Simulation of two‐dimensional tagged MRI

Crum, William R.; Berry, E.; Ridgway, John P.; Sivananthan, U. M.; Tan, Lip‐Bun; Smith, M.F.

doi:10.1002/jmri.1880070228

Cited by 20 publications

(12 citation statements)

References 32 publications

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“…In 1997, Crum et al developed an interactive computer program that simulates 2-D CMR tagging [57]. The developed software produces simulated tagged images that can be used for investigating the effect of imaging parameter selection and testing post-processing algorithms.…”

Section: Spammmentioning

confidence: 99%

Myocardial tagging by Cardiovascular Magnetic Resonance: evolution of techniques–pulse sequences, analysis algorithms, and applications

Ibrahim

2011

Journal of Cardiovascular Magnetic Resonance

237

211

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Cardiovascular magnetic resonance (CMR) tagging has been established as an essential technique for measuring regional myocardial function. It allows quantification of local intramyocardial motion measures, e.g. strain and strain rate. The invention of CMR tagging came in the late eighties, where the technique allowed for the first time for visualizing transmural myocardial movement without having to implant physical markers. This new idea opened the door for a series of developments and improvements that continue up to the present time. Different tagging techniques are currently available that are more extensive, improved, and sophisticated than they were twenty years ago. Each of these techniques has different versions for improved resolution, signal-to-noise ratio (SNR), scan time, anatomical coverage, three-dimensional capability, and image quality. The tagging techniques covered in this article can be broadly divided into two main categories: 1) Basic techniques, which include magnetization saturation, spatial modulation of magnetization (SPAMM), delay alternating with nutations for tailored excitation (DANTE), and complementary SPAMM (CSPAMM); and 2) Advanced techniques, which include harmonic phase (HARP), displacement encoding with stimulated echoes (DENSE), and strain encoding (SENC). Although most of these techniques were developed by separate groups and evolved from different backgrounds, they are in fact closely related to each other, and they can be interpreted from more than one perspective. Some of these techniques even followed parallel paths of developments, as illustrated in the article. As each technique has its own advantages, some efforts have been made to combine different techniques together for improved image quality or composite information acquisition. In this review, different developments in pulse sequences and related image processing techniques are described along with the necessities that led to their invention, which makes this article easy to read and the covered techniques easy to follow. Major studies that applied CMR tagging for studying myocardial mechanics are also summarized. Finally, the current article includes a plethora of ideas and techniques with over 300 references that motivate the reader to think about the future of CMR tagging.

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Section: Spammmentioning

confidence: 99%

Myocardial tagging by Cardiovascular Magnetic Resonance: evolution of techniques–pulse sequences, analysis algorithms, and applications

Ibrahim

2011

Journal of Cardiovascular Magnetic Resonance

237

211

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“…[1] A sub-bounding-cube includes one or more vertices of volume element [2] A sub-bounding-cube crosses one or more edge of volume element.…”

Section: Searching For the Enclosing Volume Element Of A Materials Pointmentioning

confidence: 99%

<title>Virtual MR tagging on three-dimensional deformable models</title>

Kim

Park

2001

SPIE Proceedings

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Magnetic Resonance (MR) tagging techniques have recently received much attention for the generation of suitable data sets for cardiac motion analysis. Several different techniques have been developed for the analysis of 3D cardiac wall motion from such data sets. Unfortunately, due to the lack of gold standard test data sets, most of the techniques are not fully evaluated. We have developed a virtual MR tagging system for evaluation of such 3D motion estimation techniques. It generates virtual tagging images, in particular SPAMM images, of the deformable model that undergoes a predetermined motion sequence in a virtual environment. The input to the system is a set of vertices that characterizes the shape of the volumetric model at each time phase. After the user interactively specifies the imaging and tagging parameters, the system automatically marks the intersections between the image and tagging planes within the model tissue to track its motion, computing the deformed tagging lines over the time sequence. The output is the set of tomographic images of the virtual object with SPAMM grid patterns reflecting the known 3D deformation field, and it can be used to evaluate an arbitrary cardiac motion estimation technique.

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“…Computer simulations [16], [17] are particularly useful to assess the effect of varying imaging parameters [18], [19] and to validate tag quantification methods: the deformation is exactly known, non-ideal equipment behavior is not present to confound data interpretation, deformations can be modeled that are not easily generated with real phantoms, and a large number of tests can be done without incurring the cost of MRI system usage. Previous studies have focused on the presentation of computer simulation methods.…”

Section: Introductionmentioning

confidence: 99%

“…Crum et al [17] developed a software to reproduce tagged images in the frequency domain, although they were dependent on the simulated acquisition sequence. In [16], the authors added noise to their simulation and explained how deformations could be included in the model, considering only deformations induced by radially varying contraction and reproducing the heart movements. Other studies assessed the effect of varying imaging parameters.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Tagged MRI for Quantification of Liver Stiffness Using Computer Simulated Data

et al. 2014

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The heartbeat has been proposed as an intrinsic source of motion that can be used in combination with tagged Magnetic Resonance Imaging (MRI) to measure displacements induced in the liver as an index of liver stiffness. Optimizing a tagged MRI acquisition protocol in terms of sensitivity to these displacements, which are in the order of pixel size, is necessary to develop the method as a quantification tool for staging fibrosis. We reproduced a study of cardiac-induced strain in the liver at 3T and simulated tagged MR images with different grid tag patterns to evaluate the performance of the Harmonic Phase (HARP) image analysis method and its dependence on the parameters of tag spacing and grid angle. The Partial Volume Effect (PVE), T1 relaxation, and different levels of noise were taken into account. Four displacement fields of increasing intensity were created and applied to the tagged MR images of the liver. These fields simulated the deformation at different liver stiffnesses. An Error Index (EI) was calculated to evaluate the estimation accuracy for various parameter values. In the absence of noise, the estimation accuracy of the displacement fields increased as tag spacings decreased. EIs for each of the four displacement fields were lower at 0° and the local minima of the EI were found to correspond to multiples of pixel size. The accuracy of the estimation decreased for increasing levels of added noise; as the level increased, the improved estimation caused by decreasing the tag spacing tended to zero. The optimal tag spacing turned out to be a compromise between the smallest tag period that is a multiple of the pixel size and is achievable in a real acquisition and the tag spacing that guarantees an accurate liver displacement measure in the presence of realistic levels of noise.

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Simulation of two‐dimensional tagged MRI

Cited by 20 publications

References 32 publications

Myocardial tagging by Cardiovascular Magnetic Resonance: evolution of techniques–pulse sequences, analysis algorithms, and applications

Myocardial tagging by Cardiovascular Magnetic Resonance: evolution of techniques–pulse sequences, analysis algorithms, and applications

<title>Virtual MR tagging on three-dimensional deformable models</title>

Optimization of Tagged MRI for Quantification of Liver Stiffness Using Computer Simulated Data

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