Meaney, James F.

Cardiovascular pathology is the leading cause of death and disability in the Western world, and current diagnostic testing usually evaluates the anatomy of the vessel to determine if the vessel contains blockages and plaques. However, there is a growing school of thought that other measures, such as wall shear stress, provide more useful information for earlier diagnosis and prediction of atherosclerotic related disease compared to pulsed-wave Doppler ultrasound, magnetic resonance angiography, or computed tomography angiography. A novel algorithm for quantifying wall shear stress (WSS) in atherosclerotic plaque using diagnostic ultrasound imaging, called Multifrequency ultrafast Doppler spectral analysis (MFUDSA), is presented. The development of this algorithm is presented, in addition to its optimisation using simulation studies and in-vitro experiments with flow phantoms approximating the early stages of cardiovascular disease. The presented algorithm is compared with commonly used WSS assessment methods, such as standard PW Doppler, Ultrafast Doppler, and Parabolic Doppler, as well as plane-wave Doppler. Compared to an equivalent processing architecture with one-dimensional Fourier analysis, the MFUDSA algorithm provided an increase in signal-to-noise ratio (SNR) by a factor of 4–8 and an increase in velocity resolution by a factor of 1.10–1.35. The results indicated that MFUDSA outperformed the others, with significant differences detected between the typical WSS values of moderate disease progression (p = 0.003) and severe disease progression (p = 0.001). The algorithm demonstrated an improved performance for the assessment of WSS and has potential to provide an earlier diagnosis of cardiovascular disease than current techniques allow.

PurposeTo quantify the effects of DCE acquisition and pharmacokinetic modeling processing methodologies on the absolute accuracy and precision of derived pharmacokinetic (PK) parameter values using a novel anthropomorphic phantom test device in which “ground truth” values were known a priori.MethodsGround truth arterial input function (AIF), tumor, and healthy tissue contrast agent concentration‐time curves (CTCs) were established within the phantom and repeatedly measured on a 3T MRI scanner with varying temporal resolution (Tres = 1.22–30.6 s). Ground truth CTCs, Ktrans, ve, and kep values were directly compared to measured values as a function of Tres, with and without the application of voxel‐wise flip‐angle corrections applied to the data and PK modeling performed using linear and nonlinear forms of the standard Tofts model.ResultsMeasurement of the AIF was strongly affected by the Tres used (AIF curve‐shape feature errors: 3%–222% for Tres: 1.22–30.6 s), which directly translated to errors in the derived Ktrans, ve, and kep values of 1%–24%, 2%–5%, and 1%–26% respectively across this Tres range (flip‐angle correction applied). Further appreciable improvements in accuracy and precision arising from the use of flip angle corrections and nonlinear least squares fitting were quantified and used to identify optimal acquisition and analysis methodologies for which measurement errors could be constrained below threshold levels.ConclusionThis quantitative study provides insight into how errors in AIF measurement propagate to errors in PK parameter outputs. Absolute quantification of the accuracy and precision of MR‐measured CTCs, and resultant PK parameter values, allowed for an optimal temporal resolution to be defined commensurate with maintaining Ktrans, ve, and kep measurement errors below 5% and 10% levels. An appreciable gain in PK parameter estimation accuracy at the analysis stage was also demonstrated using flip‐angle corrections and a linear approach to PK model fitting.