Quantitative Digital Subtraction Angiography Measurement of Arterial Velocity at Low Radiation Dose Rates.

PURPOSE: Quantitative digital subtraction angiography (qDSA) has been proposed to quantify blood velocity for monitoring treatment progress during blood flow altering interventions. The method requires high frame rate imaging [~ 30 frame per second (fps)] to capture temporal dynamics. This work investigates performance of qDSA in low radiation dose acquisitions to facilitate clinical translation. MATERIALS AND METHODS: Velocity quantification accuracy was evaluated at five radiation dose rates in vitro and in vivo. Angiographic technique ranged from 30 fps digital subtraction angiography ( 29.3 ± 1.7 mGy / s at the interventional reference point) down to a 30 fps protocol at 23% higher radiation dose per frame than fluoroscopy ( 1.1 ± 0.2 mGy / s ). The in vitro setup consisted of a 3D-printed model of a swine hepatic arterial tree connected to a pulsatile displacement pump. Five different flow rates (3.5-8.8 mL/s) were investigated in vitro. Angiography-based fluid velocity measurements were compared across dose rates using ANOVA and Bland-Altman analysis. The experiment was then repeated in a swine study (n = 4). RESULTS: Radiation dose rate reductions for the lowest dose protocol were 99% and 96% for the phantom and swine study, respectively. No significant difference was found between angiography-based velocity measurements at different dose rates in vitro or in vivo. Bland-Altman analysis found little bias for all lower-dose protocols (range: [- 0.1, 0.1] cm/s), with the widest limits of agreement ([- 3.3, 3.5] cm/s) occurring at the lowest dose protocol. CONCLUSIONS: This study demonstrates the feasibility of quantitative blood velocity measurements from angiographic images acquired at reduced radiation dose rates.

Quantitative 2D Digital Subtraction Venography for Venous Interventions: Validation in Phantom and In Vivo Porcine Models.

PURPOSE: To determine the feasibility of using a 2D quantitative digital subtraction venography (qDSV) technique that employs a temporally modulated contrast injection to quantify blood velocity in phantom, normal, and stenotic porcine iliac vein models. MATERIALS AND METHODS: Blood velocity was calculated using qDSV following temporally-modulated, pulsed injections of iodinated contrast medium, and compared to Doppler ultrasound (US) measurements (phantom: in-line sensor, in vivo: diagnostic linear probe). Phantom evaluation was performed in a compliant polyethylene tube phantom with simulated venous flow. In vivo evaluation of qDSV was performed in normal (n=7) and stenotic (n=3) iliac vein models. Stenoses were created using endovenous radiofrequency ablation and blood velocities were determined at baseline, post-stenosis, post-venoplasty and post-stent placement. RESULTS: In the phantom model, qDSV-calculated blood velocities (12-50 cm/s) had very strong correlations with US-measured velocities (13-51 cm/s) across a range of baseline blood velocities and injection protocols (slope=[1.01-1.13], R2=[0.96-0.99]). qDSV velocities were similar to US regardless of injection method: custom injector, commercial injector, or hand injection. In the normal in vivo model, qDSV-calculated velocities (5-18 cm/s) had strong correlation (slope=1.22, R2=0.90) with US (3-20 cm/s). In the stenosis model, blood velocity at baseline, post-stenosis, post-venoplasty, and post-stent placement were similar on qDSV and US at all time points. CONCLUSION: Venous blood velocity was accurately quantified in a venousphantom and in vivo porcine models using qDSV. Intra-procedural changes in porcine iliac vein blood velocity were quantified with qDSV after creation of a stenosis and subsequently treating it with venoplasty and stent placement.

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

Purpose: Quantitative 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. Approach: The 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. Results: Centerline 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. Conclusions: In-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.

Interleaved x-ray imaging: A method for simultaneous acquisition of quantitative and diagnostic digital subtraction angiography.

BACKGROUND: Flow altering angiographic procedures suffer from ill-defined, qualitative endpoints. Quantitative digital subtraction angiography (qDSA) is an emerging technology that aims to address this issue by providing intra-procedural blood velocity measurements from time-resolved, 2D angiograms. To date, qDSA has used 30 frame/s DSA imaging, which is associated with high radiation dose rate compared to clinical diagnostic DSA (up to 4 frame/s). PURPOSE: The purpose of this study is to demonstrate an interleaved x-ray imaging method which decreases the radiation dose rate associated with high frame rate qDSA while simultaneously providing low frame rate diagnostic DSA images, enabling the acquisition of both datasets in a single image sequence with a single injection of contrast agent. METHODS: Interleaved x-ray imaging combines low radiation dose image frames acquired at a high rate with high radiation dose image frames acquired at a low rate. The feasibility of this approach was evaluated on an x-ray system equipped with research prototype software for x-ray tube control. qDSA blood velocity quantification was evaluated in a flow phantom study for two lower dose interleaving protocols (LD1: 3.7 ± 0.02 mGy / s $3.7 \pm 0.02\ {\mathrm{mGy}}/{\mathrm{s}}$ and LD2: 1.7 ± 0.04 mGy / s $1.7 \pm 0.04{\mathrm{\ mGy}}/{\mathrm{s}}$ ) and one conventional (full dose) protocol ( 11.4 ± 0.04 mGy / s ) $11.4 \pm 0.04{\mathrm{\ mGy}}/{\mathrm{s}})$ . Dose was measured at the interventional reference point. Fluid velocities ranging from 24 to 45 cm/s were investigated. Gold standard velocities were measured using an ultrasound flow probe. Linear regression and Bland-Altman analysis were used to compare ultrasound and qDSA. RESULTS: The LD1 and LD2 interleaved protocols resulted in dose rate reductions of -67.7% and -85.5%, compared to the full dose qDSA scan. For the full dose protocol, the Bland-Altman limits of agreement (LOA) between qDSA and ultrasound velocities were [0.7, 6.7] cm/s with a mean difference of 3.7 cm/s. The LD1 interleaved protocol results were similar (LOA: [0.3, 6.9] cm/s, bias: 3.6 cm/s). The LD2 interleaved protocol resulted in slightly larger LOA: [-2.5, 5.5] cm/s with a decrease in the bias: 1.5 cm/s. Linear regression analysis showed a strong correlation between ultrasound and qDSA derived velocities using the LD1 protocol, with a R 2 ${R}^2$ of 0.96 $0.96$ , a slope of 1.05 $1.05$ and an offset of 1.9 $1.9$  cm/s. Similar values were also found for the LD2 protocol, with a R 2 ${R}^2$ of 0.93 $0.93$ , a slope of 0.98 $0.98$ and an offset of 2.0 $2.0$  cm/s. CONCLUSIONS: The interleaved method enables simultaneous acquisition of low-dose high-rate images for intra-procedural blood velocity quantification (qDSA) and high-dose low-rate images for vessel morphology evaluation (diagnostic DSA).

Spatiotemporal frequency domain analysis for blood velocity measurement during embolization procedures.

BACKGROUND: Currently, determining procedural endpoints and treatment efficacy of vascular interventions is largely qualitative and relies on subjective visual assessment of digital subtraction angiography (DSA) images leading to large interobserver variabilities and poor reproducibility. Quantitative metrics such as the residual blood velocity in embolized vessel branches could help establish objective and reproducible endpoints. Recently, velocity quantification techniques based on a contrast enhanced X-ray sequence such as qDSA and 4D DSA have been proposed. These techniques must be robust, and, to avoid radiation dose concerns, they should be compatible with low dose per frame image acquisition. PURPOSE: To develop and evaluate a technique for robust blood velocity quantification from low dose contrast enhanced X-ray image sequences that leverages the oscillating signal created by pulsatile blood flow. METHODS: The proposed spatiotemporal frequency domain (STF) approach quantifies velocities from time attenuation maps (TAMs) representing the oscillating signal over time for all points along a vessel centerline. Due to the time it takes a contrast bolus to travel along the vessel centerline, the resulting TAM resembles a sheared sine wave. The shear angle is related to the velocity and can be determined in the spatiotemporal frequency domain after applying the 2D Fourier transform to the TAM. The approach was evaluated in a straight tube phantom using three different radiation dose levels and compared to ultrasound transit-time-based measurements. The STF velocity results were also compared to previously published approaches for the measurement of blood velocity from contrast enhanced X-ray sequences including shifted least squared (SLS) and phase shift (PHS). Additionally, an in vivo porcine study (n = 8) was performed where increasing amounts of embolic particles were injected into a hepatic or splenic artery with intermittent velocity measurements after each injection to monitor the resulting reduction in velocity. RESULTS: At the lowest evaluated dose level (average air kerma rate 1.3 mGy/s at the interventional reference point), the Pearson correlation between ultrasound and STF velocity measurements was 99 % $99\%$ . This was significantly higher ( p < 0.0001 $p < 0.0001$ ) than corresponding correlation results between ultrasound and the previously published SLS and PHS approaches ( 91 $\hskip.001pt 91$ and 93 % $93\%$ , respectively). In the in vivo study, a reduction in velocity was observed in 85.7 % $85.7\%$ of cases after injection of 1 mL, 96.4 % $96.4\%$ after 3 mL, and 100.0 % $100.0\%$ after 4 mL of embolic particles. CONCLUSIONS: The results show good agreement of the spatiotemporal frequency domain approach with ultrasound even in low dose per frame image sequences. Additionally, the in vivo study demonstrates the ability to monitor the physiological changes due to embolization. This could provide quantitative metrics during vascular procedures to establish objective and reproducible endpoints.

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

BACKGROUND: In 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. PURPOSE: The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation. METHODS: The 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 = 1.2 ± 1 . 2 o $ = \;1.2 \pm {1.2^o}$ ), radii (median Murray deviation = 0.08 $ = \;0.08$ ), 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). CONCLUSIONS: This 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.

Real-time respiratory motion compensated roadmaps for hepatic arterial interventions.

PURPOSE: During hepatic arterial interventions, catheter or guidewire position is determined by referencing or overlaying a previously acquired static vessel roadmap. Respiratory motion leads to significant discrepancies between the true position and configuration of the hepatic arteries and the roadmap, which makes navigation and accurate catheter placement more challenging and time consuming. The purpose of this work was to develop a dynamic respiratory motion compensated device guidance system and evaluate the accuracy and real-time performance in an in vivo porcine liver model. METHODS: The proposed device navigation system estimates a respiratory motion model for the hepatic vasculature from prenavigational X-ray image sequences acquired under free-breathing conditions with and without contrast enhancement. During device navigation, the respiratory state is tracked based on live fluoroscopic images and then used to estimate vessel deformation based on the previously determined motion model. Additionally, guidewires and catheters are segmented from the fluoroscopic images using a deep learning approach. The vessel and device information are combined and shown in a real-time display. Two different display modes are evaluated within this work: (1) a compensated roadmap display, where the vessel roadmap is shown moving with the respiratory motion; (2) an inverse compensated device display, where the device representation is compensated for respiratory motion and overlaid on a static roadmap. A porcine study including seven animals was performed to evaluate the accuracy and real-time performance of the system. In each pig, a guidewire and microcatheter with a radiopaque marker were navigated to distal branches of the hepatic arteries under fluoroscopic guidance. Motion compensated displays were generated showing real-time overlays of the vessel roadmap and intravascular devices. The accuracy of the motion model was estimated by comparing the estimated vessel motion to the motion of the X-ray visible marker. RESULTS: The median (minimum, maximum) error across animals was 1.08 mm (0.92 mm, 1.87 mm). Across different respiratory states and vessel branch levels, the odds of the guidewire tip being shown in the correct vessel branch were significantly higher (odds ratio = 3.12, p < 0.0001) for motion compensated displays compared to a noncompensated display (median probabilities of 86 and 69%, respectively). The average processing time per frame was 17 ms. CONCLUSIONS: The proposed respiratory motion compensated device guidance system increased the accuracy of the displayed device position relative to the hepatic vasculature. Additionally, the provided display modes combine both vessel and device information and do not require the mental integration of different displays by the physician. The processing times were well within the range of conventional clinical frame rates.