Artificial Intelligence and Deep Learning in Musculoskeletal Magnetic Resonance Imaging

키워드

참고문헌 (60)

1. Chartrand G, "Deep learning : a primer for radiologists", Radiographics, 37, pp.2113~2131 (2017)
2. 도신호, "Basics of Deep Learning: A Radiologist’s Guide to Understanding Published Radiology Articles on Deep Learning", Korean Journal of Radiology, 21(1), pp.33~41 (2020), 대한영상의학회[DOI]
3. 신이랑, "Artificial intelligence in musculoskeletal ultrasound imaging", ULTRASONOGRAPHY, 40(1), pp.30~44 (2021), 대한초음파의학회[DOI]
4. Lin SY, "Ten ways artificial intelligence will transform primary care", J Gen Intern Med, 34, pp.1626~1630 (2019)
5. Monshi MMA, "Deep learning in generating radiology reports : a survey", Artif Intell Med, 106, pp.101878~ (2020)
6. Mosher TJ, "Musculoskeletal imaging at 3T : current techniques and future applications", Magn Reson Imaging Clin N Am, 14, pp.63~76 (2006)
7. Peh WC, "Artifacts in musculoskeletal magnetic resonance imaging : identification and correction", Skeletal Radiol, 30, pp.179~191 (2001)
8. Singh DR, "Artifacts in musculoskeletal MR imaging", Semin Musculoskelet Radiol, 18, pp.12~22 (2014)
9. Liu F, "Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning", Radiol Artif Intell, 1, pp.180091~ (2019)
10. Germann C, "Deep Convolutional Neural Network–based diagnosis of anterior cruciate ligament tears: performance comparison of homogenous versus heterogeneous knee MRI cohorts with different pulse sequence protocols and 1.5-T and 3-T magnetic field strengths", Invest Radiol, 55, pp.499~506 (2020)
11. Zhang L, "Deep learning approach for anterior cruciate ligament lesion detection : evaluation of diagnostic performance using arthroscopy as the reference standard", J Magn Reson Imaging, 52, pp.1745~1752 (2020)
12. Chang PD, "Deep learning for detection of complete anterior cruciate ligament tear", J Digit Imaging, 32, pp.980~986 (2019)
13. Namiri NK, "Deep learning for hierarchical severity staging of anterior cruciate ligament injuries from MRI", Radiol Artif Intell, 2, pp.e190207~ (2020)
14. Astuto B, "Automatic deep learning–assisted detection and grading of abnormalities in knee MRI studies", Radiol Artif Intell, 3, pp.e200165~ (2021)
15. Bien N, "Deep-learning-assisted diagnosis for knee magnetic resonance imaging : development and retrospective validation of MRNet", PLoS Med, 15, pp.e1002699~ (2018)
16. Fritz B, "Deep convolutional neural network-based detection of meniscus tears : comparison with radiologists and surgery as standard of reference", Skeletal Radiol, 49, pp.1207~1217 (2020)
17. Roblot V, "Artificial intelligence to diagnose meniscus tears on MRI", Diagn Interv Imaging, 100, pp.243~249 (2019)
18. Pedoia V, "3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects", J Magn Reson Imaging, 49, pp.400~410 (2019)
19. Couteaux V, "Automatic knee meniscus tear detection and orientation classification with Mask-RCNN", Diagn Interv Imaging, 100, pp.235~242 (2019)
20. Irmakci I, "Deep learning for musculoskeletal image analysis", 201953rd Asilomar Conference on Signals, Systems, and Computers, pp.1481~1485 (2019), IEEE
21. Tsai CH, "Knee injury detection using MRI with efficiently-layered network (ELNet)", International Conference on Medical Imaging with Deep Learning, pp.784~794 (2020)
22. Rizk B, "Meniscal lesion detection and characterization in adult knee MRI : a deep learning model approach with external validation", Phys Med, 83, pp.64~71 (2021)
23. Yao J, "Evaluation of a deep learning method for the automated detection of supraspinatus tears on MRI", Skeletal Radiol, 51, pp.1765~1775 (2022)
24. Shim E, "Automated rotator cuff tear classification using 3D convolutional neural network", Sci Rep, 10, pp.15632~ (2020)
25. Kim M, "MRI-based diagnosis of Rotator Cuff Tears using deep learning and weighted linear combinations", MLHC 2020: Machine Learning for Healthcare Conference, pp.292~308 (2020)
26. Conze PH, "Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders", Comput Med Imaging Graph, 83, pp.101733~ (2020)
27. Ro K, "Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI", Sci Rep, 11, pp.15065~ (2021)
28. Kim JY, "Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning", Comput Methods Programs Biomed, 182, pp.105063~ (2019)
29. Medina G, "Deep learning method for segmentation of rotator cuff muscles on MR images", Skeletal Radiol, 50, pp.683~692 (2021)
30. Al-Kafri AS, "Boundary delineation of MRI images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks", IEEE Access, 7, pp.43487~43501 (2019)
31. Won D, "Spinal stenosis grading in magnetic resonance imaging using deep convolutional neural networks", Spine(Phila Pa 1976), 45, pp.804~812 (2020)
32. Han Z, "Towards automatic report generation in spine radiology using weakly supervised framework", Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pp.185~193 (2018), Springer
33. Han Z, "Automated pathogenesis-based diagnosis of lumbar neural foraminal stenosis via deep multiscale multitask learning", Neuroinformatics, 16(3-4), pp.325~337 (2018)
34. Jamaludin A, "ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017 : automation of reading of radiological features from magnetic resonance images(MRIs)of the lumbar spine without human intervention is comparable with an expert radiologist", Eur Spine J, 26, pp.1374~1383 (2017)
35. Zheng HD, "Deep learning-based highaccuracy quantitation for lumbar intervertebral disc degeneration from MRI", Nat Commun, 13, pp.841~ (2022)
36. Hallinan JTPD, "Deep learning model for automated detection and classification of central canal, lateral recess, and neural roraminal stenosis at lumbar spine MRI", Radiology, 300, pp.130~138 (2021)
37. Lim DSW, "Improved productivity using deep learning-assisted reporting for lumbar spine MRI", Radiology, 305, pp.160~166 (2022)
38. Glockner JF, "Parallel MR imaging : a user’s guide", RadioGraphics, 25, pp.1279~1297 (2005)
39. Matcuk GR Jr, "Compressed sensing MRI : technique and clinical applications", Adv Clin Radiol, 2, pp.257~271 (2020)
40. Recht MP, "Using deep learning to accelerate knee MRI at 3 T : results of an interchangeability study", AJR Am J Roentgenol, 215, pp.1421~1429 (2020)
41. Montalt-Tordera J, "Machine learning in magnetic resonance imaging : image reconstruction", Phys Med, 83, pp.79~87 (2021)
42. Chaudhari AS, "Diagnostic accuracy of quantitative multicontrast 5-minute knee MRI using prospective artificial intelligence image quality enhancement", AJR Am J Roentgenol, 216, pp.1614~1625 (2021)
43. Sun S, "Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI", Eur Radiol, 32, pp.6167~6177 (2022)
44. Hahn S, "Image quality and diagnostic performance of accelerated shoulder MRI with deep learning-based reconstruction", AJR Am J Roentgenol, 218, pp.506~516 (2022)
45. Herrmann J, "Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction : a prospective intraindividual comparison with a standard MRI protocol", Eur Radiol, 32, pp.6215~6229 (2022)
46. Lin DJ, "Artificial intelligence for MR image reconstruction : an overview for clinicians", J Magn Reson Imaging, 53, pp.1015~1028 (2021)
47. Defazio A, "MRI banding removal via adversarial training", 34th Conference on Neural Information Processing Systems (NeurIPS 2020), pp.7660~7670 (2020)
48. Sarracanie M, "Low-field MRI: how low can we go? A fresh view on an old debate", Front Phys, 8, pp.172~ (2020)
49. Ayde R, "Deep learning for fast low-field MRI acquisitions", Sci Rep, 12, pp.11394~ (2022)
50. Johnson PM, "Improving the speed of MRI with artificial intelligence", Semin Musculoskelet Radiol, 24, pp.12~20 (2020)
51. Xia Y, "Super-resolution of cardiac MR cine imaging using conditional GANs and unsupervised transfer learning", Med Image Anal, 71, pp.102037~ (2021)
52. Zheng Y, "A hybrid convolutional neural network for super-resolution reconstruction of MR images", Med Phys, 47, pp.3013~3022 (2020)
53. Shin Y, "AI musculoskeletal clinical applications : how can AI increase my day-to-day efficiency?", Skeletal Radiol, 51, pp.293~304 (2022)
54. McDonald RJ, "The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload", Acad Radiol, 22, pp.1191~1198 (2015)
55. Gyftopoulos S, "Artificial intelligence in musculoskeletal imaging : current status and future directions", AJR Am J Roentgenol, 213, pp.506~513 (2019)
56. Titano JJ, "Automated deep-neural-network surveillance of cranial images for acute neurologic events", Nat Med, 24, pp.1337~1341 (2018)
57. Shapiro L, "Advances in musculoskeletal MRI : technical considerations", J Magn Reson Imaging, 36, pp.775~787 (2012)
58. Dong C, "Learning a deep convolul network for image super-resolution", Computer Vision -- ECCV 2014 13th European Conference, pp.184~199 (2014), Springer
59. Dong C, "Image super-resolution using deep convolutional networks", IEEE Trans Pattern Anal Mach Intell, 38, pp.295~307 (2016)
60. Goodfellow IJ, "Generative adversarial nets", 28th International Conference on Neural Information Processing Systems, pp.2672~2680 (2014), MIT Press

관련 논문AI추천