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

Automatic vessel attenuation measurement for quality control of contrast-enhanced CT: Validation on the portal vein

BACKGROUND

Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast-enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams.

PURPOSE

The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast-enhanced abdomen CT examinations.

METHODS

Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast-enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21 examinations.

RESULTS

The best classifier was found to be a random forest, with a precision of 0.892 in the held-out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4 HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2 HU.

CONCLUSIONS

The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time-consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast-enhanced CT examinations.

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.

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 (R 2 = 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 (R 2 = 0.35 ) and worst-case (R 2 = 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.