Automatic flow analysis of digital subtraction angiography using independent component analysis in patients with carotid stenosis

Purpose Current time—density curve analysis of digital subtraction angiography (DSA) provides intravascular flow information but requires manual vasculature selection. We developed an angiographic marker that represents cerebral perfusion by using automatic independent component analysis. Materials and methods We retrospectively analyzed the data of 44 patients with unilateral carotid stenosis higher than 70% according to North American Symptomatic Carotid Endarterectomy Trial criteria. For all patients, magnetic resonance perfusion (MRP) was performed one day before DSA. Fixed contrast injection protocols and DSA acquisition parameters were used before stenting. The cerebral circulation time (CCT) was defined as the difference in the time to peak between the parietal vein and cavernous internal carotid artery in a lateral angiogram. Both anterior-posterior and lateral DSA views were processed using independent component analysis, and the capillary angiogram was extracted automatically. The full width at half maximum of the time—density curve in the capillary phase in the anterior-posterior and lateral DSA views was defined as the angiographic mean transient time (aMTT; i.e., aMTTAP and aMTTLat). The correlations between the degree of stenosis, CCT, aMTTAP and aMTTLat, and MRP parameters were evaluated. Results The degree of stenosis showed no correlation with CCT, aMTTAP, aMTTLat, or any MRP parameter. CCT showed a strong correlation with aMTTAP (r = 0.67) and aMTTLat (r = 0.72). Among the MRP parameters, CCT showed only a moderate correlation with MTT (r = 0.67) and Tmax (r = 0.40). aMTTAP showed a moderate correlation with Tmax (r = 0.42) and a strong correlation with MTT (r = 0.77). aMTTLat also showed similar correlations with Tmax (r = 0.59) and MTT (r = 0.73). Conclusion Apart from vascular anatomy, aMTT estimates brain parenchyma hemodynamics from DSA and is concordant with MRP. This process is completely automatic and provides immediate measurement of quantitative peritherapeutic brain parenchyma changes during stenting.

[1]  M. Kowarschik,et al.  Monitoring Peri-Therapeutic Cerebral Circulation Time: A Feasibility Study Using Color-Coded Quantitative DSA in Patients with Steno-Occlusive Arterial Disease , 2012, American Journal of Neuroradiology.

[2]  Wan-Yuo Guo,et al.  Quantitative analysis of digital subtraction angiography using optical flow method on occlusive cerebrovascular disease , 2013, Comput. Methods Programs Biomed..

[3]  F. Calamante.  Arterial input function in perfusion MRI: a comprehensive review. , 2013, Progress in nuclear magnetic resonance spectroscopy.

[4]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[5]  J. Sánchez-Pérez,et al.  The role of complete cerebral angiography in neurosurgery. , 1949, Journal of neurosurgery.

[6]  Kevin Royalty,et al.  Effective dose to patient measurements in flat-detector and multislice computed tomography: a comparison of applications in neuroradiology , 2014, European Radiology.

[7]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[8]  A. Rosenberg,et al.  Solitary Fibrous Tumor of the Nasal Cavity and Paranasal Sinuses , 1991, The American journal of surgical pathology.

[9]  Hiroki Shirato,et al.  Difference in tracer delay-induced effect among deconvolution algorithms in CT perfusion analysis: quantitative evaluation with digital phantoms. , 2009, Radiology.

[10]  Sarah Kefayati,et al.  Transitional flow analysis in the carotid artery bifurcation by proper orthogonal decomposition and particle image velocimetry. , 2013, Medical engineering & physics.

[11]  T. Struffert,et al.  Parametric Color Coding of Digital Subtraction Angiography in the Evaluation of Carotid Cavernous Fistulas , 2013, Clinical Neuroradiology.

[12]  Wan-Yuo Guo,et al.  Peritherapeutic quantitative flow analysis of arteriovenous malformation on digital subtraction angiography. , 2012, Journal of vascular surgery.

[13]  Antonio Torralba,et al.  SIFT Flow: Dense Correspondence across Scenes and Its Applications , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Denis Trystram,et al.  Three‐dimensional dynamic time‐resolved contrast‐enhanced MRA using parallel imaging and a variable rate k‐space sampling strategy in intracranial arteriovenous malformations , 2009, Journal of magnetic resonance imaging : JMRI.

[15]  Kuo-Ching Liu,et al.  Hemodynamic segmentation of MR brain perfusion images using independent component analysis, thresholding, and Bayesian estimation , 2003, Magnetic resonance in medicine.

[16]  Wan-Yuo Guo,et al.  Using Flat-Panel Perfusion Imaging to Measure Cerebral Hemodynamics , 2016, Medicine.

[17]  Fabien Scalzo,et al.  Perfusion Angiography in Acute Ischemic Stroke , 2016, Comput. Math. Methods Medicine.

[18]  M Buxton-Thomas,et al.  Quantitative perfusion imaging in carotid artery stenosis using dynamic susceptibility contrast-enhanced magnetic resonance imaging. , 2000, Magnetic resonance imaging.

[19]  Anne L. Martel,et al.  Extracting parametric images from dynamic contrast-enhanced MRI studies of the brain using factor analysis , 2001, Medical Image Anal..

[20]  K. Schaller,et al.  Quantification of Internal Carotid Artery Flow with Digital Subtraction Angiography: Validation of an Optical Flow Approach with Doppler Ultrasound , 2014, American Journal of Neuroradiology.

[21]  Y. Kao,et al.  Peritherapeutic Hemodynamic Changes of Carotid Stenting Evaluated with Quantitative DSA in Patients with Carotid Stenosis , 2016, American Journal of Neuroradiology.

[22]  M. Kaste,et al.  Cerebral Hemodynamics in Asymptomatic and Symptomatic Patients With High-Grade Carotid Stenosis Undergoing Carotid Endarterectomy , 2003, Stroke.

[23]  T Struffert,et al.  Dynamic Angiography and Perfusion Imaging Using Flat Detector CT in the Angiography Suite: A Pilot Study in Patients with Acute Middle Cerebral Artery Occlusions , 2015, American Journal of Neuroradiology.

[24]  T. Greitz,et al.  Book Review: A Radiologic Study of the Brain Circulation by Rapid Serial Angiography of the Carotid Artery , 1957, Acta radiologica. Supplementum.

[25]  M Zellerhoff,et al.  Parametric Color Coding of Digital Subtraction Angiography , 2010, American Journal of Neuroradiology.

[26]  Alan Connelly,et al.  The Physiological Significance of the Time-to-Maximum (Tmax) Parameter in Perfusion MRI , 2010, Stroke.

[27]  K. Wong,et al.  Compensatory patterns of collateral flow in stroke patients with unilateral and bilateral carotid stenosis , 2016, BMC Neurology.

[28]  Liliana Cesar,et al.  The mixability of angiographic contrast with arterial blood. , 2009, Medical physics.

[29]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[30]  A. Alexandrov,et al.  Practice Standards for Transcranial Doppler (TCD) Ultrasound. Part II. Clinical Indications and Expected Outcomes , 2012, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[31]  A. Demchuk,et al.  Use of the Alberta Stroke Program Early CT Score (ASPECTS) for assessing CT scans in patients with acute stroke. , 2001, AJNR. American journal of neuroradiology.

[32]  Kuo-Ching Liu,et al.  Hemodynamic segmentation of MR perfusion images in patients with unilateral carotid stenosis using independent component analysis , 2008, Journal of magnetic resonance imaging : JMRI.

[33]  R. Close,et al.  X-ray videodensitometric methods for blood flow and velocity measurement: a critical review of literature. , 2000, Medical physics.

[34]  Wan-Yuo Guo,et al.  Finding the optimal deconvolution algorithm for MR perfusion in carotid stenosis: Correlations with angiographic cerebral circulation time. , 2016, Journal of neuroradiology. Journal de neuroradiologie.

[35]  H Meng,et al.  CFD: Computational Fluid Dynamics or Confounding Factor Dissemination? The Role of Hemodynamics in Intracranial Aneurysm Rupture Risk Assessment , 2014, American Journal of Neuroradiology.

[36]  Dan Wang,et al.  Comparison of the accuracy of subtraction CT angiography performed on 320-detector row volume CT with conventional CT angiography for diagnosis of intracranial aneurysms. , 2012, European journal of radiology.

[37]  T. Iwata,et al.  Predictors of Hyperperfusion Syndrome Before and Immediately After Carotid Artery Stenting in Single-Photon Emission Computed Tomography and Transcranial Color-Coded Real-Time Sonography Studies , 2011, Neurosurgery.

[38]  T. Kodama,et al.  Intraprocedural changes in angiographic cerebral circulation time predict cerebral blood flow after carotid artery stenting. , 2010, Neurologia medico-chirurgica.

[39]  C.-J. Lin,et al.  Quantifying the Cerebral Hemodynamics of Dural Arteriovenous Fistula in Transverse Sigmoid Sinus Complicated by Sinus Stenosis: A Retrospective Cohort Study , 2017, American Journal of Neuroradiology.

[40]  D. Chen,et al.  Stenotic Transverse Sinus Predisposes to Poststenting Hyperperfusion Syndrome as Evidenced by Quantitative Analysis of Peritherapeutic Cerebral Circulation Time , 2014, American Journal of Neuroradiology.

[41]  Y. Matsumoto,et al.  Intraprocedural prediction of hemorrhagic cerebral hyperperfusion syndrome after carotid artery stenting. , 2013, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.