Differentiating Vascular Pathophysiological States by Objective Analysis of Flow Dynamics

• Published in: Journal of neuroimaging 2004-04, Vol.14 (2), p.97-107
• Main Authors: Crutchfield, Kevin E., Razumovsky, Alexander Y., Tegeler, Charles H., Mozayeni, B. Robert
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; arterial-venous malformation; arteriosclerosis; Blood Flow Velocity - physiology; Blood Pressure - physiology; Brain - blood supply; Cerebral Arteries - diagnostic imaging; Cerebral Arteries - physiopathology; Cerebrovascular Disorders - classification; Cerebrovascular Disorders - diagnostic imaging; Cerebrovascular Disorders - physiopathology; Cluster Analysis; congestive heart failure; Diagnosis, Computer-Assisted - instrumentation; Diagnosis, Differential; Dynamic vascular analysis; Expert Systems - instrumentation; Female; Humans; Image Enhancement - instrumentation; Image Processing, Computer-Assisted - instrumentation; Intracranial Arteriovenous Malformations - classification; Intracranial Arteriovenous Malformations - diagnostic imaging; Intracranial Arteriovenous Malformations - physiopathology; Male; Mathematical Computing; Probability; Prospective Studies; Pulsatile Flow - physiology; Reference Values; Regional Blood Flow - physiology; Sensitivity and Specificity; sleep apnea; Sleep Apnea Syndromes - classification; Sleep Apnea Syndromes - diagnostic imaging; Sleep Apnea Syndromes - physiopathology; small vessel disease; Software Design; Technology Assessment, Biomedical; Thalamus - blood supply; transcranial Doppler ultrasound; Ultrasonography, Doppler, Transcranial - instrumentation
• ISSN: 1051-2284; 1552-6569
• DOI: 10.1111/j.1552-6569.2004.tb00224.x

ABSTRACT Background and Purpose. There is an unmet need to classify cerebrovascular conditions physiologically and to assess cerebrovascular system performance. The authors hypothesized that by simultaneously considering the dynamic parameters of flow velocity, acceleration, and pulsatility index (PI) (impedance) in individual Doppler spectrum waveforms, they could develop an objective method to elucidate the pathophysiology of vascular conditions and classify cerebrovascular disorders. This method, dynamic vascular analysis (DVA), is described. Methods. First, a theoretical model was developed to determine how any vascular segment and the ensemble of intracranial vascular segments could be defined according to its dynamic physiological characteristics. Next, the DVA method was applied to 847 anonymous serial complete clinical transcranial Doppler (TCD) studies of patients without regard for their diagnosis to ascertain actual reference ranges and the normality of the distribution curves for each dimension of the 3‐parameter nomogram. The authors applied DVA to 2 clinicalcases to see if they could track the changes in vascular performance of 2 known progressive diseases. Results. The theoretical analysis identified 295,245 possible vascular states for the ensemble of vascular segments in the cerebral circulation. When applied to clinical TCD data, DVA revealed continuous, normally distributed data for the velocity, PI, and logarithm of the acceleration. Conclusions. DVA is proposed as a method for monitoring the physiological state of each cerebral artery segment individually and in ensemble. DVA evaluates the relationship among acceleration (force or pressure), velocity, and PI and provides an objective means to evaluate intracranial vascular segments using the paradigm of the well‐described pressure‐perfusion autoregulation relationship. DVA may be used to study cerebrovascular pathophysiology and to classify, evaluate, and monitor cerebrovascular disorders or systemic disorders with cerebrovascular effects.