Assessment of cardiac mass from tagged magnetic resonance images

Makram, Abram W.; Khalifa, Ayman M.; El‐Rewaidy, Hossam; Fahmy, Ahmed S.; Ibrahim, El Sayed H.

doi:10.1007/s11604-015-0504-4

Cited by 5 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To that end, the MR-Tagging sequence is detagged following an homomorphic filtering procedure [14] to improve registration performance. Similar detagging procedures have been reported in [15] for global measures estimation. The proposed dettaging method is described below.…”

Section: Methodsmentioning

confidence: 63%

An Automated Tensorial Classification Procedure for Left Ventricular Hypertrophic Cardiomyopathy

Sanz-Estébanez

Royuela-del-Val

Merino-Caviedes

et al. 2016

Bioinformatics and Biomedical Engineering

View full text Add to dashboard Cite

Abstract. Cardiovascular diseases are the leading cause of death globally. Therefore, classification tools play a major role in prevention and treatment of these diseases. Statistical learning theory applied to magnetic resonance imaging has led to the diagnosis of a variety of cardiomyopathies states. We propose a two-stage classification scheme capable of distinguishing between heterogeneous groups of hypertrophic cardiomyopathies and healthy patients. A multimodal processing pipeline is employed to estimate robust tensorial descriptors of myocardial mechanical properties for both short-axis and long-axis magnetic resonance tagged images using the least absolute deviation method. A homomorphic filtering procedure is used to align the cine segmentations to the tagged sequence and provides 3D tensor information in meaningful areas. Results have shown that the proposed pipeline provides tensorial measurements on which classifiers for the study of hypertrophic cardiomyopathies can be built with acceptable performance even for reduced samples sets.

show abstract

Section: Methodsmentioning

confidence: 63%

An Automated Tensorial Classification Procedure for Left Ventricular Hypertrophic Cardiomyopathy

Sanz-Estébanez

Royuela-del-Val

Merino-Caviedes

et al. 2016

Bioinformatics and Biomedical Engineering

View full text Add to dashboard Cite

show abstract

“…A previous study from Makram et al investigated the extraction of EF and myocardial mass from tagged images using harmonic filtering of k ‐space. Compared with cine untagged SSFP, a strong correlation of LV mass ( r = 0.97) and moderate correlation with EF were observed ( r = 0.32).…”

Section: Discussionmentioning

confidence: 99%

“…Other studies have investigated GRE tagged MRI images to evaluate global cardiac mass and volume by employing additional k ‐space and image space processing . These results show good left ventricular (LV) mass correlation between SSFP cine and processed images, yet image quality is inferior to standard SSFP imaging in contrast to noise ratio and temporal resolution.…”

mentioning

confidence: 99%

Left ventricular function and regional strain with subtly‐tagged steady‐state free precession feature tracking

Schrauben

Cowan

Greiser

et al. 2017

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…Although this technique is shown to be promising for the assessment of regional deformation, motion quantification techniques are highly affected by the two main error sources, coarse resolution and low SNR, commonly observed in in vivo tagged CMR images. Decrease in image resolution due to the saturation bands generated in the tagged images introduces systematic error due to partial voluming at edges [8,9]. Moreover, change in SNR causes random error which is solved by various regularization techniques implemented in the motion tracking algorithms.…”

Section: Introductionmentioning

confidence: 99%

Validation of Finite Element Image Registration‐based Cardiac Strain Estimation from Magnetic Resonance Images

Berberog̃lu

Stoeck

Moireau

et al. 2019

Proc Appl Math and Mech

View full text Add to dashboard Cite

Accurate assessment of regional and global function of the heart is an important readout for the diagnosis and routine evaluation of cardiac patients. Indeed, recent clinical and experimental studies suggest that compared to global metrics, regional measures of function could allow for more accurate diagnosis and early intervention for many cardiac diseases. Although global strain measures derived from tagged magnetic resonance (MR) imaging have been shown to be reproducible for the majority of image registration techniques, the measurement of regional heterogeneity of strain is less robust. Moreover, radial strain is underestimated with the current techniques even globally. Finite element (FE)-based techniques offer a mechanistic approach for the regularization of the ill-posed registration problem. This paper presents the validation of a recently proposed FE-based image registration method with mechanical regularization named equilibrated warping. For this purpose, synthetic 3D-tagged MR images are generated from a reference biomechanical model of the left ventricle (LV). The performance of the registration algorithm is consequently tested on the images with different signal-to-noise ratios (SNRs), revealing the robustness of the method.

show abstract

Assessment of cardiac mass from tagged magnetic resonance images

Abstract: The promising results in this study show the potential of the developed method for evaluating both regional and global heart function from a single set of tMRI images, with associated reduction in scan time and patient discomfort.

Cited by 5 publications

References 6 publications

An Automated Tensorial Classification Procedure for Left Ventricular Hypertrophic Cardiomyopathy

An Automated Tensorial Classification Procedure for Left Ventricular Hypertrophic Cardiomyopathy

Left ventricular function and regional strain with subtly‐tagged steady‐state free precession feature tracking

Validation of Finite Element Image Registration‐based Cardiac Strain Estimation from Magnetic Resonance Images

Contact Info

Product

Resources

About