19,687 research outputs found

    Automatic Characterization of Myocardial Perfusion in Contrast Enhanced MRI

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    The use of contrast medium in cardiac MRI allows joining the high-resolution anatomical information provided by standard magnetic resonance with functional information obtained by means of the perfusion of contrast agent in myocardial tissues. The current approach to perfusion MRI characterization is the qualitative one, based on visual inspection of images. Moving to quantitative analysis requires extraction of numerical indices of myocardium perfusion by analysis of time/intensity curves related to the area of interest. The main problem in quantitative image sequence analysis is the heart movement, mainly due to patient respiration. We propose an automatic procedure based on image registration, segmentation of the myocardium, and extraction and analysis of time/intensity curves. The procedure requires a minimal user interaction, is robust with respect to the user input, and allows effective characterization of myocardial perfusion. The algorithm was tested on cardiac MR images acquired from voluntaries and in clinical routine

    Does quantification of myocardial perfusion SPECT study differ while image reconstruction is carried out using iteration algorithm instead of filtered back-projection? - preliminary report

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    BACKGROUND: The purpose of this study was to compare the performance of two reconstruction algorithms: conventional filtered back-projection (FBP) and an iterative algorithm -ITW- in quantitative analysis of myocardial perfusion SPECT studies. The defect size and defect severity were assessed on 99m Tc - MIBI images reconstructed using both methods and estimation of sensitivity in the detection of perfusion deficits and myocardial viability were performed as well. METHODS AND RESULTS: The study group comprised 43 patients (38 men and 5 women) in the age of 40-73 years (mean 59 years). Heart perfusion scintigraphy was performed following an injection of 22 to 25 mCi 99m Tc-MIBI for exercise and rest myocardial perfusion study. Images were reconstructed using FBP and ITW algorithms. Defect size (DS) was quantified by a threshold program and CEqual programme. Defect severity (nadir) was calculated as the ratio of minimal/maximum counts from bull?s eye polar map. Coronary arteriography was performed in all patients. RESULTS: Defect size calculated by threshold method on resting images did not differ between reconstruction methods (p=0.61 for cut-off 50% and p = 0.24 for cut-off 60%); defect severity was higher on images reconstructed with ITW (CI 0.95 = 2.4%¸5.2% of maximal counts). CONCLUSIONS: Sensitivity for detection of heart perfusion defects and estimation of myocardial viability were similar on images reconstructed by both algorithms

    Quantitative Myocardial Perfusion Imaging Versus Visual Analysis in Diagnosing Myocardial Ischemia

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    Objectives: This study sought to compare the diagnostic accuracy of visual and quantitative analyses of myocardial perfusion cardiovascular magnetic resonance against a reference standard of quantitative coronary angiography. Background: Visual analysis of perfusion cardiovascular magnetic resonance studies for assessing myocardial perfusion has been shown to have high diagnostic accuracy for coronary artery disease. However, only a few small studies have assessed the diagnostic accuracy of quantitative myocardial perfusion. Methods: This retrospective study included 128 patients randomly selected from the CE-MARC (Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease) study population such that the distribution of risk factors and disease status was proportionate to the full population. Visual analysis results of cardiovascular magnetic resonance perfusion images, by consensus of 2 expert readers, were taken from the original study reports. Quantitative myocardial blood flow estimates were obtained using Fermi-constrained deconvolution. The reference standard for myocardial ischemia was a quantitative coronary x-ray angiogram stenosis severity of ≥70% diameter in any coronary artery of >2 mm diameter, or ≥50% in the left main stem. Diagnostic performance was calculated using receiver-operating characteristic curve analysis. Results: The area under the curve for visual analysis was 0.88 (95% confidence interval: 0.81 to 0.95) with a sensitivity of 81.0% (95% confidence interval: 69.1% to 92.8%) and specificity of 86.0% (95% confidence interval: 78.7% to 93.4%). For quantitative stress myocardial blood flow the area under the curve was 0.89 (95% confidence interval: 0.83 to 0.96) with a sensitivity of 87.5% (95% confidence interval: 77.3% to 97.7%) and specificity of 84.5% (95% confidence interval: 76.8% to 92.3%). There was no statistically significant difference between the diagnostic performance of quantitative and visual analyses (p = 0.72). Incorporating rest myocardial blood flow values to generate a myocardial perfusion reserve did not significantly increase the quantitative analysis area under the curve (p = 0.79). Conclusions: Quantitative perfusion has a high diagnostic accuracy for detecting coronary artery disease but is not superior to visual analysis. The incorporation of rest perfusion imaging does not improve diagnostic accuracy in quantitative perfusion analysis

    Coronary microvascular ischemia in hypertrophic cardiomyopathy - a pixel-wise quantitative cardiovascular magnetic resonance perfusion study.

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    BACKGROUND: Microvascular dysfunction in HCM has been associated with adverse clinical outcomes. Advances in quantitative cardiovascular magnetic resonance (CMR) perfusion imaging now allow myocardial blood flow to be quantified at the pixel level. We applied these techniques to investigate the spectrum of microvascular dysfunction in hypertrophic cardiomyopathy (HCM) and to explore its relationship with fibrosis and wall thickness. METHODS: CMR perfusion imaging was undertaken during adenosine-induced hyperemia and again at rest in 35 patients together with late gadolinium enhancement (LGE) imaging. Myocardial blood flow (MBF) was quantified on a pixel-by-pixel basis from CMR perfusion images using a Fermi-constrained deconvolution algorithm. Regions-of-interest (ROI) in hypoperfused and hyperemic myocardium were identified from the MBF pixel maps. The myocardium was also divided into 16 AHA segments. RESULTS: Resting MBF was significantly higher in the endocardium than in the epicardium (mean ± SD: 1.25 ± 0.35 ml/g/min versus 1.20 ± 0.35 ml/g/min, P < 0.001), a pattern that reversed with stress (2.00 ± 0.76 ml/g/min versus 2.36 ± 0.83 ml/g/min, P < 0.001). ROI analysis revealed 11 (31%) patients with stress MBF lower than resting values (1.05 ± 0.39 ml/g/min versus 1.22 ± 0.36 ml/g/min, P = 0.021). There was a significant negative association between hyperemic MBF and wall thickness (β = −0.047 ml/g/min per mm, 95% CI: −0.057 to −0.038, P < 0.001) and a significantly lower probability of fibrosis in a segment with increasing hyperemic MBF (odds ratio per ml/g/min: 0.086, 95% CI: 0.078 to 0.095, P = 0.003). CONCLUSIONS: Pixel-wise quantitative CMR perfusion imaging identifies a subgroup of patients with HCM that have localised severe microvascular dysfunction which may give rise to myocardial ischemia

    Spatio-Temporal Modelling of Perfusion Cardiovascular MRI

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    Myocardial perfusion MRI provides valuable insight into how coronary artery and microvascular diseases affect myocardial tissue. Stenosis in a coronary vessel leads to reduced maximum blood flow (MBF), but collaterals may secure the blood supply of the myocardium but with altered tracer kinetics. To date, quantitative analysis of myocardial perfusion MRI has only been performed on a local level, largely ignoring the contextual information inherent in different myocardial segments. This paper proposes to quantify the spatial dependencies between the local kinetics via a Hierarchical Bayesian Model (HBM). In the proposed framework, all local systems are modelled simultaneously along with their dependencies, thus allowing more robust context-driven estimation of local kinetics. Detailed validation on both simulated and patient data is provided

    Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

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    In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted and clustered encodings. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis.Comment: This paper was submitted in April 2017 and accepted in November 2017 for publication in Medical Image Analysis. Please cite as: Zreik et al., Medical Image Analysis, 2018, vol. 44, pp. 72-8
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