Dependence of vessel area accuracy and precision as a function of MR imaging parameters and boundary detection algorithm

PURPOSE: To determine the appropriate image acquisition parameters for an accurate measurement of vessel cross-sectional area from MR angiography (MRA) images. MATERIALS AND METHODS: A series of images with different vessel cross-sectional areas, resolutions, and signal-to-noise ratios (SNRs) were simulated and validated experimentally. Dynamic programming (DP) was employed to determine the accuracy and precision of the vessel cross-sectional area as a function of vessel size, sampling matrix, acquisition time, and postprocessing parameters such as zooming and bias correction. RESULTS: We show that there is an optimal value of lambda (the ratio of vessel diameter to resolution) for a given intrinsic SNR that yields the most accurate and precise area measurement. Specifically, when the SNR is > or =10:1, this value of lambda is 8 and yields a cross-sectional area error of or =2. CONCLUSION: The predicted ideal result of lambda = 8 is within reach with current technology to image vessels such as the carotid artery or aorta. It is possible to determine the ideal resolution that minimizes errors in the measurement of the vessel cross-sectional area for a given SNR, processing algorithm, and vessel of interest.