Correlation Between CT Measured Pulmonary Artery Size And Invasively Confirmed Pulmonary Artery Pressures

Carabenciov, Darin; Lundy, Taylor A.; Burger, Charles D.; Lee, Augustine

doi:10.1164/ajrccm-conference.2012.185.1_meetingabstracts.a1886

Cited by 2 publications

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“…The main PA diameter normalized to the aortic diameter (PA/Ao) did significantly correlate with the MPAP as measured on a RHC, but weakly (r = 0.28, p = 0.0032). Furthermore, its discriminatory ability for PH was poor with an AUC of 0.61 [44], making its clinical utility in our population unlikely.…”

Section: Diagnostic Utility Of Pa Sizementioning

confidence: 92%

The Significance of Pulmonary Artery Size in Pulmonary Hypertension

et al. 2014

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Pulmonary hypertension (PH) has been found to have significant morbidity and mortality. The treatment of PH has advanced considerably with increasingly more effective and safer options. With an increasing effort to diagnose patients early, non-invasive techniques are often used to screen those patients likely to have PH. Computerized tomography (CT) chest scans are increasingly utilized in the evaluation of patients with exertional dyspnea, including those with suspected PH. The main role of the CT scan is to evaluate for any associated underlying diseases. There have been attempts to address the utility of CT to predict the presence of PH. This article reviews previously published investigations to summarize the relationship between pulmonary artery dimensions and PH to determine both the strength of the correlation and its discriminatory ability for use in clinical practice.

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Section: Diagnostic Utility Of Pa Sizementioning

confidence: 92%

The Significance of Pulmonary Artery Size in Pulmonary Hypertension

et al. 2014

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“…Computed tomography pulmonary angiography (CTPA) is a crucial imaging investigation performed in patients with suspected pulmonary embolism (PE) and in the work up of patients with suspected pulmonary hypertension (PH) ( 1 ). Current imaging approaches in pulmonary vascular disease rely on visual assessments or manual measurements of cardiac, pulmonary arterial and aortic size; such measures are used to risk stratify patients with acute PE ( 5 – 8 ) and diagnose PH ( 5 , 6 ).…”

Section: Introductionmentioning

confidence: 99%

Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning

Sharkey

Taylor

Alabed

et al. 2022

Front. Cardiovasc. Med.

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IntroductionComputed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA.MethodsA nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort.ResultsDice similarity coefficients (DSC) for segmented structures were in the range 0.58–0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785–0.801) and 0.520 (0.482–0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar performance in the external test cohort. The failure rates in mixed pulmonary vascular disease were low (<3.9%) indicating good generalisability of the model to different diseases.ConclusionFully automated segmentation of the four cardiac chambers and great vessels has been achieved in CTPA with high accuracy and low rates of failure. DL volumetric biomarkers can potentially improve CTPA cardiac assessment and invasive haemodynamic prediction.

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Correlation Between CT Measured Pulmonary Artery Size And Invasively Confirmed Pulmonary Artery Pressures

Cited by 2 publications

References 0 publications

The Significance of Pulmonary Artery Size in Pulmonary Hypertension

The Significance of Pulmonary Artery Size in Pulmonary Hypertension

Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning

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