Innovations in MRI and AI Integration for Vascular Plaque Evaluation and Overview of Deep Learning Techniques in Peripheral Vascular Disease
References
- Zhang J, Han R, Shao G, Lv B, Sun K. Artificial Intelligence in Cardiovascular Atherosclerosis Imaging. J Pers Med. 2022 Mar 8;12(3):
420 . doi: 10.3390/jpm12030420 - Saba L, Sanagala SS, Gupta SK, et al. Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application. Ann Transl Med. 2021 Jul;9(14):
1206 . doi: 10.21037/atm-20-7676 - Choudhury RP, Fuster V, Badimon JJ, Fisher EA, Fayad ZA. MRI and characterization of atherosclerotic plaque: emerging applications and molecular imaging. Arterioscler Thromb Vasc Biol. 2002 Jul 1;22(7):1065-74. doi: 10.1161/01.atv.0000019735.54479.2f
- Edelman RR, Flanagan O, Grodzki D, Giri S, Gupta N, Koktzoglou I. Projection MR imaging of peripheral arterial calcifications. Magn Reson Med. 2015 May;73(5):1939-45. doi: 10.1002/mrm.25320
- Roy TL, Forbes TL, Dueck AD, Wright GA. MRI for peripheral artery disease: Introductory physics for vascular physicians. Vasc Med. 2018 Apr;23(2):153-162. doi: 10.1177/1358863X18759826
- Ross R. Atherosclerosis--an inflammatory disease. N Engl J Med. 1999 Jan 14;340(2):115-26. doi: 10.1056/NEJM199901143400207
- Criqui MH, Aboyans V. Epidemiology of peripheral artery disease. Circ Res. 2015 Apr 24;116(9):1509-26. doi:
10.1161/CIRCRESAHA.116.303849 . Erratum in: Circ Res. 2015 Jun 19;117(1):e12. doi: 10.1161/RES.0000000000000059 - St Hilaire C. Medial Arterial Calcification: A Significant and Independent Contributor of Peripheral Artery Disease. Arterioscler Thromb Vasc Biol. 2022 Mar;42(3):253-260. doi: 10.1161/ATVBAHA.121.316252
- Ho CY, Shanahan CM. Medial Arterial Calcification: An Overlooked Player in Peripheral Arterial Disease. Arterioscler Thromb Vasc Biol. 2016 Aug;36(8):1475-82. doi: 10.1161/ATVBAHA.116.306717
- Gerhard-Herman MD, Gornik HL, Barrett C, et al. 2016 AHA/ACC Guideline on the Management of Patients With Lower Extremity Peripheral Artery Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2017 Mar 21;135(12):e726-e779. doi:
10.1161/CIR.0000000000000471 . Erratum in: Circulation. 2017 Mar 21;135(12):e791-e792. doi: 10.1161/CIR.0000000000000502 - Cole DA, Fox BR, Peña CS. The Role of Imaging in Peripheral Interventions. Tech Vasc Interv Radiol. 2022 Sep;25(3):
100836 . doi: 10.1016/j.tvir.2022.100836 - Makris GC, Chrysafi P, Little M, et al. The role of intravascular ultrasound in lower limb revascularization in patients with peripheral arterial disease. Int Angiol. 2017 Dec;36(6):505-516. doi: 10.23736/S0392-9590.17.03866-4
- Pollak AW, Norton PT, Kramer CM. Multimodality imaging of lower extremity peripheral arterial disease: current role and future directions. Circ Cardiovasc Imaging. 2012 Nov;5(6):797-807. doi: 10.1161/CIRCIMAGING.111.970814
- Chan D, Anderson ME, Dolmatch BL. Imaging evaluation of lower extremity infrainguinal disease: role of the noninvasive vascular laboratory, computed tomography angiography, and magnetic resonance angiography. Tech Vasc Interv Radiol. 2010 Mar;13(1):11-22. doi: 10.1053/j.tvir.2009.10.003
- Csore J, Drake M, Roy TL. Peripheral arterial disease treatment planning using noninvasive and invasive imaging methods. J Vasc Surg Cases Innov Tech. 2023 Aug 19;9(4):
101263 . doi: 10.1016/j.jvscit.2023.101263 - Ghibes P, Hagen F, Weissinger M, et al. Diagnostic performance of Photon-counting CT angiography in peripheral artery disease compared to DSA as gold standard. Eur J Radiol. 2025 Jan;182:
111834 . doi: 10.1016/j.ejrad.2024.111834 - Hosadurg N, Kramer CM. Magnetic Resonance Imaging Techniques in Peripheral Arterial Disease. Adv Wound Care (New Rochelle). 2023 Nov;12(11):611-625. doi: 10.1089/wound.2022.0161
- Cavallo AU, Koktzoglou I, Edelman RR, et al. Noncontrast Magnetic Resonance Angiography for the Diagnosis of Peripheral Vascular Disease. Circ Cardiovasc Imaging. 2019 May;12(5):
e008844 . doi: 10.1161/CIRCIMAGING.118.008844 - Henningsson M, Malik S, Botnar R, Castellanos D, Hussain T, Leiner T. Black-Blood Contrast in Cardiovascular MRI. J Magn Reson Imaging. 2022 Jan;55(1):61-80. doi: 10.1002/jmri.27399
- Pollak AW, Kramer CM. MRI in Lower Extremity Peripheral Arterial Disease: Recent Advancements. Curr Cardiovasc Imaging Rep. 2013 Feb 1;6(1):55-60. doi: 10.1007/s12410-012-9175-z
- Goffart S, Delingette H, Chierici A, et al. Artificial Intelligence Techniques for Prognostic and Diagnostic Assessments in Peripheral Artery Disease: A Scoping Review. Angiology. 2025 Jan 17:
33197241310572 . doi: 10.1177/00033197241310572 - Kampaktsis PN, Emfietzoglou M, Al Shehhi A, et al. Artificial intelligence in atherosclerotic disease: Applications and trends. Front Cardiovasc Med. 2023 Jan 19;9:
949454 . doi: 10.3389/fcvm.2022.949454 - Flores AM, Demsas F, Leeper NJ, Ross EG. Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes. Circ Res. 2021 Jun 11;128(12):1833-1850. doi: 10.1161/CIRCRESAHA.121.318224
- Dossabhoy SS, Ho VT, Ross EG, Rodriguez F, Arya S. Artificial intelligence in clinical workflow processes in vascular surgery and beyond. Semin Vasc Surg. 2023 Sep;36(3):401-412. doi: 10.1053/j.semvascsurg.2023.07.002
- Perez S, Thandra S, Mellah I, Kraemer L, Ross E. Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease. Curr Cardiovasc Risk Rep. 2024;18(12):187-195. doi: 10.1007/s12170-024-00752-7
- Li B, Feridooni T, Cuen-Ojeda C, et al. Machine learning in vascular surgery: a systematic review and critical appraisal. NPJ Digit Med. 2022 Jan 19;5(1):
7 . doi: 10.1038/s41746-021-00552-y - Butova X, Shayakhmetov S, Fedin M, Zolotukhin I, Gianesini S. Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management. J Pers Med. 2021 Dec 2;11(12):
1280 . doi: 10.3390/jpm11121280 - Javidan AP, Li A, Lee MH, Forbes TL, Naji F. A Systematic Review and Bibliometric Analysis of Applications of Artificial Intelligence and Machine Learning in Vascular Surgery. Ann Vasc Surg. 2022 Sep;85:395-405. doi: 10.1016/j.avsg.2022.03.019
- Jie P, Fan M, Zhang H, et al. Diagnostic value of artificial intelligence-assisted CTA for the assessment of atherosclerosis plaque: a systematic review and meta-analysis. Front Cardiovasc Med. 2024 Sep 3;11:
1398963 . doi: 10.3389/fcvm.2024.1398963 - Dai L, Zhou Q, Zhou H, et al. Deep learning-based classification of lower extremity arterial stenosis in computed tomography angiography. Eur J Radiol. 2021 Mar;136:
109528 . doi: 10.1016/j.ejrad.2021.109528 - arXiv [Internet]. New York, NY:
Cornell University ; c2025. Tan M, Le Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning;2019 May 28 [cited 2025 Aug 24]. Available from:https://arxiv.org/abs/1905.11946 - Mistelbauer G, Morar A, Schernthaner R, et al. Semi-automatic vessel detection for challenging cases of peripheral arterial disease. Comput Biol Med. 2021 Jun;133:
104344 . doi: 10.1016/j.compbiomed.2021.104344 - Luo X, Ara L, Ding H, Rollins D, Motaganahalli R, Sawchuk AP. Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies. J Vasc Surg. 2021 Sep;74(3):988-996.e1. doi: 10.1016/j.jvs.2021.02.050
- von Haxthausen F, Hagenah J, Kaschwich M, Kleemann M, García-Vázquez V, Floris E. Robotized ultrasound imaging of the peripheral arteries – a phantom study. Curr Dir Biomed Eng. 2020 Sep;6(1):
20200033 . doi: 10.1515/cdbme-2020-0033 - Jiang B, Chen A, Bharat S, Zheng M. Automatic Ultrasound Vessel Segmentation with Deep Spatiotemporal Context Learning. In: Noble, JA, Aylward, S, Grimwood, A, Min, Z, Lee, SL, Hu, Y, editors. Simplifying Medical Ultrasound. 26th International Conference on Medical Image Computing and Computer-Assisted Intervention.
Oct 8, 2023 ; Vancouver, BC:Springer Nature . - Khagi B, Belousova T, Short CM, et al. A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging. Magn Reson Imaging. 2024 Feb;106:31-42. doi: 10.1016/j.mri.2023.11.014
- Khagi B, Belousova T, Short CM, et al. Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging-Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease. Am J Cardiol. 2024 Jun 1;220:56-66. doi: 10.1016/j.amjcard.2024.03.035
- Khagi B, Belousova T, Short CM, et al. Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease. Sci Rep. 2025 Feb 10;15(1):
4996 . doi: 10.1038/s41598-025-87747-5 - Zhang JL, Conlin CC, Li X, et al. Exercise-induced calf muscle hyperemia: Rapid mapping of magnetic resonance imaging using deep learning approach. Physiol Rep. 2020 Aug;8(16):
e14563 . doi: 10.14814/phy2.14563 - Csore J, Drake M, Karmonik C, et al. Employing magnetic resonance histology for precision chronic limb-threatening ischemia treatment planning. J Vasc Surg. 2025 Feb;81(2):351-363.e3. doi: 10.1016/j.jvs.2024.08.054
- Roy TL, Chen HJ, Dueck AD, Wright GA. Magnetic resonance imaging characteristics of lesions relate to the difficulty of peripheral arterial endovascular procedures. J Vasc Surg. 2018 Jun;67(6):1844-1854.e2. doi: 10.1016/j.jvs.2017.09.034
- Roy T, Liu G, Shaikh N, Dueck AD, Wright GA. Puncturing Plaques. J Endovasc Ther. 2017 Feb;24(1):35-46. doi: 10.1177/1526602816671135
- Csore J, Karmonik C, Wilhoit K, Buckner L, Roy TL. Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility Study. Diagnostics (Basel). 2023 May 31;13(11):
1925 . doi: 10.3390/diagnostics13111925 - Csore J, Roy TL, Wright G, Karmonik C. Unsupervised classification of multi-contrast magnetic resonance histology of peripheral arterial disease lesions using a convolutional variational autoencoder with a Gaussian mixture model in latent space: A technical feasibility study. Comput Med Imaging Graph. 2024 Jul;115:
102372 . doi: 10.1016/j.compmedimag.2024.102372
DOI: https://doi.org/10.14797/mdcvj.1642 | Journal eISSN: 1947-6108
Language: English
Page range: 71 - 80
Submitted on: May 22, 2025
Accepted on: Aug 13, 2025
Published on: Oct 7, 2025
Published by: Houston Methodist DeBakey Heart & Vascular Center
In partnership with: Paradigm Publishing Services
Keywords:
© 2025 Eniko Pomozi, Carlos Quintero-Peña, Judit Csore, Alexander Crichton, Guangyu Wang, Christof Karmonik, Trisha Roy, published by Houston Methodist DeBakey Heart & Vascular Center
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.