Cerebral Blood Flow Assessment by Digital Subtraction Angiography
DOI:
https://doi.org/10.17140/ROJ-1-104Keywords:
Image processing, Intracranial blood flow, Inversed blood flowAbstract
Cerebral vascular disease is responsible for nearly 800 000 hospital admissions annually with a total healthcare cost of $34 billion. Neuro-interventional surgery is at the forefront of fast-response assessment and intervention to assess patients’ health using digital subtraction angiography (DSA). Unfortunately, cerebral blood flow cannot be reliably measured with DSA during intervention. In this study, we introduced a novel inversion-based method to quantify blood flow from DSA intensity profiles. This technique yields absolute volumetric flow rates in major cerebral arteries comparable in accuracy to QMRA measurements. This remarkable outcome was achieved by incorporating patient-specific anatomical data as well as flow physics into the image analysis. The inversion-based flow assessment is a promising approach to render absolute flow measurements with traditional DSA. We also discuss the precision of several methods for assessing cerebral blood flow before and after intervention in DSA were investigated. Angiographic intensity data in four patients with cerebral vascular diseases were analyzed to compute time-to-peak index (TTPi) and relative cerebral angiographic blood flow index (RCABi). Flow indices were compared with quantitative magnetic resonance angiography acquired pre- and post-intervention. The results suggest that individual intensitybased flow indices do not always reflect blood flow improvements after intervention; twodimensional index maps showed the improvements more robustly.
References
Go AS, Mozaffarian D, Roger VL, et al. Executive summary: heart disease and stroke statistics--2013 update: a report from the American Heart Association. Circulation. 2013; 127(1):143- 152. doi: 10.1161/CIR.0b013e318282ab8f
van Rooij WJ, Sluzewski M, Slob MJ, Rinkel GJ. Predictive value of angiographic testing for tolerance to therapeutic occlusion of the carotid artery. AJNR Am J Neuroradiol. 2005; 26(1): 175-178. Web site. http://www.ajnr.org/content/26/1/175. long. Accessed April 14, 2016
Macdonald RL, Wallace MC, Kestle JR. Role of angiography following aneurysm surgery. J neurosurg. 1993; 79(6): 826-832. Web site. http://thejns.org/doi/abs/10.3171/jns.1993.79.6.0826. Accessed April 14, 2016
Chiang VL, Gailloud P, Murphy KJ, Rigamonti D, Tamargo RJ. Routine intraoperative angiography during aneurysm surgery. J neurosurg. 2002; 96(6): 988-992. Web site. http://thejns.org/doi/ abs/10.3171/jns.2002.96.6.0988. Accessed April 14, 2016
Tang G, Cawley CM, Dion JE, Barrow DL. Intraoperative angiography during aneurysm surgery: a prospective evaluation of efficacy. J neurosurg. 2002; 96(6): 993-999. Web site. http://thejns.org/doi/abs/10.3171/jns.2002.96.6.0993?url_ ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_ pub%3dpubmed. Accessed April 14, 2016
Zhao M, Amin-Hanjani S, Ruland S, Curcio AP, Ostergren L, Charbel FT. Regional cerebral blood flow using quantitative MR angiography. AJNR Am J Neuroradiol. 2007; 28(8): 1470-1473. doi: 10.3174/ajnr.A0582
BauerAM,Amin-Hanjani S,AlarajA, Charbel FT. Quantitative magnetic resonance angiography in the evaluation of the subclavian steal syndrome: report of 5 patients. J Neuroimaging. 2009; 19(3): 250-252. doi: 10.1111/j.1552-6569.2008.00297.x
Mallett BL, Veall N. Investigation of cerebral blood-flow in hypertension, using radioactive-xenon inhalation and extracranial recording. Lancet. 1963; 281(7290): 1081-1082. doi: 10.1016/S0140-6736(63)92116-0
Chiron C, Raynaud C, Tzourio N, et al. Regional cerebral blood flow by SPECT imaging in Sturge-Weber disease: an aid for diagnosis. J Neurol Neurosurg Psychiatry. 1989; 52(12): 1402-1409. Web site. http://jnnp.bmj.com/content/52/12/1402. long. Accessed April 14, 2016
Iida H, Itoh H, Nakazawa M, et al. Quantitative mapping of regional cerebral blood flow using iodine-123-IMP and SPECT. J Nucl Med. 1994; 35(12): 2019-2030. Web site. http://jnm. snmjournals.org/content/35/12/2019.long. Accessed April 14, 2016
Demirkaya S, Uluc K, Bek S, Vural O. Normal blood flow velocities of basal cerebral arteries decrease with advancing age: a transcranial Doppler sonography study. Tohoku J Exp Med. 2008; 214(2): 145-149. doi: 10.1620/tjem.214.145
Ibaraki M, Ito H, Shimosegawa E, et al. Cerebral vascular mean transit time in healthy humans: a comparative study with PET and dynamic susceptibility contrast-enhanced MRI. J Cereb Blood Flow Metab. 2007; 27(2): 404-413. doi: 10.1038/ sj.jcbfm.9600337
Parsons MW. Perfusion CT: is it clinically useful? International journal of stroke : official journal of the International Stroke Society. 2008; 3(1): 41-50.
Allmendinger AM, Tang ER, Lui YW, Spektor V. Imaging of stroke: Part 1, Perfusion CT--overview of imaging technique, interpretation pearls, and common pitfalls. AJR Am J Roentgenol. 2012; 198(1): 52-62. doi: 10.2214/AJR.10.7255
Kamalian S, Kamalian S, Konstas AA, et al. CT perfusion mean transit time maps optimally distinguish benign oligemia from true “at-risk” ischemic penumbra, but thresholds vary by postprocessing technique. AJNR Am J Neuroradiol. 2012; 33(3): 545-549. doi: 10.3174/ajnr.A2809
Fieselmann A, Ganguly A, Deuerling-Zheng Y, Zellerhoff M, Rohkohl C, Boese J, et al. Interventional 4-D C-arm CT perfusion imaging using interleaved scanning and partial reconstruction interpolation. IEEE Trans Med Imaging. 2012; 31(4): 892-906. doi: 10.1109/TMI.2011.2181531
Fieselmann A, Kowarschik M, Ganguly A, Hornegger J, Fahrig R. Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details. Int biome imaging. 2011; 2011: 467563. doi: 10.1155/2011/467563
Zhang C, Villa-Uriol MC, De Craene M, Pozo JM, Macho JM, Frangi AF. Dynamic estimation of three-dimensional cerebrovascular deformation from rotational angiography. Med phys. 2011; 38(3) :1294-1306. doi: 10.1118/1.3549761
Waechter I, Bredno J, Barratt DC, Weese J, Hawkes DJ. Quantification of blood flow from rotational angiography. Med Image Comput Comput Assist Interv. 2007; 10(Pt 1): 634-641. doi: 10.1007/978-3-540-75757-3_77
Waechter I, Bredno J, Hermans R, Weese J, Barratt DC, Hawkes DJ. Model-based blood flow quantification from rotational angiography. Med image anal. 2008; 12(5): 586-602. doi: 10.1016/j.media.2008.06.003
Tenjin H, Asakura F, Nakahara Y, et al. Evaluation of intraaneurysmal blood velocity by time-density curve analysis and digital subtraction angiography. AJNR Am j neuroradiol. 1998; 19(7): 1303-1307. Web site. http://www.ajnr.org/ content/19/7/1303.long. Accessed April 14, 2016
Kamp MA, Slotty P, Turowski B, et al. Microscope-integrated quantitative analysis of intraoperative indocyanine green fluorescence angiography for blood flow assessment: first experience in 30 patients. Neurosurgery. 2012; 70(1 Suppl Operative): 65-73. doi: 10.1227/NEU.0b013e31822f7d7c
Divani AA, Lieber BB, Wakhloo AK, Gounis MJ, Hopkins LN. Determination of blood flow velocity and transit time in cerebral arteriovenous malformation using microdroplet angiography. Ann Biomed Eng. 2001; 29(2): 135-144. doi: 10.1114/1.1349696
Bursch JH, Hahne HJ, Brennecke R, Gronemeier D, Heintzen PH. Assessment of arterial blood flow measurements by digital angiography. Radiol. 1981; 141(1): 39-47. doi: 10.1148/ radiology.141.1.7291540
Molloi S, Zhou Y, Kassab GS. Regional volumetric coronary blood flow measurement by digital angiography: in vivo validation. Acad radiol. 2004; 11(7): 757-766. doi: 10.1016/j. acra.2004.04.002
Hangiandreou NJ, Folts JD, Peppler WW, Mistretta CA. Coronary blood flow measurement using an angiographic first pass distribution technique: a feasibility study. Med phys. 1991; 18(5): 947-954. doi: 10.1118/1.596716
Huang SP, Decker RJ, Goodrich KC, Parker DJ, Muhlestein JB, Blatter DD, et al. Velocity measurement based on bolus tracking with the aid of three-dimensional reconstruction from digital subtraction angiography. Med phys. 1997; 24(5): 677- 686. doi: 10.1118/1.597990
Efron U, Price RR, Smith CW, Brill AB. A method to determine the instantaneous blood-flow using cine- or video-densitometric data. Proc. SPIE 0143, Applications of Electronic Imaging Systems. 1978: 154-161. doi: 10.1117/12.956561
Sarry L, Boire JY, Zanca M, Lusson JR, Cassagnes J. Assessment of stenosis severity using a novel method to estimate spatial and temporal variations of blood flow velocity in biplane coronarography.Phys med biol. 1997; 42(8): 1549-1564.Web site. http://iopscience.iop.org/article/10.1088/0031-9155/42/8/006/ pdf. Accessed April 14, 2016
Shpilfoygel SD, Close RA, Valentino DJ, Duckwiler GR. X-ray videodensitometric methods for blood flow and velocity measurement: a critical review of literature. Med phy. 2000; 27(9): 2008-2023. doi: 10.1118/1.1288669
Lorensen WE, Cline HE. Marching cubes: A high resolution 3D surface construction algorithm. SIGGRAPH Comput Graph. 1987; 21(4): 163-169. doi: 10.1145/37401.37422
Piccinelli M, Veneziani A, Steinman DA, Remuzzi A, Antiga L. A framework for geometric analysis of vascular structures: application to cerebral aneurysms. IEEE Trans Med Imaging. 2009; 28(8): 1141-1155. doi: 10.1109/TMI.2009.2021652
Westerhof N, Stergiopulos N, Noble M. Snapshots of Hemodynamics: An aid for clinical research and graduate education; 2005.
Bui A, Sutalo ID, Manasseh R, Liffman K. Dynamics of pulsatile flow in fractal models of vascular branching networks. Med Biol Eng Comput. 2009; 47(7): 763-772. doi: 10.1007/ s11517-009-0492-6
Moon J, Linninger AA. A hybrid sequential niche algorithm for optimal engineering design with solution multiplicity. Comput Chem Eng. 2009; 33(7): 1261-1271. doi: 10.1016/j. compchemeng.2009.02.006
Kulkarni K, Zhang LB, Linninger AA. Model and parameter uncertainty in distributed systems. Ind Eng Chem Res. 2006; 45(23): 7832-7840. doi: 10.1021/ie0604876
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