Single-breath-hold 3-D CINE imaging of the left ventricle using Cartesian sampling

Wetzl, Jens; Schmidt, Michaela; Pontana, François; Longère, Benjamin; Lugauer, Felix; Maier, Andreas; Hornegger, Joachim; Forman, Christoph

doi:10.1007/s10334-017-0624-1

Cited by 37 publications

(46 citation statements)

References 35 publications

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“…The imaging protocol proposed in Section 3.1 is composed of the data acquisition suggested in [26] and reconstruction as described in [27] with the goal to capture the proximal coronary arteries in a single breath-hold scan. For detailed experiments and corresponding evaluation of the acceleration strategies please refer to the original manuscripts.…”

Section: Methodsmentioning

confidence: 99%

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Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography

et al. 2018

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Congenital anomalies of the coronary ostia can lead to sudden death. A screening solution would be useful to prevent adverse outcomes for the affected individuals. To be considered for integration into clinical routine, such a procedure must meet strict constraints in terms of invasiveness, time and user interaction. Imaging must be fast and seamlessly integrable into the clinical process. Non-contrast enhanced coronary magnetic resonance angiography (MRA) is well suited for this. Furthermore, planar reformations proved effective to reduce the acquired volumetric datasets to 2D images. These usually require time consuming user interaction, though. To fulfill the aforementioned challenges, we present a fully automated solution for imaging and reformatting of the proximal coronary arteries which enables rapid screening of these. The proposed pipeline consists of: (I) highly accelerated single breath-hold MRA data acquisition, (II) coronary ostia detection and vessel centerline extraction, and (III) curved planar reformation of the proximal coronary arteries, as well as multiplanar reformation of the coronary ostia. The procedure proved robust and effective in ten volunteer data sets. Imaging of the proximal coronary arteries took 24 ± 5 s and was successful within one breath-hold for all patients. The extracted centerlines achieve an overlap of 0.76 ± 0.18 compared to the reference standard and the average distance of the centerline points from the spherical surface for reformation was 1.1 ± 0.51 mm. The promising results encourage further experiments on patient data, particularly in coronary ostia anomaly screening.

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Section: Methodsmentioning

confidence: 99%

“…Upon completion of the data acquisition, image formation is performed using a compressed sensing reconstruction as described in [27]. Regarding (2), a spatio-temporal wavelet method was adopted for regularization.…”

Section: Data Acquisitionmentioning

confidence: 99%

Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography

et al. 2018

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“…In cardiac MRI, can be chosen e.g., as the temporal Fourier transform, spatio-temporal total variation, or spatio-temporal wavelets (Figure 2). CS has been extensively used in numerous cardiac applications, such as cardiac cine imaging (14,15), firstpass cardiac perfusion (16), 3D late gadolinium enhancement (LGE) imaging (17), 3D whole-heart coronary MR angiography (CMRA), and more recently for 4D and 5D free-running CMRA (18)(19)(20)(21), among many others. We briefly review some of those techniques in the next paragraphs.…”

Section: Cs For Cmr Imagingmentioning

confidence: 99%

“…Acquisition was performed on five healthy subjects using a golden radial pseudorandom sampling and non-rigid respiratory motion-corrected reconstruction with CS temporal regularization was performed offline (reconstruction time ∼2-2.5 h). A 3D cardiac cine acquisition with CS reconstruction has been proposed to image the left ventricle in a single breathhold (15). Ten healthy subjects and three patients were imaged at 1.9 × 1.9 × 2.5 mm 3 spatial and 42-48 ms temporal resolution in ∼19 s using a Cartesian spiral phyllotaxis sampling (24).…”

Section: Cardiac Cine Imagingmentioning

confidence: 99%

From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction

Bustin

Fuin

Botnar

et al. 2020

Front. Cardiovasc. Med.

107

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Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive assessment of cardiovascular disease. However, CMR suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, different contrasts, and/or whole-heart coverage. In addition, both cardiac and respiratory-induced motion of the heart during the acquisition need to be accounted for, further increasing the scan time. Several undersampling reconstruction techniques have been proposed during the last decades to speed up CMR acquisition. These techniques rely on acquiring less data than needed and estimating the non-acquired data exploiting some sort of prior information. Parallel imaging and compressed sensing undersampling reconstruction techniques have revolutionized the field, enabling 2-to 3-fold scan time accelerations to become standard in clinical practice. Recent scientific advances in CMR reconstruction hinge on the thriving field of artificial intelligence. Machine learning reconstruction approaches have been recently proposed to learn the non-linear optimization process employed in CMR reconstruction. Unlike analytical methods for which the reconstruction problem is explicitly defined into the optimization process, machine learning techniques make use of large data sets to learn the key reconstruction parameters and priors. In particular, deep learning techniques promise to use deep neural networks (DNN) to learn the reconstruction process from existing datasets in advance, providing a fast and efficient reconstruction that can be applied to all newly acquired data. However, before machine learning and DNN can realize their full potentials and enter widespread clinical routine for CMR image reconstruction, there are several technical hurdles that need to be addressed. In this article, we provide an overview of the recent developments in the area of artificial intelligence for CMR image reconstruction. The underlying assumptions of established techniques such as compressed sensing and low-rank reconstruction are briefly summarized, while a greater focus is given to recent advances in dictionary learning and deep learning based CMR reconstruction. In particular, approaches that exploit neural networks as implicit or explicit priors are discussed for 2D dynamic cardiac imaging and 3D whole-heart CMR imaging. Current limitations, challenges, and potential future directions of these techniques are also discussed.

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“…Nonetheless, there is a clear need for a 3-D cine technique capable of covering the whole heart volume in a single breath-hold. To that end, Wetzl et al [3] introduce a 3-D CINE imaging technique capable of covering the left ventricle with nearly isotropic resolution in a single breath-hold of approximately 19 s. The sequence employs a Cartesian sampling strategy, which covers k-space very efficiently using a phyllotaxis trajectory and compressed sensing reconstruction. In the paper, the authors demonstrate in volunteers as well as patients that functional parameters derived using the new sequence did not differ significantly from the standard of reference acquisitions.…”

Section: Myocardial Functionmentioning

confidence: 99%

Advances in cardiovascular MR imaging

Leiner

Strijkers

2018

Magn Reson Mater Phy

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Single-breath-hold 3-D CINE imaging of the left ventricle using Cartesian sampling

Abstract: We demonstrate single-breath-hold 3-D CINE imaging in volunteers and three example patient cases, which features fast reconstruction and allows reformatting to arbitrary orientations.

Cited by 37 publications

References 35 publications

Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography

Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography

From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction

Advances in cardiovascular MR imaging

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