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For: Chaudhari AS, Kogan F, Pedoia V, Majumdar S, Gold GE, Hargreaves BA. Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis. J Magn Reson Imaging 2020;52:1321-39. [PMID: 31755191 DOI: 10.1002/jmri.26991] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 6.3] [Reference Citation Analysis]
Number Citing Articles
1 Oeding JF, Williams RJ, Nwachukwu BU, Martin RK, Kelly BT, Karlsson J, Camp CL, Pearle AD, Ranawat AS, Pareek A. A practical guide to the development and deployment of deep learning models for the Orthopedic surgeon: part I. Knee Surg Sports Traumatol Arthrosc 2022. [DOI: 10.1007/s00167-022-07239-1] [Reference Citation Analysis]
2 Wirth W, Ladel C, Maschek S, Wisser A, Eckstein F, Roemer F. Quantitative measurement of cartilage morphology in osteoarthritis: current knowledge and future directions. Skeletal Radiol 2022. [DOI: 10.1007/s00256-022-04228-w] [Reference Citation Analysis]
3 Barbieri M, Chaudhari AS, Moran CJ, Gold GE, Hargreaves BA, Kogan F. A method for measuring B0 field inhomogeneity using quantitative double-echo in steady-state. Magn Reson Med 2022. [PMID: 36161727 DOI: 10.1002/mrm.29465] [Reference Citation Analysis]
4 Schmidt AM, Desai AD, Watkins LE, Crowder HA, Black MS, Mazzoli V, Rubin EB, Lu Q, Mackay JW, Boutin RD, Kogan F, Gold GE, Hargreaves BA, Chaudhari AS. Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry. Magnetic Resonance Imaging. [DOI: 10.1002/jmri.28365] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Zhou X, Shen X, Abdulhay E. MRI Semi-Quantitative Evaluation of Clinical Features of Cartilage Injury in Patients with Osteoarthritis. Concepts in Magnetic Resonance Part A 2022;2022:1-10. [DOI: 10.1155/2022/9057181] [Reference Citation Analysis]
6 Hu Y, Tang J, Zhao S, Li Y, Hussein AF. Deep Learning-Based Multimodal 3 T MRI for the Diagnosis of Knee Osteoarthritis. Computational and Mathematical Methods in Medicine 2022;2022:1-13. [DOI: 10.1155/2022/7643487] [Reference Citation Analysis]
7 Paz A, Orozco GA, Korhonen RK, García JJ, Mononen ME. Expediting Finite Element Analyses for Subject-Specific Studies of Knee Osteoarthritis: A Literature Review. Applied Sciences 2021;11:11440. [DOI: 10.3390/app112311440] [Reference Citation Analysis]
8 Calivà F, Namiri NK, Dubreuil M, Pedoia V, Ozhinsky E, Majumdar S. Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging. Nat Rev Rheumatol 2021. [PMID: 34848883 DOI: 10.1038/s41584-021-00719-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
9 Yeoh PSQ, Lai KW, Goh SL, Hasikin K, Hum YC, Tee YK, Dhanalakshmi S. Emergence of Deep Learning in Knee Osteoarthritis Diagnosis. Comput Intell Neurosci 2021;2021:4931437. [PMID: 34804143 DOI: 10.1155/2021/4931437] [Cited by in Crossref: 4] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
10 Sveinsson B, Chaudhari AS, Zhu B, Koonjoo N, Torriani M, Gold GE, Rosen MS. Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network. Radiol Artif Intell 2021;3:e200122. [PMID: 34617020 DOI: 10.1148/ryai.2021200122] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
11 Thomas KA, Krzemiński D, Kidziński Ł, Paul R, Rubin EB, Halilaj E, Black MS, Chaudhari A, Gold GE, Delp SL. Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning. Cartilage 2021;:19476035211042406. [PMID: 34496667 DOI: 10.1177/19476035211042406] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Said O, Schock J, Abrar DB, Schad P, Kuhl C, Nolte T, Knobe M, Prescher A, Truhn D, Nebelung S. In-Situ Cartilage Functionality Assessment Based on Advanced MRI Techniques and Precise Compartmental Knee Joint Loading through Varus and Valgus Stress. Diagnostics (Basel) 2021;11:1476. [PMID: 34441410 DOI: 10.3390/diagnostics11081476] [Reference Citation Analysis]
13 Freitas AC, Gaspar AS, Sousa I, Teixeira RPAG, Hajnal JV, Nunes RG. Improving B 1 + parametric estimation in the brain from multispin-echo sequences using a fusion bootstrap moves solver. Magn Reson Med 2021. [PMID: 34231250 DOI: 10.1002/mrm.28878] [Reference Citation Analysis]
14 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: 14.0] [Reference Citation Analysis]
15 Yu C, Zhao B, Li Y, Zang H, Li L. Vibrational Spectroscopy in Assessment of Early Osteoarthritis-A Narrative Review. Int J Mol Sci 2021;22:5235. [PMID: 34063436 DOI: 10.3390/ijms22105235] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
16 Jang H, Ma Y, Carl M, Jerban S, Chang EY, Du J. Ultrashort echo time Cones double echo steady state (UTE-Cones-DESS) for rapid morphological imaging of short T2 tissues. Magn Reson Med 2021;86:881-92. [PMID: 33755258 DOI: 10.1002/mrm.28769] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
17 Desai AD, Caliva F, Iriondo C, Mortazi A, Jambawalikar S, Bagci U, Perslev M, Igel C, Dam EB, Gaj S, Yang M, Li X, Deniz CM, Juras V, Regatte R, Gold GE, Hargreaves BA, Pedoia V, Chaudhari AS; IWOAI Segmentation Challenge Writing Group. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Radiol Artif Intell 2021;3:e200078. [PMID: 34235438 DOI: 10.1148/ryai.2021200078] [Cited by in Crossref: 17] [Cited by in F6Publishing: 20] [Article Influence: 17.0] [Reference Citation Analysis]
18 Fritz RC, Chaudhari AS, Boutin RD. Preoperative MRI of Articular Cartilage in the Knee: A Practical Approach. J Knee Surg 2020;33:1088-99. [PMID: 33124010 DOI: 10.1055/s-0040-1716719] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
19 Liu F. Improving Quantitative Magnetic Resonance Imaging Using Deep Learning. Semin Musculoskelet Radiol 2020;24:451-9. [PMID: 32992372 DOI: 10.1055/s-0040-1709482] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Davis DL. Editorial Comment on "Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement". AJR Am J Roentgenol 2021;216:1625. [PMID: 32903055 DOI: 10.2214/AJR.20.24431] [Reference Citation Analysis]
21 Chaudhari AS, Sandino CM, Cole EK, Larson DB, Gold GE, Vasanawala SS, Lungren MP, Hargreaves BA, Langlotz CP. Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. J Magn Reson Imaging 2021;54:357-71. [PMID: 32830874 DOI: 10.1002/jmri.27331] [Cited by in Crossref: 15] [Cited by in F6Publishing: 20] [Article Influence: 7.5] [Reference Citation Analysis]
22 Fürst D, Wirth W, Chaudhari A, Eckstein F. Layer-specific analysis of femorotibial cartilage t2 relaxation time based on registration of segmented double echo steady state (dess) to multi-echo-spin-echo (mese) images. MAGMA 2020;33:819-28. [PMID: 32458188 DOI: 10.1007/s10334-020-00852-6] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]