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Differentiating Vascular Pathophysiological States by Objective Analysis of Flow Dynamics

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 (P...

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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
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description 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.
doi_str_mv 10.1111/j.1552-6569.2004.tb00224.x
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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. 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Robert</creatorcontrib><title>Differentiating Vascular Pathophysiological States by Objective Analysis of Flow Dynamics</title><title>Journal of neuroimaging</title><addtitle>J Neuroimaging</addtitle><description>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.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>arterial-venous malformation</subject><subject>arteriosclerosis</subject><subject>Blood Flow Velocity - physiology</subject><subject>Blood Pressure - physiology</subject><subject>Brain - blood supply</subject><subject>Cerebral Arteries - diagnostic imaging</subject><subject>Cerebral Arteries - physiopathology</subject><subject>Cerebrovascular Disorders - classification</subject><subject>Cerebrovascular Disorders - diagnostic imaging</subject><subject>Cerebrovascular Disorders - physiopathology</subject><subject>Cluster Analysis</subject><subject>congestive heart failure</subject><subject>Diagnosis, Computer-Assisted - instrumentation</subject><subject>Diagnosis, Differential</subject><subject>Dynamic vascular analysis</subject><subject>Expert Systems - instrumentation</subject><subject>Female</subject><subject>Humans</subject><subject>Image Enhancement - instrumentation</subject><subject>Image Processing, Computer-Assisted - instrumentation</subject><subject>Intracranial Arteriovenous Malformations - classification</subject><subject>Intracranial Arteriovenous Malformations - diagnostic imaging</subject><subject>Intracranial Arteriovenous Malformations - physiopathology</subject><subject>Male</subject><subject>Mathematical Computing</subject><subject>Probability</subject><subject>Prospective Studies</subject><subject>Pulsatile Flow - physiology</subject><subject>Reference Values</subject><subject>Regional Blood Flow - physiology</subject><subject>Sensitivity and Specificity</subject><subject>sleep apnea</subject><subject>Sleep Apnea Syndromes - classification</subject><subject>Sleep Apnea Syndromes - diagnostic imaging</subject><subject>Sleep Apnea Syndromes - physiopathology</subject><subject>small vessel disease</subject><subject>Software Design</subject><subject>Technology Assessment, Biomedical</subject><subject>Thalamus - blood supply</subject><subject>transcranial Doppler ultrasound</subject><subject>Ultrasonography, Doppler, Transcranial - instrumentation</subject><issn>1051-2284</issn><issn>1552-6569</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><recordid>eNqVkMtOGzEUQK0KVCjtL1QWC3Yz-DkeI3WBQnkJkUgFqq4s27HBqTMTxg5k_p6JEsEab2zJ59wrHQAOMSrxcI5nJeacFBWvZEkQYmU2CBHCytUXsP_-tTO8EccFITXbA99Smg0QZoR-BXuYI8k5p_vg31nw3nWuyUHn0DzCB53sMuoOTnR-ahdPfQptbB-D1RH-yTq7BE0Px2bmbA4vDp42Og5Mgq2H57F9hWd9o-fBpu9g1-uY3I_tfQDuz3_fjS6Lm_HF1ej0prCEM1EwiYnj1mFdcym9RkjWlZcSecYQw8gbj6WpWCWNmXqLjDeUCmk8kmIqZE0PwNFm7qJrn5cuZTUPyboYdePaZVIC11Siig7gyQa0XZtS57xadGGuu15hpNZh1Uyt66l1PbUOq7Zh1WqQf263LM3cTT_UbckB-LUBXkN0_SdGq-vxrRSDX2z8kLJbvfu6-68qQQVXf28vlBDV6FJMHtSEvgFdypgL</recordid><startdate>200404</startdate><enddate>200404</enddate><creator>Crutchfield, Kevin E.</creator><creator>Razumovsky, Alexander Y.</creator><creator>Tegeler, Charles H.</creator><creator>Mozayeni, B. Robert</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>200404</creationdate><title>Differentiating Vascular Pathophysiological States by Objective Analysis of Flow Dynamics</title><author>Crutchfield, Kevin E. ; Razumovsky, Alexander Y. ; Tegeler, Charles H. ; Mozayeni, B. Robert</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2547-4912e5ce1a8599fa00986f990f440410fbf19b6469bbdfc0bfb3379bf097d7983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>arterial-venous malformation</topic><topic>arteriosclerosis</topic><topic>Blood Flow Velocity - physiology</topic><topic>Blood Pressure - physiology</topic><topic>Brain - blood supply</topic><topic>Cerebral Arteries - diagnostic imaging</topic><topic>Cerebral Arteries - physiopathology</topic><topic>Cerebrovascular Disorders - classification</topic><topic>Cerebrovascular Disorders - diagnostic imaging</topic><topic>Cerebrovascular Disorders - physiopathology</topic><topic>Cluster Analysis</topic><topic>congestive heart failure</topic><topic>Diagnosis, Computer-Assisted - instrumentation</topic><topic>Diagnosis, Differential</topic><topic>Dynamic vascular analysis</topic><topic>Expert Systems - instrumentation</topic><topic>Female</topic><topic>Humans</topic><topic>Image Enhancement - instrumentation</topic><topic>Image Processing, Computer-Assisted - instrumentation</topic><topic>Intracranial Arteriovenous Malformations - classification</topic><topic>Intracranial Arteriovenous Malformations - diagnostic imaging</topic><topic>Intracranial Arteriovenous Malformations - physiopathology</topic><topic>Male</topic><topic>Mathematical Computing</topic><topic>Probability</topic><topic>Prospective Studies</topic><topic>Pulsatile Flow - physiology</topic><topic>Reference Values</topic><topic>Regional Blood Flow - physiology</topic><topic>Sensitivity and Specificity</topic><topic>sleep apnea</topic><topic>Sleep Apnea Syndromes - classification</topic><topic>Sleep Apnea Syndromes - diagnostic imaging</topic><topic>Sleep Apnea Syndromes - physiopathology</topic><topic>small vessel disease</topic><topic>Software Design</topic><topic>Technology Assessment, Biomedical</topic><topic>Thalamus - blood supply</topic><topic>transcranial Doppler ultrasound</topic><topic>Ultrasonography, Doppler, Transcranial - instrumentation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Crutchfield, Kevin E.</creatorcontrib><creatorcontrib>Razumovsky, Alexander Y.</creatorcontrib><creatorcontrib>Tegeler, Charles H.</creatorcontrib><creatorcontrib>Mozayeni, B. Robert</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of neuroimaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Crutchfield, Kevin E.</au><au>Razumovsky, Alexander Y.</au><au>Tegeler, Charles H.</au><au>Mozayeni, B. Robert</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Differentiating Vascular Pathophysiological States by Objective Analysis of Flow Dynamics</atitle><jtitle>Journal of neuroimaging</jtitle><addtitle>J Neuroimaging</addtitle><date>2004-04</date><risdate>2004</risdate><volume>14</volume><issue>2</issue><spage>97</spage><epage>107</epage><pages>97-107</pages><issn>1051-2284</issn><eissn>1552-6569</eissn><abstract>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.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><pmid>15095553</pmid><doi>10.1111/j.1552-6569.2004.tb00224.x</doi><tpages>11</tpages></addata></record>
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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
title Differentiating Vascular Pathophysiological States by Objective Analysis of Flow Dynamics
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