Detecting Myocardial Ischemia at Rest With Cardiac Phase–Resolved Blood Oxygen Level–Dependent Cardiovascular Magnetic Resonance

Tsaftaris, Sotirios A.; Zhou, Xiangzhi; Tang, Richard; Li, Debiao; Dharmakumar, Rohan

doi:10.1161/circimaging.112.976076

Cited by 28 publications

(56 citation statements)

References 34 publications

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“…BOLD imaging has been demonstrated to detect ischemia in stress and rest in preclinical models and also in early human trials. 59,60 Stress CMR confers several advantages over other noninvasive imaging modalities. The high spatial and temporal reso lutions of CMR in addition to lack of ionizing radiation allow for better delineation of anatomic, structural, and functional information.…”

Section: Cardiovascular Magnetic Resonance Overviewmentioning

confidence: 99%

Diagnostic usefulness of myocardial perfusion imaging in patients reluctant to undergo angiography

Narang¹,

Singh²,

Patel³

2016

RRCC

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Abstract:Patients with known or suspected coronary artery disease are often referred for ischemic testing to aid in risk assessment and guide management when symptoms develop. Invasive coronary angiography and percutaneous intervention are typically reserved for patients with symptoms refractory to medical management. The use of noninvasive modalities with myocardial perfusion imaging is a powerful diagnostic and prognostic tool for patients reluctant to undergo angiography. This review focuses on evaluation of coronary artery disease with myocardial perfusion imaging using single-photon emission computerized tomography, positron emission tomography, myocardial contrast echocardiography, cardiovascular magnetic resonance, and cardiovascular computerized tomography.

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Section: Cardiovascular Magnetic Resonance Overviewmentioning

confidence: 99%

Diagnostic usefulness of myocardial perfusion imaging in patients reluctant to undergo angiography

Narang¹,

Singh²,

Patel³

2016

RRCC

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“…2D short-axis images of the whole cardiac cycle (2D+time, cine) were acquired at baseline and severe ischemia (inflicted as stenosis of the left-anterior descending coronary artery (LAD)) on a 1.5T Espree (Siemens Healthcare) in the same 10 canines along the mid ventricle using both standard CINE and a flow and motion compensated CP-BOLD acquisition within few minutes of each other [17]. All quantitative experiments are performed in a strict leave-one-subject-out cross-validation.…”

Section: Data Preparation and Parameter Settingsmentioning

confidence: 99%

“…However, since most of the proposed algorithms rely on a (dis)similarity metric build based on the assumptions of consistent intensity and local shape, images with pathologies and locally varying intensity may not be accurately aligned. One example of that is the registration of image sequences of Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MR. CP-BOLD MR is a truly noninvasive method for early diagnosis of an ongoing ischemia, observing changes in myocardial signal intensity patterns as a function of cardiac phase [17]. As Figure 1 illustrates, time series of intensity vary as a function of cardiac phase when BOLD effect is present -it appears maximal in systole and minimal in diastole.…”

Section: Introductionmentioning

confidence: 99%

Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR

Öksüz

Mukhopadhyay

Bevilacqua

et al. 2015

Lecture Notes in Computer Science

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Abstract. Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MRI is a new contrast agent-and stress-free imaging technique for the assessment of myocardial ischemia at rest. The precise registration among the cardiac phases in this cine type acquisition is essential for automating the analysis of images of this technique, since it can potentially lead to better specificity of ischemia detection. However, inconsistency in myocardial intensity patterns and the changes in myocardial shape due to the heart's motion lead to low registration performance for state-of-the-art methods. This low accuracy can be explained by the lack of distinguishable features in CP-BOLD and inappropriate metric definitions in current intensity-based registration frameworks. In this paper, the sparse representations, which are defined by a discriminative dictionary learning approach for source and target images, are used to improve myocardial registration. This method combines appearance with Gabor and HOG features in a dictionary learning framework to sparsely represent features in a low dimensional space. The sum of absolute differences of these distinctive sparse representations are used to define a similarity term in the registration framework. The proposed approach is validated on a dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canines.

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“…An emerging cinelike cardiac modality is Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MR, a truly noninvasive method that identifies the ischemic myocardium by examining changes in myocardial signal intensity patterns as a function of cardiac phase [19].…”

Section: Introductionmentioning

confidence: 99%

“…Fully supervised myocardial segmentation (i.e., separating myocardium from the rest of the anatomy) developed for standard CINE MR, however, underperform in the case of CP-BOLD MR due to the spatio-temporal intensity variations of the myocardial BOLD effect [14,19]. Thus, in addition to violating shape invariance (as with standard CINE MR), the principal assumption of appearance invariance (consistent intensity [12]) is violated in CP-BOLD MR as well.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Myocardial Segmentation for Cardiac MRI

Mukhopadhyay

Öksüz

Bevilacqua

et al. 2015

Lecture Notes in Computer Science

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Abstract. Though unsupervised segmentation was a de-facto standard for cardiac MRI segmentation early on, recently cardiac MRI segmentation literature has favored fully supervised techniques such as Dictionary Learning and Atlas-based techniques. But, the benefits of unsupervised techniques e.g., no need for large amount of training data and better potential of handling variability in anatomy and image contrast, is more evident with emerging cardiac MR modalities. For example, CP-BOLD is a new MRI technique that has been shown to detect ischemia without any contrast at stress but also at rest conditions. Although CP-BOLD looks similar to standard CINE, changes in myocardial intensity patterns and shape across cardiac phases, due to the heart's motion, BOLD effect and artifacts affect the underlying mechanisms of fully supervised segmentation techniques resulting in a significant drop in segmentation accuracy. In this paper, we present a fully unsupervised technique for segmenting myocardium from the background in both standard CINE MR and CP-BOLD MR. We combine appearance with motion information (obtained via Optical Flow) in a dictionary learning framework to sparsely represent important features in a low dimensional space and separate myocardium from background accordingly. Our fully automated method learns background-only models and one class classifier provides myocardial segmentation. The advantages of the proposed technique are demonstrated on a dataset containing CP-BOLD MR and standard CINE MR image sequences acquired in baseline and ischemic condition across 10 canine subjects, where our method outperforms state-of-the-art supervised segmentation techniques in CP-BOLD MR and performs at-par for standard CINE MR.

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Detecting Myocardial Ischemia at Rest With Cardiac Phase–Resolved Blood Oxygen Level–Dependent Cardiovascular Magnetic Resonance

Cited by 28 publications

References 34 publications

Diagnostic usefulness of myocardial perfusion imaging in patients reluctant to undergo angiography

Diagnostic usefulness of myocardial perfusion imaging in patients reluctant to undergo angiography

Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR

Unsupervised Myocardial Segmentation for Cardiac MRI

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