Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging

This study presents an in silico approach that extends previous intensity thresholding analysis to estimate the optimal “per‐slice” intensity threshold value using the individual components of the simulated ASL signal (signal arising independently from capilla ry blood as well as pulmonary arterial and pulmonary venous blood). The aim of this study was to assess whether the threshold value should vary with slice location, posture, or cardiac output. We applied an in silico modeling approach to predict the blood flow distribution and the corresponding ASL quantification of pulmonary perfusion in multiple sagittal imaging slices. There was a significant increase in ASL signal and heterogeneity (COV = 0.90 to COV = 1.65) of ASL signals when slice location changed from lateral to medial. Heterogeneity of the ASL signal within a slice was significant ly lower (P  = 0.03) in prone (COV  = 1.08) compared to in the supine posture (COV = 1.17). Increasing stroke volume resulted in an increase in ASL signal and conversely an increase in heart rate resulted in a decrease in ASL signal. However, when cardiac output was increased via an increase in both stroke volume and heart rate, ASL signal remained relatively constant. Despite these differences, we conclude that a threshold value of 35% provides optimal removal of large vessel signal independent of slice location, posture, and cardiac output.
Source: Physiological Reports - Category: Physiology Authors: Tags: Original Research Source Type: research