4-D time-resolved velocity-encoded phase-contrast MRI (4-D PCI) is a fully non-invasive technique to assess hemodynamics in vivo with a broad range of potential applications in multiple cardiovascular diseases. It is capable of providing quantitative flow values and anatomical information simultaneously. The long acquisition time, however, still inhibits its wider clinical use. Acceleration is achieved at present using parallel MRI (pMRI) techniques which can lead to substantial loss of image quality for higher acceleration factors. Both the high-dimensionality and the significant degree of spatio-temporal correlation in 4-D PCI render it ideally suited for recently proposed compressed sensing (CS) techniques. We propose the Multi-Dimensional Flow-preserving Compressed Sensing (MuFloCoS) method to exploit these properties. A multi-dimensional iterative reconstruction is combined with an interleaved sampling pattern (I-VT), an adaptive masked and weighted temporal regularization (TMW) and fully automatically obtained vessel-masks. The performance of the novel method was analyzed concerning image quality, feasibility of acceleration factors up to 15, quantitative flow values and diagnostic accuracy in phantom experiments and an in vivo carotid study with 18 volunteers. Comparison with iterative state-of-the-art methods revealed significant improvements using the new method, the temporal normalized root mean square error of the peak velocity was reduced by 45.32% for the novel MuFloCoS method with acceleration factor 9. The method was furthermore applied to two patient cases with diagnosed high-grade stenosis of the ICA, which confirmed the performance of MuFloCoS to produce valuable results in the presence of pathological findings in 56 s instead of over 8 min (full sampling).

Many flow visualization techniques, especially integration-based methods, are problematic when the measured data exhibit noise and discretization issues. Particularly, this is the case for flow-sensitive phase-contrast magnetic resonance imaging (PC-MRI) data sets which not only record anatomic information, but also time-varying flow information. We propose a novel approach for the visualization of such data sets using integration-based methods. Our ideas are based upon finite-time Lyapunov exponents (FTLE) and enable identification of vessel boundaries in the data as high regions of separation. This allows us to correctly restrict integration-based visualization to blood vessels. We validate our technique by comparing our approach to existing anatomy-based methods as well as addressing the benefits and limitations of using FTLE to restrict flow. We also discuss the importance of parameters, i.e., advection length and data resolution, in establishing a well-defined vessel boundary. We extract appropriate flow lines and surfaces that enable the visualization of blood flow within the vessels. We further enhance the visualization by analyzing flow behavior in the seeded region and generating simplified depictions.