Background

Previous studies of mechanical strain anomalies in myocardial infarction (MI) have been largely limited to analysis of one-dimensional (1D) and two-dimensional (2D) strain parameters. Advances in cardiovascular magnetic resonance (CMR) methods now permit a complete three-dimensional (3D) interrogation of myocardial regional strain. The aim of this study was to investigate the incremental value of CMR-based 3D strain and to test the hypothesis that 3D strain is superior to 1D or 2D strain analysis in the assessment of viability using a porcine model of infarction.

Methods

Infarction was induced surgically in 20 farm pigs. Cine, late gadolinium enhancement, and CMR tagging images were acquired at 11 days before (baseline), and 11 days (early) and 1 month (late) after induction of infarct. Harmonic phase analysis was performed to measure circumferential, longitudinal, and radial strains in myocardial segments, which were defined based on the transmurality of delayed enhancement. Univariate, bivariate, and multivariate logistic regression models of strain parameters were created and analyzed to compare the overall diagnostic accuracy of 3D strain analysis with 1D and 2D analyses in identifying the infarct and its adjacent regions from healthy myocardium.

Results

3D strain differed significantly in infarct, adjacent, and remote segments (p < 0.05) at early and late post-MI. In univariate, bivariate, and multivariate analyses, circumferential, longitudinal, and radial strains were significant factors (p < 0.001) in differentiation of infarct and adjacent segments from baseline values. In identification of adjacent segments, receiver operating characteristic analysis using the 3D strain multivariate model demonstrated a significant improvement (p < 0.01) in overall diagnostic accuracy in comparison with 2D (circumferential and radial) and 1D (circumferential) models (3D: 96%, 2D: 81%, and 1D: 71%). A similar trend was observed in identification of infarct segments.

Conclusions

Cumulative 3D strain information accurately identifies infarcts and their neighboring regions from healthy myocardium. The 3D interrogation of myocardial contractility provides incremental diagnostic accuracy in delineating the dysfunctional and nonviable myocardium in comparison with 1D or 2D quantification of strain. The infarct neighboring regions are the major beneficiaries of the 3D assessment of regional strain.

Current MRI methods for myocardial motion and strain quantification have limited resolution because of Fourier space spectral peak interference. Methods have been proposed to remove this interference in order to improve resolution; however, these methods are clinically impractical due to the prolonged imaging times. In this paper, we propose total removal of unwanted harmonic peaks (TruHARP); a myocardial motion and strain quantification methodology that uses a novel single breath-hold MR image acquisition protocol. In post-processing, TruHARP separates the spectral peaks in the acquired images, enabling high-resolution motion and strain quantification. The impact of high resolution on calculated circumferential and radial strains is studied using realistic Monte Carlo simulations, and the improvement in strain maps is demonstrated in six human subjects.

Purpose

To develop and evaluate a practical method for the quantification of signal-to-noise ratio (SNR) on coronary magnetic resonance angiograms (MRA) acquired with parallel imaging.

Materials and Methods

To quantify the spatially varying noise due to parallel imaging reconstruction, a new method has been implemented incorporating image data acquisition followed by a fast noise scan during which radiofrequency pulses, cardiac triggering and navigator gating are disabled. The performance of this method was evaluated in a phantom study where SNR measurements were compared to those of a reference standard (multiple repetitions). Subsequently, SNR of myocardium and posterior skeletal muscle was determined on in vivo human coronary MRA.

Results

In a phantom, the SNR measured using the proposed method deviated less than 10.1% from the reference method for small geometry factors (<=2). In-vivo, the noise scan for a 10 minutes coronary MRA acquisition was acquired in 30s. Higher signal and lower SNR, due to spatially varying noise, were found in myocardium compared to posterior skeletal muscle.

Conclusion

SNR quantification based on a fast noise scan is a validated and easy-to-use method when applied to 3D coronary MRA obtained with parallel imaging as long as the geometry factor remains low.

Background

We sought to investigate the relationship between infarct and dyssynchrony post- myocardial infarct (MI), in a porcine model. Mechanical dyssynchrony post-MI is associated with left ventricular (LV) remodeling and increased mortality.

Methods

Cine, gadolinium-contrast, and tagged cardiovascular magnetic resonance (CMR) were performed pre-MI, 9 ± 2 days (early post-MI), and 33 ± 10 days (late post-MI) post-MI in 6 pigs to characterize cardiac morphology, location and extent of MI, and regional mechanics. LV mechanics were assessed by circumferential strain (eC). Electro-anatomic mapping (EAM) was performed within 24 hrs of CMR and prior to sacrifice.

Results

Mean infarct size was 21 ± 4% of LV volume with evidence of post-MI remodeling. Global eC significantly decreased post MI (-27 ± 1.6% vs. -18 ± 2.5% (early) and -17 ± 2.7% (late), p < 0.0001) with no significant change in peri-MI and MI segments between early and late time-points. Time to peak strain (TTP) was significantly longer in MI, compared to normal and peri-MI segments, both early (440 ± 40 ms vs. 329 ± 40 ms and 332 ± 36 ms, respectively; p = 0.0002) and late post-MI (442 ± 63 ms vs. 321 ± 40 ms and 355 ± 61 ms, respectively; p = 0.012). The standard deviation of TTP in 16 segments (SD16) significantly increased post-MI: 28 ± 7 ms to 50 ± 10 ms (early, p = 0.012) to 54 ± 19 ms (late, p = 0.004), with no change between early and late post-MI time-points (p = 0.56). TTP was not related to reduction of segmental contractility. EAM revealed late electrical activation and greatly diminished conduction velocity in the infarct (5.7 ± 2.4 cm/s), when compared to peri-infarct (18.7 ± 10.3 cm/s) and remote myocardium (39 ± 20.5 cm/s).

Conclusions

Mechanical dyssynchrony occurs early after MI and is the result of delayed electrical and mechanical activation in the infarct.

To understand the role of the tongue in speech production, it is desirable to directly image the motion and strain of the muscles within the tongue. Magnetic resonance tagging—which was originally developed for cardiac imaging—has previously been applied to image both two-dimensional and three-dimensional tongue motion during speech. However, to quantify three-dimensional motion and strain, multiple images yielding two-dimensional motion must be acquired at different orientations and then interpolated—a time-consuming task both in image acquisition and processing. Recently, a new MR imaging and image processing method called zHARP was developed to encode and track 3D motion from a single slice without increasing acquisition time. zHARP was originally developed and applied to cardiac imaging. The application of zHARP to the tongue is not straightforward because the tongue in repetitive speech does not move as consistently as the heart in its beating cycle. Therefore tongue images are more susceptible to motion artifacts. Moreover, these artifacts are greatly exaggerated as compared to conventional tagging because of the nature of zHARP acquisition. In this work, we re-implemented the zHARP imaging sequence and optimized it for the tongue motion analysis. We also optimized image acquisition by designing and developing a specialized MRI scanner triggering method and vocal repetition to better synchronize speech repetitions. Our method was validated using a moving phantom. Results of 3D motion tracking and strain analysis on the tongue experiments demonstrate the effectiveness of this method.

MRI techniques for tissue motion and strain quantifications have limited resolution because of interference from the conjugate echo or spectral peak in Fourier space. Methods have been proposed to remove this interference in order to improve resolution; however, these methods are clinically impractical due to long image acquisition time. In this paper, we propose TruHARP, an MRI motion and strain quantification methodology that involves a novel single breath-hold imaging protocol. In post-processing, TruHARP separates the spectral peaks in the acquired datasets, enabling high resolution motion and strain quantification. The impact of high resolution on circumferential and radial strain is studied using a realistic simulation and the improvement in strain maps is demonstrated in an in-vivo human study.

Fuzzy c-means (FCM) clustering has been extensively studied and widely applied in the tissue classification of biomedical images. Previous enhancements to FCM have accounted for intensity shading, membership smoothness, and variable cluster sizes. In this paper, we introduce a new parameter called “compactness” which captures additional information of the underlying clusters. We then propose a new classification algorithm, FCM with variable compactness (FCMVC), to classify three major tissues in brain MRIs by incorporating the compactness terms into a previously reported improvement to FCM. Experiments on both simulated phantoms and real magnetic resonance brain images show that the new method improves the repeatability of the tissue classification for the same subject with different acquisition protocols.