Data-Driven Learning Framework for Fast Quantitative Knee Joint Mapping

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Data-Driven Learning Framework for Fast Quantitative Knee Joint Mapping

Brief description of study

The purpose of this research study is to develop and improve current 3D MRI sequences in the assessment of the human knee joint in patients with early osteoarthritis and in healthy controls. This study will utilize 3D MRI and data-driven learning-based compressed sensing (DDL-CS), to analyze the knee joint and provide a better understanding of cartilage (a type of connective tissue) and menisci (crescent-shaped bands of thick, rubbery cartilage that is attached to your shin bone) of the knee joint.

Clinical Study Identifier: s21-00710

Principal Investigator: Marcelo Victor Wust Zibetti.

Other Investigators: Steven Abramson, Gregory Chang, Ravinder R Regatte, James S Babb, Jonathan Samuels.

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