Abstract #4519

Reduced input combination study for the Simultaneous Multi-Tissue Segmentation and Multi-Parameter Quantification Network (MSMQ-Net) of Knee

Xing Lu¹, Yajun Ma¹, Jiyo Athertya¹, Chun-Nan Hsu², Eric Y Chang^1,3, Amilcare Gentili^1,3, Christine Chung¹, and Jiang Du¹

¹Department of Radiology, University of California, San Diego, San Diego, CA, United States, ²Department of Neurosciences, University of California, San Diego, San Diego, CA, United States, ³Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States

Synopsis

Keywords: Osteoarthritis, Quantitative ImagingTo accelerate ultrashort echo time (UTE) based multi-parameter quantitative MRI (qMRI), we performed comparison studies on the input MRI image numbers and the effects on the prediction quality of the MSMQ-Net. The MSMQ-net was modified and trained accordingly with different combinations of inputs. Both image similarity and regional analysis were evaluated. The results demonstrate that 90% accuracy can be achieved for both UTE-T1 and UTE-T1rho mapping when the scan time is reduced by 75%.

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