Accelerated whole‐heart 3D CSPAMM for myocardial motion quantification

Rutz, Andrea K.; Ryf, Salome; Plein, Sven; Boesiger, Peter; Kozerke, Sebastian

doi:10.1002/mrm.21363

Cited by 92 publications

(89 citation statements)

References 32 publications

(42 reference statements)

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“…All images were acquired during breath-holds of approximately 15 seconds and were gated to the vector ECG. 3DTAG datasets were obtained with three sequential breath-hold acquisitions in each orthogonal direction (TR/TE= 7.0/3.2ms, flip angle= 19-25 • , tag distance= 7mm) (Rutz et al, 2008). Images were acquired with reduced field-of-view enclosing the left ventricle.…”

Section: Mr Acquisitionmentioning

confidence: 99%

Benchmarking framework for myocardial tracking and deformation algorithms: An open access database

Tobon-Gómez

Craene

McLeod

et al. 2013

Medical Image Analysis

153

163

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In this paper we present a benchmarking framework for the validation of cardiac motion analysis algorithms. The reported methods are the response to an open challenge that was put to the medical imaging community through a MICCAI workshop. The database included magnetic resonance (MR) and 3D ultrasound (3DUS) datasets from a dynamic phantom and 15 healthy volunteers. Participants processed 3D tagged MR datasets (3DTAG), cine steady state free precession MR datasets (SSFP) and 3DUS datasets, amounting to 1158 image volumes. Ground-truth for motion tracking was based on 12 landmarks (4 walls at 3 ventricular levels). They were manually tracked by two observers in the 3DTAG data over the whole cardiac cycle, using an in-house application with 4D visualization capabilities. The median of the inter-observer variability was computed for the phantom dataset (0.77mm) and for the volunteer datasets (0.84mm). The ground-truth was registered to 3DUS coordinates using a point based similarity transform. Four institutions responded to the challenge by providing motion estimates for the data: Fraunhofer MEVIS (MEVIS), Bremen, Germany; Imperial College London -University College London (IUCL), UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Inria-Asclepios project (INRIA), France. Details on the implementation and evaluation of the four methodologies are presented in this manuscript. The manually tracked landmarks were used to evaluate tracking accuracy of all methodologies. For 3DTAG, median values were computed over all time frames for the phantom dataset (MEVIS=1.20mm, IUCL=0.73mm, UPF=1.10mm, INRIA=1.09mm) and for the volunteer datasets (MEVIS=1.33mm, IUCL=1.52mm, UPF=1.09mm, INRIA=1.32mm). For 3DUS, median values were computed at end diastole and end systole for the phantom dataset (MEVIS=4.40mm, UPF=3.48mm, INRIA=4.78mm) and for the volunteer datasets (MEVIS=3.51mm, UPF=3.71mm, INRIA=4.07mm). For SSFP, median values were computed at end diastole and end systole for the phantom dataset (UPF=6.18mm, INRIA=3.93mm) and for the volunteer datasets (UPF=3.09mm, INRIA=4.78mm). Finally, strain curves were generated and qualitatively compared. Good agreement was found between the different modalities and methodologies, except for radial strain that showed a high variability in cases of lower image quality.

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Section: Mr Acquisitionmentioning

confidence: 99%

Benchmarking framework for myocardial tracking and deformation algorithms: An open access database

Tobon-Gómez

Craene

McLeod

et al. 2013

Medical Image Analysis

153

163

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“…However, those modalities are not widely used in clinics and give access to limited components of these fields (often one or two dimensional). Even with tridimensional tagging [7], it remains challenging to extract accurately, and to evaluate the quality of the recovered motion from another imaging modality [6] as it requires intermodality temporal and spatial registration.…”

mentioning

confidence: 99%

Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images

Prakosa¹,

Sermesant²,

Delingette³

et al. 2013

IEEE Trans. Med. Imaging

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Abstract-We propose a new approach for the generation of synthetic but visually realistic time series of cardiac images based on an electromechanical model of the heart and real clinical 4D image sequences. This is achieved by combining three steps. The first step is the simulation of a cardiac motion using an electromechanical model of the heart and the segmentation of the end diastolic image of a cardiac sequence. We use biophysical parameters related to the desired condition of the simulated subject. The second step extracts the cardiac motion from the real sequence using non-rigid image registration. Finally, a synthetic time series of cardiac images corresponding to the simulated motion is generated in the third step by combining the motion estimated by image registration and the simulated one. With this approach, image processing algorithms can be evaluated as we know the ground-truth motion underlying the image sequence. Moreover, databases of visually realistic images of controls and patients can be generated for which the underlying cardiac motion and some biophysical parameters are known.Such databases can open new avenues for machine learning approaches.

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“…LV wall motion, thickening, and strain can be measured also by using the myocardial tagging technique, 75,76 which is performed with cine MR imaging by applying a special radio-frequency prepulse immediately following detection of R wave on the ECG tracing. 77 Myocardial tagging allows the determination and quantification of LV wall thickening of specific myocardial segments.…”

Section: Left Ventricular Wall Motion Analysis By Cmrmentioning

confidence: 99%

Correlation of Electrocardiogram and Regional Cardiac Magnetic Resonance Imaging Findings in ST‐Elevation Myocardial Infarction: A Literature Review

Rinta-Kiikka

Tuohinen

Ryymin

et al. 2014

Noninvasive Electrocardiol

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Background:Patients with acute ST-elevation myocardial infarction (STEMI) benefit substantially from emergent coronary reperfusion. The principal mechanism is to open the occluded coronary artery to minimize myocardial injury. Thus the size of the area at risk is a critical determinant of the patient outcome, although other factors, such as reperfusion injury, have major impact on the final infarct size. Acute coronary occlusion almost immediately induces metabolic changes within the myocardium, which can be assessed with both the electrocardiogram (ECG) and cardiac magnetic resonance (CMR) imaging.Methods: The 12-lead ECG is the principal diagnostic method to detect and risk-stratify acute STEMI. However, to achieve a correct diagnosis, it is paramount to compare different ECG parameters with golden standards in imaging, such as CMR. In this review, we discuss aspects of ECG and CMR in the assessment of acute regional ischemic changes in the myocardium using the 17 segment model of the left ventricle presented by American Heart Association (AHA), and their relation to coronary artery anatomy.Results: Using the 17 segment model of AHA, the segments 12 and 16 remain controversial. There is an important overlap in myocardial blood supply at the antero-lateral region between LAD and LCx territories concerning these two segments.Conclusion: No all-encompassing correlation can be found between ECG and CMR findings in acute ischemia with respect to coronary anatomy.

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Accelerated whole‐heart 3D CSPAMM for myocardial motion quantification

Cited by 92 publications

References 32 publications

Benchmarking framework for myocardial tracking and deformation algorithms: An open access database

Benchmarking framework for myocardial tracking and deformation algorithms: An open access database

Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images

Correlation of Electrocardiogram and Regional Cardiac Magnetic Resonance Imaging Findings in ST‐Elevation Myocardial Infarction: A Literature Review

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