Hiyama T, Sekiya K, Kuno H, Oda S, Kusumoto M, Minami M, et al. Imaging of extracranial head and neck lesions in cancer patients: a symptom-based approach. Jpn J Radiol. 2019;37(5):354–70.
Koyfman SA, Ismaila N, Crook D, D’Cruz A, Rodriguez CP, Sher DJ, et al. Management of the neck in squamous cell carcinoma of the oral cavity and oropharynx: ASCO Clinical Practice Guideline. J Clin Oncol. 2019;37(20):1753–74.
Article PubMed PubMed Central Google Scholar
Suzuki N, Kuribayashi A, Sakamoto K, Sakamoto J, Nakamura S, Watanabe H, et al. Diagnostic abilities of 3T MRI for assessing mandibular invasion of squamous cell carcinoma in the oral cavity: comparison with 64-row multidetector CT. Dentomaxillofac Radiol. 2019;48(4):20180311.
Article PubMed PubMed Central Google Scholar
Kami Y, Chikui T, Togao O, Kawano S, Fujii S, Ooga M, et al. Usefulness of reconstructed images of Gd-enhanced 3D gradient echo sequences with compressed sensing for mandibular cancer diagnosis: comparison with CT images and histopathological findings. Eur Radiol. 2023;33(2):845–53.
Article CAS PubMed Google Scholar
Pruessmann KP, Weiger M, Bornert P, Boesiger P. Advances in sensitivity encoding with arbitrary k-space trajectories. Magn Reson Med. 2001;46(4):638–51.
Article CAS PubMed Google Scholar
Takumi K, Nagano H, Nakanosono R, Kumagae Y, Fukukura Y, Yoshiura T. Combined signal averaging and compressed sensing: impact on quality of contrast-enhanced fat-suppressed 3D turbo field-echo imaging for pharyngolaryngeal squamous cell carcinoma. Neuroradiology. 2020;62(10):1293–9.
Bratke G, Rau R, Weiss K, Kabbasch C, Sircar K, Morelli JN, et al. Accelerated MRI of the lumbar spine using compressed sensing: quality and efficiency. J Magn Reson Imaging. 2019;49(7):e164–75.
Iuga AI, Rauen PS, Siedek F, Grosse-Hokamp N, Sonnabend K, Maintz D, et al. A deep learning-based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers. Br J Radiol. 2023;96(1146):20220074.
Article PubMed PubMed Central Google Scholar
Kami Y, Chikui T, Togao O, Ooga M, Yoshiura K. Comparison of image quality of head and neck lesions between 3D gradient echo sequences with compressed sensing and the multi-slice spin echo sequence. Acta Radiol Open. 2020;9(9):2058460120956644.
Article PubMed PubMed Central Google Scholar
Kim S, Park H, Park SH. A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies. Biomed Eng Lett. 2024;14(6):1221–42.
Article PubMed PubMed Central Google Scholar
Han Y, Sunwoo L, Ye JC. k -space deep learning for accelerated MRI. IEEE Trans Med Imaging. 2020;39(2):377–86.
Pezzotti N, Yousefi S, Elmahdy MS, Van Gemert JHF, Schuelke C, Doneva M, et al. An adaptive intelligence algorithm for undersampled knee MRI reconstruction. Ieee Access. 2020;8:204825–38.
Foreman SC, Neumann J, Han J, Harrasser N, Weiss K, Peeters JM, et al. Deep learning-based acceleration of compressed sense MR imaging of the ankle. Eur Radiol. 2022;32(12):8376–85.
Article CAS PubMed PubMed Central Google Scholar
Fujima N, Nakagawa J, Ikebe Y, Kameda H, Harada T, Shimizu Y, et al. Improved image quality in contrast-enhanced 3D-T1 weighted sequence by compressed sensing-based deep-learning reconstruction for the evaluation of head and neck. Magn Reson Imaging. 2024;108:111–5.
Funayama S, Motosugi U, Ichikawa S, Morisaka H, Omiya Y, Onishi H. Model-based deep learning reconstruction using a folded image training strategy for abdominal 3D T1-weighted imaging. Magn Reson Med Sci. 2023;22(4):515–26.
Harder FN, Weiss K, Amiel T, Peeters JM, Tauber R, Ziegelmayer S et al. Prospectively accelerated T2-weighted imaging of the prostate by combining compressed SENSE and deep learning in patients with histologically proven prostate cancer. Cancers (Basel) 2022;14(23).
Heckel R, Jacob M, Chaudhari A, Perlman O, Shimron E. Deep learning for accelerated and robust MRI reconstruction. MAGMA. 2024;37(3):335–68.
Article CAS PubMed PubMed Central Google Scholar
Hashim. Novel image-dependent quality assessment measures. J Comput Sci. 2014;10(8):1548–60.
Renieblas GP, Nogues AT, Gonzalez AM, Gomez-Leon N, Del Castillo EG. Structural similarity index family for image quality assessment in radiological images. J Med Imaging (Bellingham). 2017;4(3):035501.
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