The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.

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Phase-contrast (PC) magnetic resonance imaging (MRI) flow measurements suffer from the effect of the point spread function (PSF) due to the limited sampling of k-space. The PSF, which in this case is a sinc function, deforms the flow profile and forms a ringing pattern around the vessel. In this work, an empirical method is presented that corrects for errors due to the deformation of the flow profile. The ringing pattern is used to obtain a well-defined vessel segmentation, which after correction provides more accurate vessel radius and volume flow rate (VFR). The correction method was developed from phantom measurements at constant flow and applied on phantom measurements at moderately pulsatile flow. After correction, the error of the estimated tube radius and the VFR was less than 10% and 5%, respectively. Corresponding errors without correction overestimated the radius by 60% and the VFR by 35%. Preliminary results indicate that the method is also valid in vivo. Quantitative flow measurements using 2D phase-contrast (PC) MRI enable the noninvasive assessment of blood flow rate based on a phase shift of the magnetization obtained when a medium moves in the direction of a magnetic field gradient (1). Velocity-encoded phase images can be calculated using the phase difference of two data sets, acquired with different first-gradient moments. Provided that the flow rate is constant, the phase shift in each pixel is directly proportional to the velocity component along the direction of the magnetic field gradient (2). The volume flow rate (VFR) can be estimated by integrating the pixel values over the vessel cross section. However, the accuracy of the measured VFR is limited by object motion, intravoxel dephasing, partial volume effect, and Gibbs ringing effect caused by the limited sampling of k-space, and errors related to the selection of the region of interest (ROI) for VFR calculations in the images (3-6).Both flowing and stationary media contribute to the pixel values at the vessel boundaries. This partial volume effect has an increasing influence on the VFR as the spatial resolution decreases (3,4,7). Partial volume effects are sometimes explained by considering box-shaped voxels with uniform signal sensitivity (3,4). In reality, the voxel has the shape and sensitivity of the point spread function (PSF) of the MR acquisition.Due to the limited sampling of k-space and the resulting PSF, a Gibbs ringing effect is present in the velocity-encoded phase images. The PSF, which in this case is a sinc function, deforms the flow profile and forms a ringing pattern around the vessel. This effect increases as the spatial resolution decreases.The selection of ROI is critical for the accurate estimation of the VFR, especially in vessels the size of a few pixels. Even a small error in the ROI selection may lead to large errors in the VFR estimate (8).The aim of this work was to study the effect of limited sampling of k-space on velocity-encoded phase images, and to develop a means of correcting the effects in order to obt...