Magnetic Resonance Imaging (MRI) has become one of most important medical imaging tools over the past 30 years because it is non-invasive, requires no ionizing radiation, and provides exquisite images of soft tissues and anatomic structures with many tissue/disease specific contrasts. While MRI has served the community well for many years, it is increasingly clear that it also has significant limitations.

One of the principle limitations is the lack of quantitative information for tissue/structure characterization. The current paradigm of MRI is to use a set of scanner settings to generate an image "weighted" by a specific MR contrast mechanism (physical parameter), where it is hoped that variations in the parameter will be accentuated. However, without quantitative knowledge of the parameters, the final image contrast may depend on many factors, which complicates image interpretation and diagnostic performance. Quantitative measurement can provide a great deal of information about tissue properties and pathological conditions, since these parameters ultimately determine the contrast that is observed in conventional images.

The purpose of this study is to evaluate novel quantitative MRI techniques in clinical studies to determine whether they can provide better, faster and more useful information for clinical diagnosis. In this study, the investigators propose to apply MR Fingerprinting and evaluate its performance for different diseases. These include, but are not limited to, diseases in the liver, kidney, cardiac, pancreas etc. The investigators hypothesize that the quantitative MR Fingerprinting imaging technique will lead to improved tissue characterization and diagnosis.

Inclusion Criteria:

• The study will include English-speaking patients that are already scheduled to undergo a clinical MRI for diagnostic purposes.

Exclusion Criteria:

• Pregnant women will be excluded.
• Women will be excluded from the healthy volunteers, due to a lack of funding for pregnancy testing. Funding for this will be considered for the future.

Deidentified individual data that supports the results will be shared beginning 9 to 36 months following publication provided the investigator who proposes to use the data has approval from an Institutional Review Board (IRB), Independent Ethics Committee (IEC), or Research Ethics Board (REB), as applicable, and executes a data use/sharing agreement with UNC.

9 to 36 months following publication

The investigator who proposes to use the data has approval from an Institutional Review Board (IRB), Independent Ethics Committee (IEC), or Research Ethics Board (REB), as applicable, and executes a data use/sharing agreement with UNC.

AEs were collected from the beginning of the MRI procedure to up to 1 hour after.