Calculation of the modulation transfer function (MTF) is a multi-step procedure. At each step in the calculation, the algorithms can have intrinsic errors which are independent of the imaging system or physics. We designed a software tool with a graphical user interface to facilitate calculation of MTF and the analysis of accuracy in those calculations. To minimize the source of errors, simulated edge images without any noise or artifacts were used. We first examined the accuracy of a commonly used edge-slope estimation algorithm; namely line-by-line differentiation followed by a linear regression fit. The influence of edge length and edge phase on the linear regression algorithm is demonstrated. Furthermore, the relationship of edge-slope estimation error and MTF error are illustrated. We compared the performance of two kernels, [-1,1] and [-1,0,1], in the computation of the line spread function (LSF) from finite element differentiation of the edge spread function (ESF). We found that there is no practical advantage in choosing the [-1,0,1] kernel, as recommended by IEC. However, a correction for finite element differentiation should be applied; otherwise, there is a measurable error in the MTF. Finally, we added noise into the edge images and compared the performance of two noise reduction methods on the ESF; convolution with a boxcar kernel and a monotonicity constraint. The former method always produces MTF error higher than 4% up to the sampling frequency, while the latter was consistently less than 1%.
Endothelial dysfunction in response to vasoactive stimuli is closely associated with diseases such as atherosclerosis, hypertension and congestive heart failure. The current method of using ultrasound to image the brachial artery along the longitudinal axis is insensitive for measuring the small vasodilatation that occurs in response to flow mediation. The goal of this study is to overcome this limitation by using cross-sectional imaging of the brachial artery in conjunction with the User-Guided Automated Boundary Detection (UGABD) algorithm for extracting arterial boundaries.
High-resolution ultrasound imaging was performed on rigid plastic tubing, on elastic rubber tubing phantoms with steady and pulsatile flow, and on the brachial artery of a healthy volunteer undergoing reactive hyperemia. The area of cross section of time-series images was analyzed by UGABD by propagating the boundary from one frame to the next. The UGABD results were compared by linear correlation with those obtained by manual tracing.
UGABD measured the cross-sectional area of the phantom tubing to within 5% of the true area. The algorithm correctly detected pulsatile vasomotion in phantoms and in the brachial artery. A comparison of area measurements made using UGABD with those made by manual tracings yielded a correlation of 0.9 and 0.8 for phantoms and arteries, respectively. The peak vasodilatation due to reactive hyperemia was two orders of magnitude greater in pixel count than that measured by longitudinal imaging.
Cross-sectional imaging is more sensitive than longitudinal imaging for measuring flow-mediated dilatation of brachial artery, and thus may be more suitable for evaluating endothelial dysfunction.