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Cited by in F6Publishing
For: 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: 1.3] [Reference Citation Analysis]
Number Citing Articles
1 Kessler DA, Mackay JW, Mcdonnell SM, Janiczek RL, Graves MJ, Kaggie JD, Gilbert FJ. Segmentation of knee MRI data with convolutional neural networks for semi-automated three-dimensional surface-based analysis of cartilage morphology and composition. Osteoarthritis Imaging 2022;2:100010. [DOI: 10.1016/j.ostima.2022.100010] [Reference Citation Analysis]
2 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;13:747S-56S. [PMID: 34496667 DOI: 10.1177/19476035211042406] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
3 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]