Current MRI measurements have the disadvantage that they are over-sampling during data acquisition, which leads to unnecessary long measuring times with relatively low information density of the obtained data, thereby hindering the detection of sparse data within the obtained big data collections. Another complication of current MRI measurements is the fact that the contrast between for instance healthy and malignant tissue can be small or even absent. In order to address the first issue, the phasor analysis approach will be introduced for MRI image analysis. This new way of analyzing MRI data will contribute to the ongoing effort to speed up MRI experiments by many modern concepts in acquisition, post-processing and visualization, like MR fingerprinting, parallel imaging, sparse sampling, and computer aided analysis. It also allows running more powerful data acquisition schemes to obtain an extended amount of information within the same measuring time. Current medical MRI techniques are mostly based on simplified models to describe soft matter characteristics of tissue. An example is Magnetic Resonance Elastometry (MRE), the imaging-based counterpart of palpation, which allows for the detection of altered mechanical properties of tissues which can be caused by many different types of disease processes. MRE however assumes ideal visco-elastic behaviour of tissue, which is known be a simplification for many clinical applications. In this track the intricate relationships between multiscale structure and dynamics of soft matter and visco-elastic functionality by MRI will be addressed. This physics-driven approach will be accompanied by the simultaneous data-driven approach based on phasor analysis.