Dataset Information

Fully quantitative pixel-wise analysis of cardiovascular magnetic resonance perfusion improves discrimination of dark rim artifact from perfusion defects associated with epicardial coronary stenosis.

ABSTRACT: BACKGROUND:Dark rim artifacts in first-pass cardiovascular magnetic resonance (CMR) perfusion images can mimic perfusion defects and affect diagnostic accuracy for coronary artery disease (CAD). We evaluated whether quantitative myocardial blood flow (MBF) can differentiate dark rim artifacts from true perfusion defects in CMR perfusion. METHODS:Regadenoson perfusion CMR was performed at 1.5 T in 76 patients. Significant CAD was defined by quantitative invasive coronary angiography (QCA) ??50% diameter stenosis. Non-significant CAD (NonCAD) was defined as stenosis by QCA

SUBMITTER: Ta AD

PROVIDER: S-EPMC5842542 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Fully quantitative pixel-wise analysis of cardiovascular magnetic resonance perfusion improves discrimination of dark rim artifact from perfusion defects associated with epicardial coronary stenosis.

Ta Allison D AD Hsu Li-Yueh LY Conn Hannah M HM Winkler Susanne S Greve Anders M AM Shanbhag Sujata M SM Chen Marcus Y MY Patricia Bandettini W W Arai Andrew E AE

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 20180308 1

<h4>Background</h4>Dark rim artifacts in first-pass cardiovascular magnetic resonance (CMR) perfusion images can mimic perfusion defects and affect diagnostic accuracy for coronary artery disease (CAD). We evaluated whether quantitative myocardial blood flow (MBF) can differentiate dark rim artifacts from true perfusion defects in CMR perfusion.<h4>Methods</h4>Regadenoson perfusion CMR was performed at 1.5 T in 76 patients. Significant CAD was defined by quantitative invasive coronary angiograph ...[more]

PMID: 29514708

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Project description:ObjectivesThe authors developed a fully automated framework to quantify myocardial blood flow (MBF) from contrast-enhanced cardiac magnetic resonance (CMR) perfusion imaging and evaluated its diagnostic performance in patients.BackgroundFully quantitative CMR perfusion pixel maps were previously validated with microsphere MBF measurements and showed potential in clinical applications, but the methods required laborious manual processes and were excessively time-consuming.MethodsCMR perfusion imaging was performed on 80 patients with known or suspected coronary artery disease (CAD) and 17 healthy volunteers. Significant CAD was defined by quantitative coronary angiography (QCA) as ≥70% stenosis. Nonsignificant CAD was defined by: 1) QCA as <70% stenosis; or 2) coronary computed tomography angiography as <30% stenosis and a calcium score of 0 in all vessels. Automatically generated MBF maps were compared with manual quantification on healthy volunteers. Diagnostic performance of the automated MBF pixel maps was analyzed on patients using absolute MBF, myocardial perfusion reserve (MPR), and relative measurements of MBF and MPR.ResultsThe correlation between automated and manual quantification was excellent (r = 0.96). Stress MBF and MPR in the ischemic zone were lower than those in the remote myocardium in patients with significant CAD (both p < 0.001). Stress MBF and MPR in the remote zone of the patients were lower than those in the normal volunteers (both p < 0.001). All quantitative metrics had good area under the curve (0.864 to 0.926), sensitivity (82.9% to 91.4%), and specificity (75.6% to 91.1%) on per-patient analysis. On a per-vessel analysis of the quantitative metrics, area under the curve (0.837 to 0.864), sensitivity (75.0% to 82.7%), and specificity (71.8% to 80.9%) were good.ConclusionsFully quantitative CMR MBF pixel maps can be generated automatically, and the results agree well with manual quantification. These methods can discriminate regional perfusion variations and have high diagnostic performance for detecting significant CAD. (Technical Development of Cardiovascular Magnetic Resonance Imaging; NCT00027170).

Project description:ObjectivesThis study sought to assess the performance of cardiovascular magnetic resonance (CMR) myocardial perfusion mapping against invasive coronary physiology reference standards for detecting coronary artery disease (CAD, defined by fractional flow reserve [FFR] ≤0.80), microvascular dysfunction (MVD) (defined by index of microcirculatory resistance [IMR] ≥25) and the ability to differentiate between the two.BackgroundDifferentiation of epicardial (CAD) and MVD in patients with stable angina remains challenging. Automated in-line CMR perfusion mapping enables quantification of myocardial blood flow (MBF) to be performed rapidly within a clinical workflow.MethodsFifty patients with stable angina and 15 healthy volunteers underwent adenosine stress CMR at 1.5T with quantification of MBF and myocardial perfusion reserve (MPR). FFR and IMR were measured in 101 coronary arteries during subsequent angiography.ResultsTwenty-seven patients had obstructive CAD and 23 had nonobstructed arteries (7 normal IMR, 16 abnormal IMR). FFR positive (epicardial stenosis) areas had significantly lower stress MBF (1.47 ± 0.48 ml/g/min) and MPR (1.75 ± 0.60) than FFR-negative IMR-positive (MVD) areas (stress MBF: 2.10 ± 0.35 ml/g/min; MPR: 2.41 ± 0.79) and normal areas (stress MBF: 2.47 ± 0.50 ml/g/min; MPR: 2.94 ± 0.81). Stress MBF ≤1.94 ml/g/min accurately detected obstructive CAD on a regional basis (area under the curve: 0.90; p < 0.001). In patients without regional perfusion defects, global stress MBF <1.82 ml/g/min accurately discriminated between obstructive 3-vessel disease and MVD (area under the curve: 0.94; p < 0.001).ConclusionsThis novel automated pixel-wise perfusion mapping technique can be used to detect physiologically significant CAD defined by FFR, MVD defined by IMR, and to differentiate MVD from multivessel coronary disease. A CMR-based diagnostic algorithm using perfusion mapping for detection of epicardial disease and MVD warrants further clinical validation.

Project description:The purpose of the dataset is to provide annotated images for pixel classification tasks with application to powered wheelchair users. As some of the widely available datasets contain only general objects, we introduced this dataset to cover the missing pieces, which can be considered as application-specific objects. However, these objects of interest are not only important for powered wheelchair users but also for indoor navigation and environmental understanding in general. For example, indoor assistive and service robots need to comprehend their surroundings to ease navigation and interaction with different size objects. The proposed dataset is recorded using a camera installed on a powered wheelchair. The camera is installed beneath the joystick so that it can have a clear vision with no obstructions from the user's body or legs. The powered wheelchair is then driven through the corridors of the indoor environment, and a one-minute video is recorded. The collected video is annotated on the pixel level for semantic segmentation (pixel classification) tasks. Pixels of different objects are annotated using MATLAB software. The dataset has various object sizes (small, medium, and large), which can explain the variation of the pixel's distribution in the dataset. Usually, Deep Convolutional Neural Networks (DCNNs) that perform well on large-size objects fail to produce accurate results on small-size objects. Whereas training a DCNN on a multi-size objects dataset can build more robust systems. Although the recorded objects are vital for many applications, we have included more images of different kinds of door handles with different angles, orientations, and illuminations as they are rare in the publicly available datasets. The proposed dataset has 1549 images and covers nine different classes. We used the dataset to train and test a semantic segmentation system that can aid and guide visually impaired users by providing visual cues. The dataset is made publicly available at this link.

Dataset Information

Fully quantitative pixel-wise analysis of cardiovascular magnetic resonance perfusion improves discrimination of dark rim artifact from perfusion defects associated with epicardial coronary stenosis.

Publications

Fully quantitative pixel-wise analysis of cardiovascular magnetic resonance perfusion improves discrimination of dark rim artifact from perfusion defects associated with epicardial coronary stenosis.

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