Fully automated tool to identify the aorta and compute flow using phase‐contrast MRI: Validation and application in a large population based study

Goel, Akshay; McColl, Roderick W; King, Kevin S.; Whittemore, Anthony R; Peshock, Ronald M

doi:10.1002/jmri.24338

Cited by 20 publications

(17 citation statements)

References 38 publications

(41 reference statements)

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“…Manual user input is required in one image for initialization of the proposed segmentation method. A previous study has demonstrated a method for automatic identification of the ascending and descending aorta in 2D PC-MR images, assuming a strictly circular vessel lumen (Goel et al, 2014), which is not applicable on non-circular structures such as the pulmonary artery. Bratt et al (2019) recently provided proof of concept for a promising deep learning algorithm for fully automatic time-resolved segmentation of aortic blood flow with similar bias and variability as the current study.…”

Section: Comparison With Earlier Studies and Future Workmentioning

confidence: 99%

A new vessel segmentation algorithm for robust blood flow quantification from two‐dimensional phase‐contrast magnetic resonance images

Bidhult

Hedström

Carlsson

et al. 2019

Clin Physio Funct Imaging

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Blood flow measurements in the ascending aorta and pulmonary artery from phasecontrast magnetic resonance images require accurate time-resolved vessel segmentation over the cardiac cycle. Current semi-automatic segmentation methods often involve time-consuming manual correction, relying on user experience for accurate results. The purpose of this study was to develop a semi-automatic vessel segmentation algorithm with shape constraints based on manual vessel delineations for robust segmentation of the ascending aorta and pulmonary artery, to evaluate the proposed method in healthy volunteers and patients with heart failure and congenital heart disease, to validate the method in a pulsatile flow phantom experiment, and to make the method freely available for research purposes. Algorithm shape constraints were extracted from manual reference delineations of the ascending aorta (n = 20) and pulmonary artery (n = 20) and were included in a semi-automatic segmentation method only requiring manual delineation in one image. Bias and variability (bias AE SD) for flow volume of the proposed algorithm versus manual reference delineations were 0Á0 AE 1Á9 ml in the ascending aorta (n = 151; seven healthy volunteers; 144 heart failure patients) and À1Á7 AE 2Á9 ml in the pulmonary artery (n = 40; 25 healthy volunteers; 15 patients with atrial septal defect). Interobserver bias and variability were lower (P = 0Á008) for the proposed semi-automatic method (À0Á1 AE 0Á9 ml) compared to manual reference delineations (1Á5 AE 5Á1 ml). Phantom validation showed good agreement between the proposed method and timer-and-beaker flow volumes (0Á4 AE 2Á7 ml). In conclusion, the proposed semiautomatic vessel segmentation algorithm can be used for efficient analysis of flow and shunt volumes in the aorta and pulmonary artery.

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Section: Comparison With Earlier Studies and Future Workmentioning

confidence: 99%

A new vessel segmentation algorithm for robust blood flow quantification from two‐dimensional phase‐contrast magnetic resonance images

Bidhult

Hedström

Carlsson

et al. 2019

Clin Physio Funct Imaging

View full text Add to dashboard Cite

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“…Automated quantitative flow analysis is dependent on the reliable identification of the vessel or region of interest, as well as its subsequent segmentation. To this end, Goel et al demonstrated the feasibility of a robust fully automated tool to localize the ascending (AAo) and descending aorta (DAo) from acquired images using the Hough transform [83]. Hough transform assigns high values to circular objects and outputs these values as a spatial density map.…”

Section: Automated Image Interpretation In Cardiologymentioning

confidence: 99%

Cardiac imaging: working towards fully-automated machine analysis & interpretation

Slomka

Dey

Sitek

et al. 2017

Expert Review of Medical Devices

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Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

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“…Goel et al proposed a possible solution to the localization of ascending and descending aorta on 2D PC-MRI images in 2014. However, once detected, vessel lumen boundaries were approximated to a perfect circle, so that local distensibility parameters could not be calculated [12]. In the last years, other research groups proposed semi-automated methods for aortic lumen segmentation still requiring an operator to perform a manual initialization of the segmentation process [8,11].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Although they developed a robust localization algorithm, after being localized, the aortic lumen was just approximated as a perfect circle. For this reason, their solution did not replace previous approaches that semi-automatically segment the aorta because an accurate segmentation and the adherence to the actual anatomical structure is fundamental to evaluate local aortic elasticity parameters [12].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Fully automated contour detection of the ascending aorta in cardiac 2D phase-contrast MRI

Codari

Scarabello

Secchi

et al. 2018

Magnetic Resonance Imaging

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Purpose: In this study we proposed a fully automated method for localizing and segmenting the ascending aortic lumen with phase-contrast magnetic resonance imaging (PC-MRI). Material and Methods:Twenty-five phase-contrast series were randomly selected out of a large population dataset of patients whose cardiac MRI examination, performed from September 2008 to October 2013, was unremarkable. The local Ethics Committee approved this retrospective study.The ascending aorta was automatically identified on each phase of the cardiac cycle using a priori knowledge of aortic geometry. The frame that maximized the area, eccentricity, and solidity parameters was chosen for unsupervised initialization. Aortic segmentation was performed on each frame using active contouring without edges techniques. The entire algorithm was developed using Matlab R2016b. To validate the proposed method, the manual segmentation performed by a highly experienced operator was used. Dice similarity coefficient, Bland-Altman analysis, and Pearson's correlation coefficient were used as performance metrics. Results:Comparing automated and manual segmentation of the aortic lumen on 714 images, Bland-Altman analysis showed a bias of -6.68 mm 2 , a coefficient of repeatability of 91.22 mm 2 , a mean area measurement of 581.40 mm 2 , and a reproducibility of 85%. Automated and manual segmentation were highly correlated (R = 0.98). The Dice similarity coefficient versus the manual reference standard was 94.6 ± 2.1% (mean ± standard deviation). Conclusion:A fully automated and robust method for identification and segmentation of ascending aorta on PC-MRI was developed. Its application on patients with a variety of pathologic conditions is advisable.

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Fully automated tool to identify the aorta and compute flow using phase‐contrast MRI: Validation and application in a large population based study

Cited by 20 publications

References 38 publications

A new vessel segmentation algorithm for robust blood flow quantification from two‐dimensional phase‐contrast magnetic resonance images

A new vessel segmentation algorithm for robust blood flow quantification from two‐dimensional phase‐contrast magnetic resonance images

Cardiac imaging: working towards fully-automated machine analysis & interpretation

Fully automated contour detection of the ascending aorta in cardiac 2D phase-contrast MRI

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