A Quantitative Digital Subtraction Angiography Technique for Characterizing Reduction in Hepatic Arterial Blood Flow During Transarterial Embolization

Periyasamy, Sarvesh; Hoffman, Carson; Longhurst, Colin A.; Schefelker, G.; Özkan, Orhan Ş.; Speidel, Michael A.; Laeseke, Paul F.

doi:10.1007/s00270-020-02640-0

Cited by 12 publications

(18 citation statements)

References 21 publications

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“…The proposed qDSA technique takes advantage of spatial and temporal information along the vessel allowing for a more robust quantitative technique. The ability to characterize intraprocedural arterial velocity reductions during TAE using qDSA was recently demonstrated in a clinically relevant porcine liver model (Periyasamy et al 2020). In that study, qDSA was compared to the commercially available iFlow.…”

Section: Discussionmentioning

confidence: 99%

A technique for intra-procedural blood velocity quantitation using time-resolved 2D digital subtraction angiography

et al. 2021

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Background 2D digital subtraction angiography (DSA) is utilized qualitatively to assess blood velocity changes that occur during arterial interventions. Quantitative angiographic metrics, such as blood velocity, could be used to standardize endpoints during angiographic interventions. Purpose To assess the accuracy and precision of a quantitative 2D DSA (qDSA) technique and to determine its feasibility for in vivo measurements of blood velocity. Materials and methods A quantitative DSA technique was developed to calculate intra-procedural blood velocity. In vitro validation was performed by comparing velocities from the qDSA method and an ultrasonic flow probe in a bifurcation phantom. Parameters of interest included baseline flow rate, contrast injection rate, projection angle, and magnification. In vivo qDSA analysis was completed in five different branches of the abdominal aorta in two 50 kg swine and compared to 4D Flow MRI. Linear regression, Bland-Altman, Pearson’s correlation coefficient and chi squared tests were used to assess the accuracy and precision of the technique. Results In vitro validation showed strong correlation between qDSA and flow probe velocities over a range of contrast injection and baseline flow rates (slope = 1.012, 95% CI [0.989,1.035], Pearson’s r = 0.996, p < .0001). The application of projection angle and magnification corrections decreased variance to less than 5% the average baseline velocity (p = 0.999 and p = 0.956, respectively). In vivo validation showed strong correlation with a small bias between qDSA and 4D Flow MRI velocities for all five abdominopelvic arterial vessels of interest (slope = 1.01, Pearson’s r = 0.880, p = <.01, Bias = 0.117 cm/s). Conclusion The proposed method allows for accurate and precise calculation of blood velocities, in near real-time, from time resolved 2D DSAs.

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Section: Discussionmentioning

confidence: 99%

A technique for intra-procedural blood velocity quantitation using time-resolved 2D digital subtraction angiography

et al. 2021

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“…Quantifying blood velocity or flow from two-dimensional (2D) time-resolved angiograms has been proposed to monitor progress or determine endpoints for interventional procedures 1 – 8 The goal of these algorithms is to provide quantitative information during the intervention that otherwise rely on qualitative angiographic endpoints with high inter-observer variability 9 . To this end, a robust quantitative algorithm may help provide more consistent treatment endpoints.…”

Section: Introductionmentioning

confidence: 99%

“…In previous work, the use of a static vessel centerline has been proposed 1 – 7 Centerlines are extracted through thresholding and subsequent thinning 4 , 6 , 7 , 12 or by using Dijkstra’s shortest path algorithm, 13 where the cost of each pixel is computed based on the distance from the vessel wall 1 , 2 . However, TAMs calculated from static vessel centerlines are susceptible to vessel motion artifacts, which may lead to inaccurate blood velocity measurements.…”

Section: Introductionmentioning

confidence: 99%

Motion-compensation approach for quantitative digital subtraction angiography and its effect on in-vivo blood velocity measurement

Whitehead,

Periyasamy,

Laeseke

et al. 2024

J. Med. Imag.

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.PurposeQuantitative monitoring of flow-altering interventions has been proposed using algorithms that quantify blood velocity from time-resolved two-dimensional angiograms. These algorithms track the movement of contrast oscillations along a vessel centerline. Vessel motion may occur relative to a statically defined vessel centerline, corrupting the blood velocity measurement. We provide a method for motion-compensated blood velocity quantification.ApproachThe motion-compensation approach utilizes a vessel segmentation algorithm to perform frame-by-frame vessel registration and creates a dynamic vessel centerline that moves with the vasculature. Performance was evaluated in-vivo through comparison with manually annotated centerlines. The method was also compared to a previous uncompensated method using best- and worst-case static centerlines chosen to minimize and maximize centerline placement accuracy. Blood velocities determined through quantitative DSA (qDSA) analysis for each centerline type were compared through linear regression analysis.ResultsCenterline distance errors were 0.3±0.1 mm relative to gold standard manual annotations. For the uncompensated approach, the best- and worst-case static centerlines had distance errors of 1.1±0.6 and 2.9±1.2 mm, respectively. Linear regression analysis found a high R-squared between qDSA-derived blood velocities using gold standard centerlines and motion-compensated centerlines (R2=0.97) with a slope of 1.15 and a small offset of −0.6 cm/s. The use of static centerlines resulted in low coefficients of determination for the best case (R2=0.35) and worst-case (R2=0.20) scenarios, with slopes close to zero.ConclusionsIn-vivo validation of motion-compensated qDSA analysis demonstrated improved velocity quantification accuracy in vessels with motion, addressing an important clinical limitation of the current qDSA algorithm.

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“…Specifically, hepatic arterial interventions (e.g., transarterial embolization) are a routinely performed image-guided intervention and are the subject of many active investigations in image guidance. 24 Therefore, realistic CHPs are important for the development and evaluation of hepatic blood flow quantification algorithms [25][26][27][28][29][30][31] and the synthesis of realistic interventional hepatic angiograms for machine learning (ML) applications (e.g., vessel segmentation for treatment planning and tumor feeding artery identification). This would require the simulation of motion and contrast dynamics as well as randomizable anatomy to provide sufficient data variability for the training of ML algorithms.…”

Section: Introductionmentioning

confidence: 99%

In silico simulation of hepatic arteries: An open‐source algorithm for efficient synthetic data generation

et al. 2023

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BackgroundIn silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high‐resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random.PurposeThe purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation.MethodsThe vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization‐based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non‐intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented.Results: The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high‐resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law ), radii (median Murray deviation ), and smoothly curved, non‐intersecting vessels. Furthermore, the algorithm assures a main feeding artery to each Couinaud segment and is random (variability = 0.98 ± 0.01).ConclusionsThis method facilitates the generation of large datasets of high‐resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.

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A Quantitative Digital Subtraction Angiography Technique for Characterizing Reduction in Hepatic Arterial Blood Flow During Transarterial Embolization

Cited by 12 publications

References 21 publications

A technique for intra-procedural blood velocity quantitation using time-resolved 2D digital subtraction angiography

A technique for intra-procedural blood velocity quantitation using time-resolved 2D digital subtraction angiography

Motion-compensation approach for quantitative digital subtraction angiography and its effect on in-vivo blood velocity measurement

In silico simulation of hepatic arteries: An open‐source algorithm for efficient synthetic data generation

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