The effect of deep learning-based compressed sensing on the image quality of contrast-enhanced 3D T1-weighted images of the maxillofacial region

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

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