A validated patient-specific FSI model for vascular access in haemodialysis.

The flow rate inside arteriovenous fistulas is many times higher than physiological flow and is accompanied by high wall shear stress resulting in low patency rates. A fluid-structure interaction finite element model is developed to analyse the blood flow and vessel mechanics to elucidate the mechanisms that can lead to failure. The simulations are validated against flow measurements obtained from magnetic resonance imaging data.

Preoperative three-dimensional model creation of magnetic resonance brain images as a tool to assist neurosurgical planning.

BACKGROUND: Neurosurgeons regularly plan their surgery using magnetic resonance imaging (MRI) images, which may show a clear distinction between the area to be resected and the surrounding healthy brain tissue depending on the nature of the pathology. However, this distinction is often unclear with the naked eye during the surgical intervention, and it may be difficult to infer depth and an accurate volumetric interpretation from a series of MRI image slices. OBJECTIVES: In this work, MRI data are used to create affordable patient-specific 3-dimensional (3D) scale models of the brain which clearly indicate the location and extent of a tumour relative to brain surface features and important adjacent structures. METHODS: This is achieved using custom software and rapid prototyping. In addition, functionally eloquent areas identified using functional MRI are integrated into the 3D models. RESULTS: Preliminary in vivo results are presented for 2 patients. The accuracy of the technique was estimated both theoretically and by printing a geometrical phantom, with mean dimensional errors of less than 0.5 mm observed. CONCLUSIONS: This may provide a practical and cost-effective tool which can be used for training, and during neurosurgical planning and intervention.

Tracking myocardial motion from cine DENSE images using spatiotemporal phase unwrapping and temporal fitting.

Displacement encoding with stimulated echoes (DENSE) encodes myocardial tissue displacement into the phase of the MR image. Cine DENSE allows for rapid quantification of myocardial displacement at multiple cardiac phases through the majority of the cardiac cycle. For practical sensitivities to motion, relatively high displacement encoding frequencies are used and phase wrapping typically occurs. In order to obtain absolute measures of displacement, a two-dimensional (2-D) quality-guided phase unwrapping algorithm was adapted to unwrap both spatially and temporally. Both a fully automated algorithm and a faster semi-automated algorithm are proposed. A method for computing the 2-D trajectories of discrete points in the myocardium as they move through the cardiac cycle is introduced. The error in individual displacement measurements is reduced by fitting a time series to sequential displacement measurements along each trajectory. This improvement is in turn reflected in strain maps, which are derived directly from the trajectories. These methods were validated both in vivo and on a rotating phantom. Further measurements were made to optimize the displacement encoding frequency and to estimate the baseline strain noise both on the phantom and in vivo. The fully automated phase unwrapping algorithm was successful for 767 out of 800 images (95.9%), and the semi-automated algorithm was successful for 786 out of 800 images (98.3%). The accuracy of the tracking algorithm for typical cardiac displacements on a rotating phantom is 0.24 +/- 0.15 mm. The optimal displacement encoding frequency is in the region of 0.1 cycles/mm, and, for 2 scans of 17-s duration, the strain noise after temporal fitting was estimated to be 2.5 +/- 3.0% at end-diastole, 3.1 +/- 3.1% at end-systole, and 5.3 +/- 5.0% in mid-diastole. The improvement in intra-myocardial strain measurements due to temporal fitting is apparent in strain histograms, and also in identifying regions of dysfunctional myocardium in studies of patients with infarcts.