Abstract #0627

Evaluation of an accelerated Deep Learning-reconstructed T2 mapping technique through knee cartilage regional analysis using DOSMA framework

Laura Carretero Gómez^1,2, Maggie Fung³, Bruno Nunes⁴, Valentina Pedoia⁵, Sharmila Majumdar⁵, Akshay Chaudhari ⁶, Arjun Divyang Desai^6,7, Anthony Andrea Gatti⁶, Feliks Kogan⁶, and Mario Padron⁸

¹GE Healthcare, Munich, Germany, ²LAIMBIO, Rey Juan Carlos University, Madrid, Spain, ³GE Healthcare, New York, NY, United States, ⁴GE Healthcare, San Ramon, CA, United States, ⁵University of California, San Francisco, San Francisco, CA, United States, ⁶Radiology, Stanford University, Stanford, CA, United States, ⁷Electrical Engineering, Stanford University, Stanford, CA, United States, ⁸Clinica CEMTRO, Madrid, Spain

Synopsis

Keywords: Cartilage, Quantitative Imaging

The clinical translation of MRI Quantitative Imaging is still hampered by the high variability and suboptimal reproducibility of the cartilage biomarkers. The purpose of this work is to validate the consistency of a novel accelerated DL reconstructed T2 mapping technique compared to conventional reconstructed acquisition, on knee patient population. To access both femoral cartilage T2 maps, we propose a semi-automatic workflow through AI-based cartilage segmentation and regional quantification using DOSMA framework. Relaxometry analysis showed no difference between both T2 mapping techniques, implying a great step into an extensive clinical adoption.

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