Khan MA, Hashim MJ, Mustafa H, Baniyas MY, Al Suwaidi SKBM, AlKatheeri R, Alblooshi FMK, Almatrooshi MEAH, Alzaabi MEH, Al Darmaki RS, Lootah SNAH (2020) Global epidemiology of ischemic heart disease: results from the global burden of disease study. Cureus. https://doi.org/10.7759/CUREUS.9349
Article PubMed PubMed Central Google Scholar
Alskaf E, Dutta U, Scannell CM, Chiribiri A (2022) Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis. Inf Med Unlocked 32:101055. https://doi.org/10.1016/J.IMU.2022.101055
Greenwood JP, Herzog BA, Brown JM, Everett CC, Nixon J, Bijsterveld P, Maredia N, Motwani M, Dickinson CJ, Ball SG, Plein S (2016) Prognostic value of cardiovascular magnetic resonance and single-photon emission computed tomography in suspected coronary heart disease: long-term follow-up of a prospective, diagnostic accuracy cohort study. Ann Intern Med 165(1):1–9. https://doi.org/10.7326/M15-1801
Bazmpani MA, Nikolaidou C, Papanastasiou CA, Ziakas A, Karamitsos TD (2022) Cardiovascular magnetic resonance parametric mapping techniques for the assessment of chronic coronary syndromes. J Cardiovasc Develop Dis 9(12):443. https://doi.org/10.3390/JCDD9120443
Canet EP, Janier MF, Revel D (1999) Magnetic resonance perfusion imaging in ischemic heart disease. J Magn Reson Imaging 10:423
Article CAS PubMed Google Scholar
Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, Pennell DJ, Rumberger JA, Ryan TJ, Verani MS (2002) Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. J Cardiovasc Magn Reson 4(2):203–210. https://doi.org/10.1081/JCMR-120003946
Kim YC, Kim K, Choe YH (2023) Automatic calculation of myocardial perfusion reserve using deep learning with uncertainty quantification. Quant Imaging Med Surg 13(12):7936–7949. https://doi.org/10.21037/QIMS-23-840/COIF
Article PubMed PubMed Central Google Scholar
Hsu LY, Kellman P, Arai AE (2008) Nonlinear myocardial signal intensity correction improves quantification of contrast-enhanced first-pass MR perfusion in humans. J Magn Reson Imaging 27(4):793–801. https://doi.org/10.1002/jmri.21286
Jacobs M, Benovoy M, Chang LC, Arai AE, Hsu LY (2016) Evaluation of an automated method for arterial input function detection for first-pass myocardial perfusion cardiovascular magnetic resonance. J Cardiovasc Magn Reson. https://doi.org/10.1186/s12968-016-0239-0
Article PubMed PubMed Central Google Scholar
Scannell CM, Veta M, Villa ADM, Sammut EC, Lee J, Breeuwer M, Chiribiri A (2020) Deep-learning-based preprocessing for quantitative myocardial perfusion MRI. J Magn Reson Imaging 51(6):1689–1696. https://doi.org/10.1002/jmri.26983
van Herten RLM, Chiribiri A, Breeuwer M, Veta M, Scannell CM (2022) Physics-informed neural networks for myocardial perfusion MRI quantification. Med Image Anal. https://doi.org/10.1016/j.media.2022.102399
Article PubMed PubMed Central Google Scholar
Ishida M, Morton G, Schuster A, Nagel E, Chiribiri A (2010) Quantitative assessment of myocardial perfusion MRI. Curr Cardiovasc Imaging Rep 3(2):65–73. https://doi.org/10.1007/s12410-010-9013-0
Schwab F, Ingrisch M, Marcus R, Bamberg F, Hildebrandt K, Adrion C, Gliemi C, Nikolaou K, Reiser M, Theisen D (2015) Tracer kinetic modeling in myocardial perfusion quantification using MRI. Magn Reson Med 73(3):1206–1215. https://doi.org/10.1002/mrm.25212
Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2(3):243–260. https://doi.org/10.1016/S1361-8415(98)80022-4
Article CAS PubMed Google Scholar
Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. Neuroimage. https://doi.org/10.1016/J.NEUROIMAGE.2008.10.040
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. Lecture notes in computer science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9351, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28/COVER
Crum WR, Camara O, Hill DLG (2006) Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans Med Imaging 25(11):1451–1461. https://doi.org/10.1109/TMI.2006.880587
Kingma DP, Ba JL (2014) Adam: a method for stochastic optimization. In: 3rd International conference on learning representations, ICLR 2015 - conference track proceedings. https://arxiv.org/abs/1412.6980v9
Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual U-net. IEEE Geosci Remote Sens Lett 15(5):749–753. https://doi.org/10.1109/LGRS.2018.2802944
Campello VM, Gkontra P, Izquierdo C, Martin-Isla C, Sojoudi A, Full PM, Maier-Hein K, Zhang Y, He Z, Ma J, Parreno M, Albiol A, Kong F, Shadden SC, Acero JC, Sundaresan V, Saber M, Elattar M, Li H, Lekadir K (2021) Multi-centre, multi-vendor and multi-disease cardiac segmentation: the mms challenge. IEEE Trans Med Imaging 40(12):3543–3554. https://doi.org/10.1109/TMI.2021.3090082
Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng PA, Cetin I, Lekadir K, Camara O, Gonzalez Ballester MA, Sanroma G, Napel S, Petersen S, Tziritas G, Grinias E, Khened M, Kollerathu VA, Krishnamurthi G, Rohe MM, Jodoin PM (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514–2525. https://doi.org/10.1109/TMI.2018.2837502
Comments (0)