Artifact‐reduced two‐dimensional cine steady state free precession for myocardial blood‐ oxygen‐level‐dependent imaging

Zhou, Xiangzhi; Tsaftaris, Sotirios A.; Liu, Ying; Tang, Richard; Klein, Rachel; Zuehlsdorff, Sven; Li, Debiao; Dharmakumar, Rohan

doi:10.1002/jmri.22116

Cited by 13 publications

(17 citation statements)

References 17 publications

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“…It is possible that distal coronary disease may have been missed. In future studies, diagnostic performance will be improved with the use of multislice imaging with parallel imaging techniques and artifact reduction methods (20,34).…”

Section: Performance Of Bold and Perfusion Imaging Formentioning

confidence: 99%

Myocardial Oxygenation in Coronary Artery Disease

Arnold

Karamitsos

Bhamra-Ariza

et al. 2012

Journal of the American College of Cardiology

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Section: Performance Of Bold and Perfusion Imaging Formentioning

confidence: 99%

Myocardial Oxygenation in Coronary Artery Disease

Arnold

Karamitsos

Bhamra-Ariza

et al. 2012

Journal of the American College of Cardiology

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“…While anesthetized and mechanically ventilated, the subjects were imaged using a clinical 1.5T MRI system during baseline conditions (i.e., without any occluder activation) and severe stenosis (> 90% LAD occlusion). As detailed in [5], CP–BOLD data were acquired mid-ventricle at rest with breath holding twice (pre and 20 minutes post occlusion) using a flow compensated steady state free procession (SSFP) BOLD cine sequence [7] with parameters: field of view, 240×145 mm 2 ; spatial resolution, 1.2×1.2×8 mm 3 ; readout bandwidth, 930 Hz/pixel; flip angle, 70°; TR/echo time (TE), 6.2/3.1 ms; temporal resolution 37.2 ms; and 24 time frames. Late Gadolinium Enhancement (LGE) images were acquired within 20 minutes post occlusion (to rule out early infarction) and after 3 hours of occlusion and during reperfusion (to identify myocardial regions succumbed to ischemic tissue damage), using the sequence described in [31] with parameters: spatial resolution, 1.3 × 1.3 × 8 mm 3 ; TE/TR, 3.9/8.2 ms; TI, 200 to 220 ms; flip angle, 25°; and readout bandwidth, 140 Hz/pixel.…”

Section: Methodsmentioning

confidence: 99%

“…CP–BOLD MRI is a new contrast agent- and stress-free imaging technique for the assessment of myocardial ischemia at rest [5], capturing simultaneously BOLD changes (≈10–20%) and wall motion in a single acquisition that can be viewed as a movie (cine BOLD) [6], [7]. Identifying ischemic territories at rest is important, since provocative stress can be contraindicated [8], carrying rare but serious risks [9].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Generation of Myocardial Blood–Oxygen-Level-Dependent MRI Time Series Via Structural Sparse Decomposition Modeling

Rusu

Morisi

Boschetto

et al. 2014

IEEE Trans. Med. Imaging

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This paper aims to identify approaches that generate appropriate synthetic data (computer generated) for Cardiac Phase-resolved Blood-Oxygen-Level-Dependent (CP–BOLD) MRI. CP–BOLD MRI is a new contrast agent- and stress-free approach for examining changes in myocardial oxygenation in response to coronary artery disease. However, since signal intensity changes are subtle, rapid visualization is not possible with the naked eye. Quantifying and visualizing the extent of disease relies on myocardial segmentation and registration to isolate the myocardium and establish temporal correspondences and ischemia detection algorithms to identify temporal differences in BOLD signal intensity patterns. If transmurality of the defect is of interest pixel-level analysis is necessary and thus a higher precision in registration is required. Such precision is currently not available affecting the design and performance of the ischemia detection algorithms. In this work, to enable algorithmic developments of ischemia detection irrespective to registration accuracy, we propose an approach that generates synthetic pixel-level myocardial time series. We do this by (a) modeling the temporal changes in BOLD signal intensity based on sparse multi-component dictionary learning, whereby segmentally derived myocardial time series are extracted from canine experimental data to learn the model; and (b) demonstrating the resemblance between real and synthetic time series for validation purposes. We envision that the proposed approach has the capacity to accelerate development of tools for ischemia detection while markedly reducing experimental costs so that cardiac BOLD MRI can be rapidly translated into the clinical arena for the noninvasive assessment of ischemic heart disease.

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“…The protocol, detailed in [3], included breath-held acquisitions at mid-ventricle position with a flow compensated CP–BOLD sequence [2] at baseline and at 20mins post-occlusion. Scan parameters were: field of view, 240×145 mm 2 ; spatial resolution, 1.2×1.2×8 mm 3 ; readout bandwidth, 930 Hz per pixel; flip angle, 70°; TR/echo time (TE), 6.2/3.1 ms; and temporal resolution 37.2 ms).…”

Section: Data and Pre-processingmentioning

confidence: 99%

“…CARDIAC Phase-resolved Blood-Oxygen-Level Dependent (CP–BOLD) Magnetic Resonance Imaging (MRI) is a state-of-the-art technique for directly examining changes in myocardial oxygenation without any contrast media [1], [2]. In a single acquisition it obtains both BOLD contrast and myocardial function that can be seen as a movie (effectively a cine BOLD acquisition).…”

Section: Introductionmentioning

confidence: 99%

Dictionary-Driven Ischemia Detection From Cardiac Phase-Resolved Myocardial BOLD MRI at Rest

Bevilacqua

Dharmakumar

Tsaftaris

2016

IEEE Trans. Med. Imaging

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Cardiac Phase-resolved Blood-Oxygen-Level Dependent (CP–BOLD) MRI provides a unique opportunity to image an ongoing ischemia at rest. However, it requires post-processing to evaluate the extent of ischemia. To address this, here we propose an unsupervised ischemia detection (UID) method which relies on the inherent spatio-temporal correlation between oxygenation and wall motion to formalize a joint learning and detection problem based on dictionary decomposition. Considering input data of a single subject, it treats ischemia as an anomaly and iteratively learns dictionaries to represent only normal observations (corresponding to myocardial territories remote to ischemia). Anomaly detection is based on a modified version of One-class Support Vector Machines (OCSVM) to regulate directly the margins by incorporating the dictionary-based representation errors. A measure of ischemic extent (IE) is estimated, reflecting the relative portion of the myocardium affected by ischemia. For visualization purposes an ischemia likelihood map is created by estimating posterior probabilities from the OCSVM outputs, thus obtaining how likely the classification is correct. UID is evaluated on synthetic data and in a 2D CP–BOLD data set from a canine experimental model emulating acute coronary syndromes. Comparing early ischemic territories identified with UID against infarct territories (after several hours of ischemia), we find that IE, as measured by UID, is highly correlated (Pearson’s r = 0.84) w.r.t. infarct size. When advances in automated registration and segmentation of CP–BOLD images and full coverage 3D acquisitions become available, we hope that this method can enable pixel-level assessment of ischemia with this truly non-invasive imaging technique.

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Artifact‐reduced two‐dimensional cine steady state free precession for myocardial blood‐ oxygen‐level‐dependent imaging

Cited by 13 publications

References 17 publications

Myocardial Oxygenation in Coronary Artery Disease

Myocardial Oxygenation in Coronary Artery Disease

Synthetic Generation of Myocardial Blood–Oxygen-Level-Dependent MRI Time Series Via Structural Sparse Decomposition Modeling

Dictionary-Driven Ischemia Detection From Cardiac Phase-Resolved Myocardial BOLD MRI at Rest

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