Quantification of cerebral blood flow in adults by contrast-enhanced near-infrared spectroscopy: Validation against MRI.
Daniel MilejLian HeAndrou AbdalmalakWesley Boehs BakerUdunna C AnazodoMamadou DiopSudipto DoluiVenkaiah C KavuriWilliam PavloskyLin WangRamani BaluJohn A DetreOlivia AmendoliaFrancis QuattroneW Andrew KofkeArjun G YodhKeith St LawrencePublished in: Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism (2019)
The purpose of this study was to assess the accuracy of absolute cerebral blood flow (CBF) measurements obtained by dynamic contrast-enhanced (DCE) near-infrared spectroscopy (NIRS) using indocyanine green as a perfusion contrast agent. For validation, CBF was measured independently using the MRI perfusion method arterial spin labeling (ASL). Data were acquired at two sites and under two flow conditions (normocapnia and hypercapnia). Depth sensitivity was enhanced using time-resolved detection, which was demonstrated in a separate set of experiments using a tourniquet to temporally impede scalp blood flow. A strong correlation between CBF measurements from ASL and DCE-NIRS was observed (slope = 0.99 ± 0.08, y-intercept = -1.7 ± 7.4 mL/100 g/min, and R2 = 0.88). Mean difference between the two techniques was 1.9 mL/100 g/min (95% confidence interval ranged from -15 to 19 mL/100g/min and the mean ASL CBF was 75.4 mL/100 g/min). Error analysis showed that structural information and baseline absorption coefficient were needed for optimal CBF reconstruction with DCE-NIRS. This study demonstrated that DCE-NIRS is sensitive to blood flow in the adult brain and can provide accurate CBF measurements with the appropriate modeling techniques.
Keyphrases
- contrast enhanced
- cerebral blood flow
- blood flow
- diffusion weighted
- magnetic resonance imaging
- diffusion weighted imaging
- magnetic resonance
- computed tomography
- healthcare
- machine learning
- room temperature
- mass spectrometry
- electronic health record
- single molecule
- deep learning
- loop mediated isothermal amplification
- childhood cancer