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Proceedings Paper

Effect of truncated singular value decomposition on digital subtraction angiography derived angiographic parametric imaging maps
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Paper Abstract

Angiographic parametric imaging (API) is a quantitative imaging method which uses digital subtraction angiography (DSA) to calculate biomarkers related to hemodynamics. This method has been used for neurovascular disease diagnosis and treatment outcome predictions in clinical settings but results are regarded with caution since derived biomarkers are strongly correlated with contrast injection parameters. This study aimed to assess utilization of truncated singular value decomposition (TSVD) in correcting API maps across various injection rates. Digital angiography data was collected using two neurovascular phantoms embedded in a simulated flow loop. Contrast volumes of 5 and 10 mL along with injection rates of 5, 10, 15, and 20 mL/sec were utilized during testing. API maps were generated with baseline and stenosis models using gamma variate fitting along with TSVD of the arterial input of the phantom. Surrogate regional blood flow (sRBF) and regional blood volume (sRBV) maps indicate consistent values across varying injection rates along with decreases in flow and volumes following introduction of a stenosis (Baseline: sRBF=53.3±4.20 arbitrary volume units (AVU)/min, sRBV=2.66±0.14 AVU, Stenosis: sRBF=28.6±3.78 AVU/min, sRBV=1.75±0.45 AVU). Mean transit time (MTT) and time of maximal residue function (Tmax) maps indicate consistent and decreasing parameter values respectively as injection rates increase along with increases in each parameter in the presence of a stenosis (Baseline: MTT=0.72±0.14 sec, Tmax=1.36±0.14 sec, Stenosis: MTT=0.94±0.13 sec, Tmax=1.71±0.18 sec). This study indicates TSVD has the potential to normalize API parameter maps across various injection rates potentially allowing for the implementation of API in ischemic stroke diagnosis and treatment.

Paper Details

Date Published: 16 March 2020
PDF: 11 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131235 (16 March 2020); doi: 10.1117/12.2545994
Show Author Affiliations
Ryan A. Rava, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr., Univ. at Buffalo (United States)
Ariana B. Allman, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr., Univ. at Buffalo (United States)
Stephen Rudin, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr., Univ. at Buffalo (United States)
Univ. at Buffalo Jacobs School of Medicine (United States)
Ciprian N. Ionita, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr., Univ. at Buffalo (United States)
Univ. at Buffalo Jacobs School of Medicine (United States)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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