BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Chaudhari AS, Grissom MJ, Fang Z, Sveinsson B, Lee JH, Gold GE, Hargreaves BA, Stevens KJ. Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement. American Journal of Roentgenology 2021;216:1614-25. [DOI: 10.2214/ajr.20.24172] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 Barbieri M, Chaudhari AS, Moran CJ, Gold GE, Hargreaves BA, Kogan F. A method for measuring B(0) field inhomogeneity using quantitative double-echo in steady-state. Magn Reson Med 2023;89:577-93. [PMID: 36161727 DOI: 10.1002/mrm.29465] [Reference Citation Analysis]
2 Lin DJ, Walter SS, Fritz J. Artificial Intelligence-Driven Ultra-Fast Superresolution MRI: 10-Fold Accelerated Musculoskeletal Turbo Spin Echo MRI Within Reach. Invest Radiol 2023;58:28-42. [PMID: 36355637 DOI: 10.1097/RLI.0000000000000928] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Kijowski R, Fritz J. Emerging Technology in Musculoskeletal MRI and CT. Radiology 2022. [DOI: 10.1148/radiol.220634] [Reference Citation Analysis]
4 Koh DM, Papanikolaou N, Bick U, Illing R, Kahn CE Jr, Kalpathi-Cramer J, Matos C, Martí-Bonmatí L, Miles A, Mun SK, Napel S, Rockall A, Sala E, Strickland N, Prior F. Artificial intelligence and machine learning in cancer imaging. Commun Med (Lond) 2022;2:133. [PMID: 36310650 DOI: 10.1038/s43856-022-00199-0] [Reference Citation Analysis]
5 Son H, Lee S, Kim K, Koo K, Hwang CH. Deep learning-based quantitative estimation of lymphedema-induced fibrosis using three-dimensional computed tomography images. Sci Rep 2022;12. [DOI: 10.1038/s41598-022-19204-6] [Reference Citation Analysis]
6 Liu J, Yang X, Liao T, Huang Y, Che H. Detection Method of Athlete Joint Injury Based on Deep Learning Model. Computational and Mathematical Methods in Medicine 2022;2022:1-11. [DOI: 10.1155/2022/8165580] [Reference Citation Analysis]
7 Li J, Qian K, Liu J, Huang Z, Zhang Y, Zhao G, Wang H, Li M, Liang X, Zhou F, Yu X, Li L, Wang X, Yang X, Jiang Q. Identification and diagnosis of meniscus tear by magnetic resonance imaging using a deep learning model. Journal of Orthopaedic Translation 2022;34:91-101. [DOI: 10.1016/j.jot.2022.05.006] [Reference Citation Analysis]
8 Li J, Qian K, Liu J, Huang Z, Zhang Y, Zhao G, Wang H, Li M, Liang X, Zhou F, Yu X, Li L, Wang X, Yang X, Jiang Q. The rapid identification and diagnosis of meniscus tear by Magnetic Resonance Imaging using a deep learning model.. [DOI: 10.1101/2022.01.11.22269112] [Reference Citation Analysis]
9 Gokyar S, Robb FJL, Kainz W, Chaudhari A, Winkler SA. MRSaiFE: An AI-based Approach Towards the Real-Time Prediction of Specific Absorption Rate. IEEE Access 2021;9:140824-34. [PMID: 34722096 DOI: 10.1109/access.2021.3118290] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
10 Hahn S, Yi J, Lee HJ, Lee Y, Lim YJ, Bang JY, Kim H, Lee J. Image Quality and Diagnostic Performance of Accelerated Shoulder MRI With Deep Learning-Based Reconstruction. AJR Am J Roentgenol 2021. [PMID: 34523950 DOI: 10.2214/AJR.21.26577] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
11 Chaudhari AS, Mittra E, Davidzon GA, Gulaka P, Gandhi H, Brown A, Zhang T, Srinivas S, Gong E, Zaharchuk G, Jadvar H. Low-count whole-body PET with deep learning in a multicenter and externally validated study. NPJ Digit Med 2021;4:127. [PMID: 34426629 DOI: 10.1038/s41746-021-00497-2] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
12 Rosenkrantz AB. Editor's Notebook: June 2021. AJR Am J Roentgenol 2021;216:1409-10. [PMID: 34019460 DOI: 10.2214/AJR.21.25770] [Reference Citation Analysis]
13 Li MD, Chang CY. Beyond the AJR: "Machine-Learning, MRI Bone Shape and Important Clinical Outcomes in Osteoarthritis: Data From the Osteoarthritis Initiative". AJR Am J Roentgenol 2021;217:522. [PMID: 33438456 DOI: 10.2214/AJR.20.25413] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]