Ba-Ssalamaha A, Schick S, Heimberger K, et al. Ultrafast magnetic resonance imaging of the brain. Magn Reson Imaging. 2000;18(3):237–43. https://doi.org/10.1016/s0730-725x(99)00140-x.

Article  CAS  PubMed  Google Scholar 

Scheidler J, Heuck AF, Bruening R, et al. Magnetic resonance imaging of the female pelvis. New circularly polarized body array coil versus standard body coil. Invest Radiol. 1997;32(1):1–6. https://doi.org/10.1097/00004424-199701000-00001.

Article  CAS  PubMed  Google Scholar 

Mergo PJ, Engelken JD, Helmberger T, Ros PR. MRI in focal liver disease: a comparison of small and ultra-small superparamagnetic iron oxide as hepatic contrast agents. J Magn Reson Imaging. 1998;8(5):1073–8. https://doi.org/10.1002/jmri.1880080511.

Article  CAS  PubMed  Google Scholar 

Ueda T, Ohno Y, Yamamoto K, et al. Compressed sensing and deep learning reconstruction for women’s pelvic MRI denoising: utility for improving image quality and examination time in routine clinical practice. Eur J Radiol. 2021;134: 109430. https://doi.org/10.1016/j.ejrad.2020.109430.

Article  PubMed  Google Scholar 

Ueda T, Ohno Y, Yamamoto K, et al. Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging. Radiology. 2022;303(2):373–81. https://doi.org/10.1148/radiol.204097.

Article  PubMed  Google Scholar 

Kashiwagi N, Tanaka H, Yamashita Y, et al. Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI. Acta Radiol Open. 2021;10(6):20584601211023940. https://doi.org/10.1177/20584601211023939.

Article  PubMed  PubMed Central  Google Scholar 

Kaufman L, Kramer DM, Crooks LE, Ortendahl DA. Measuring signal-to-noise ratios in MR imaging. Radiology. 1989;173(1):265–7. https://doi.org/10.1148/radiology.173.1.2781018.

Article  CAS  PubMed  Google Scholar 

Price RR, Axel L, Morgan T, et al. Quality assurance methods and phantoms for magnetic resonance imaging: report of AAPM nuclear magnetic resonance task group no. 1. Med Phys. 1990;17(2):287–95. https://doi.org/10.1118/1.596566.

Article  CAS  PubMed  Google Scholar 

Dietrich O, Raya JG, Reeder SB, Reiser MF, Schoenberg SO. Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters. J Magn Reson Imaging. 2007;26(2):375–85. https://doi.org/10.1002/jmri.20969.

Article  PubMed  Google Scholar 

Goerner FL, Clarke GD. Measuring signal-to-noise ratio in partially parallel imaging MRI. Med Phys. 2011;38(9):5049–57. https://doi.org/10.1118/1.3618730.

Article  PubMed  PubMed Central  Google Scholar 

Steckner MC. A simple method for estimating the noise level in a signal region of an MR image. Med Phys. 2010;37(9):5072–9. https://doi.org/10.1118/1.3480511.

Article  PubMed  Google Scholar 

Reeder SB, Wintersperger BJ, Dietrich O, et al. Practical approaches to the evaluation of signal-to-noise ratio performance with parallel imaging: application with cardiac imaging and a 32-channel cardiac coil. Magn Reson Med. 2005;54(3):748–54. https://doi.org/10.1002/mrm.20636.

Article  PubMed  Google Scholar 

Sodickson DK, Griswold MA, Jakob PM, Edelman RR, Manning WJ. Signal-to-noise ratio and signal-to-noise efficiency in SMASH imaging. Magn Reson Med. 1999;41(5):1009–22. https://doi.org/10.1002/(sici)1522-2594(199905)41:5%3c1009::aid-mrm21%3e3.0.co;2-4.

Article  CAS  PubMed  Google Scholar 

National Electrical Manufacturers Association (NEMA). Determination of signal-to-noise ratio (SNR) in diagnostic magnetic resonance imaging. NEMA Standards Publication MS 1-2008 2008; 2018.

Google Scholar 

McCann AJ, Workman A, McGrath C. A quick and robust method for measurement of signal-to-noise ratio in MRI. Phys Med Biol. 2013;58(11):3775–90. https://doi.org/10.1088/0031-9155/58/11/3775.

Article  CAS  PubMed  Google Scholar 

Imai H, Miyati T, Ogura A, et al. Signal-to-noise ratio measurement in parallel MRI with subtraction mapping and consecutive methods. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2008;64(8):930–6. https://doi.org/10.6009/jjrt.64.930.

Article  PubMed  Google Scholar 

Miyati T, Imai H, Ogura A, et al. Novel SNR determination method in parallel MRI. Proc. SPIE 6142, Medical Imaging 2006: Physics of Medical Imaging, 61423O. (2006). https://doi.org/10.1117/12.653482

Ogura A, Miyati T, Kobayashi M, et al. Method of SNR determination using clinical images. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2007;63(9):1099–104. https://doi.org/10.6009/jjrt.63.1099.

Article  PubMed  Google Scholar 

Henkelman RM. Measurement of signal intensities in the presence of noise in MR images. Med Phys. 1985;12(2):232–3. https://doi.org/10.1118/1.595711.

Article  CAS  PubMed  Google Scholar 

Constantinides CD, Atalar E, McVeigh ER. Signal-to-noise measurements in magnitude images from NMR phased arrays. Magn Reson Med. 1997;38(5):852–7. https://doi.org/10.1002/mrm.1910380524.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn Reson Med. 1995;34(6):910–4. https://doi.org/10.1002/mrm.1910340618.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mueller-Lisse UG, Murer S, Mueller-Lisse UL, Kuhn M, Scheidler J, Scherr M. Everyman’s prostate phantom: kiwi-fruit substitute for human prostates at magnetic resonance imaging, diffusion-weighted imaging and magnetic resonance spectroscopy. Eur Radiol. 2017;27(8):3362–71. https://doi.org/10.1007/s00330-016-4706-7.

Article  Google Scholar 

Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600–12. https://doi.org/10.1109/tip.2003.819861.

Article  PubMed  Google Scholar 

Pawar K, Chen Z, Shah NJ, Egan GF. Suppressing motion artefacts in MRI using an inception-ResNet network with motion simulation augmentation. NMR Biomed. 2022;35(4):e4225. https://doi.org/10.1002/nbm.4225.

Article  PubMed  Google Scholar 

Kim T, Park JC, Gach HM, Chun J, Mutic S. Technical note: real-time 3D MRI in the presence of motion for MRI-guided radiotherapy: 3D dynamic keyhole imaging with super-resolution. Med Phys. 2019;46(10):4631–8. https://doi.org/10.1002/mp.13748.

Article  PubMed  Google Scholar 

Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9(1):62–6.

Article  Google Scholar 

Jumiawi WAH, El-Zaart A. Improvement in the between-class variance based on lognormal distribution for accurate image segmentation. Entropy. 2022;24(9): 1204. https://doi.org/10.3390/e24091204.

Article  PubMed  PubMed Central  Google Scholar 

Lloyd S. Least squares quantization in PCM. IEEE Trans Inf Theory. 1982;28(2):129–37.

Article  Google Scholar 

Fatnassi C, Zaidi H. Fast and accurate pseudo multispectral technique for whole-brain MRI tissue classification. Phys Med Biol. 2019;64(14): 145005. https://doi.org/10.1088/1361-6560/ab239e.

Article  PubMed  Google Scholar 

Kates R, Atkinson D, Brant-Zawadzki M. Fluid-attenuated inversion recovery (FLAIR): clinical prospectus of current and future applications. Top Magn Reson Imaging. 1996;8(6):389–96.

Article  CAS  PubMed  Google Scholar 

Bakshi R, Ariyaratana S, Benedict RH, Jacobs L. Fluid-attenuated inversion recovery magnetic resonance imaging detects cortical and juxtacortical multiple sclerosis lesions. Arch Neurol. 2001;58(5):742–8. https://doi.org/10.1001/archneur.58.5.742.

Article  CAS  PubMed  Google Scholar 

Billot B, Magdamo C, Cheng Y, Arnold SE, Das S, Iglesias JE. Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets. Proc Natl Acad Sci U S A. 2023;120(9): e2216399120. https://doi.org/10.1073/pnas.2216399120.

Article