CT and MRI using deep learning Jpn J Radiol 37 73-80

[1] Kono K(2001)The role of diffusion-weighted imaging in patients with brain tumors AJNR Am J Neuroradiol 22 1081-1088

[2] Inoue Y(2015)Role of diffusion-weighted imaging in acute stroke management using low-field magnetic resonance imaging in resource-limited settings West Afr J Radiol 22 61-66

[3] Nakayama K(1994)Interleaved echo planar imaging on a standard MRI system Magn Reson Med 31 67-72

[4] Shakudo M(2009)High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-based reacquisition Magn Reson Med 62 468-475

[5] Morino M(2013)Clinical evaluation of single-shot and readout-segmented diffusion-weighted imaging in stroke patients at 3 T Acta Radiol 54 299-306

[6] Ohata K(2018)A comparison of readout segmented EPI and interleaved EPI in high-resolution diffusion weighted imaging Magn Reson Imaging 1 39-47

[7] Okorie CK(2020)Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers Magn Reson Med Sci 19 195-206

[8] Ogbole GI(2022)Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method Eur Radiol 32 4527-453

[9] Owolabi MO(2021)A preliminary study of deep learning-based reconstruction specialized for denoising in high-frequency domain: usefulness in high-resolution three-dimensional magnetic resonance cisternography of the cerebellopontine angle Neuroradiology 63 63-71

[10] Ogun O(2019)Improvement of image quality at CT and MRI using deep learning Jpn J Radiol 37 73-80