Tag and contour detection in tagged MR images of the left ventricle

Computational Analysis of Cardiac Contractile Function

PURPOSE OF REVIEW

Heart failure results in the high incidence and mortality all over the world. Mechanical properties of myocardium are critical determinants of cardiac function, with regional variations in myocardial contractility demonstrated within infarcted ventricles. Quantitative assessment of cardiac contractile function is therefore critical to identify myocardial infarction for the early diagnosis and therapeutic intervention.

RECENT FINDINGS

Current advancement of cardiac functional assessments is in pace with the development of imaging techniques. The methods tailored to advanced imaging have been widely used in cardiac magnetic resonance, echocardiography, and optical microscopy. In this review, we introduce fundamental concepts and applications of representative methods for each imaging modality used in both fundamental research and clinical investigations. All these methods have been designed or developed to quantify time-dependent 2-dimensional (2D) or 3D cardiac mechanics, holding great potential to unravel global or regional myocardial deformation and contractile function from end-systole to end-diastole. Computational methods to assess cardiac contractile function provide a quantitative insight into the analysis of myocardial mechanics during cardiac development, injury, and remodeling.

HARP-I: A Harmonic Phase Interpolation Method for the Estimation of Motion From Tagged MR Images

We proposed a novel method called HARP-I, which enhances the estimation of motion from tagged Magnetic Resonance Imaging (MRI). The harmonic phase of the images is unwrapped and treated as noisy measurements of reference coordinates on a deformed domain, obtaining motion with high accuracy using Radial Basis Functions interpolations. Results were compared against Shortest Path HARP Refinement (SP-HR) and Sine-wave Modeling (SinMod), two harmonic image-based techniques for motion estimation from tagged images. HARP-I showed a favorable similarity with both methods under noise-free conditions, whereas a more robust performance was found in the presence of noise. Cardiac strain was better estimated using HARP-I at almost any motion level, giving strain maps with less artifacts. Additionally, HARP-I showed better temporal consistency as a new method was developed to fix phase jumps between frames. In conclusion, HARP-I showed to be a robust method for the estimation of motion and strain under ideal and non-ideal conditions.

Fully Automated Myocardial Strain Estimation from Cardiovascular MRI-tagged Images Using a Deep Learning Framework in the UK Biobank

PURPOSE

To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac MRI-tagged images.

MATERIALS AND METHODS

In this retrospective cross-sectional study, 4508 cases from the U.K. Biobank were split randomly into 3244 training cases, 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of (a) a convolutional neural network (CNN) for localization and (b) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. Radial and circumferential strain were then calculated from the motion of the landmarks and averaged on a slice basis.

RESULTS

Within the test set, myocardial end-systolic circumferential Green strain errors were -0.001 ± 0.025, -0.001 ± 0.021, and 0.004 ± 0.035 in the basal, mid-, and apical slices, respectively (mean ± standard deviation of differences between predicted and manual strain). The framework reproduced significant reductions in circumferential strain in participants with diabetes, hypertensive participants, and participants with a previous heart attack. Typical processing time was approximately 260 frames (approximately 13 slices) per second on a GPU with 12 GB RAM compared with 6-8 minutes per slice for the manual analysis.

CONCLUSION

The fully automated combined RNN and CNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due to diabetes, hypertension, and previous heart attack.Published under a CC BY 4.0 license. Supplemental material is available for this article.

Myocardial deformation assessment using cardiovascular magnetic resonance-feature tracking technique

BACKGROUND

Cardiovascular magnetic resonance (CMR) imaging is an important modality that allows the assessment of regional myocardial function by measuring myocardial deformation parameters, such as strain and strain rate throughout the cardiac cycle. Feature tracking is a promising quantitative post-processing technique that is increasingly used. It is commonly applied to cine images, in particular steady-state free precession, acquired during routine CMR examinations.

OBJECTIVE

To review the studies that have used feature tracking techniques in healthy subjects or patients with cardiovascular diseases. The article emphasizes the advantages and limitations of feature tracking when applied to regional deformation parameters. The challenges of applying the techniques in clinics and potential solutions are also reviewed.

RESULTS

Research studies in healthy volunteers and/or patients either applied CMR-feature tracking alone to assess myocardial motion or compared it with either established CMR-tagging techniques or to speckle tracking echocardiography. These studies assessed the feasibility and reliability of calculating or determining global and regional myocardial deformation strain parameters. Regional deformation parameters are reviewed and compared. Better reproducibility for global deformation was observed compared with segmental parameters. Overall, studies demonstrated that circumferential was the most reproducible deformation parameter, usually followed by longitudinal strain; in contrast, radial strain showed high variability.

CONCLUSION

Although feature tracking is a promising tool, there are still discrepancies in the results obtained using different software packages. This highlights a clear need for standardization of MRI acquisition parameters and feature tracking analysis methodologies. Validation, including physical and numerical phantoms, is still required to facilitate the use of feature tracking in routine clinical practice.

Phase Vector Incompressible Registration Algorithm for Motion Estimation From Tagged Magnetic Resonance Images

Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissue. It is challenging to obtain 3-D motion estimates due to a tradeoff between image slice density and acquisition time. Typically, interpolation methods are used either to combine 2-D motion extracted from sparse slice acquisitions into 3-D motion or to construct a dense volume from sparse acquisitions before image registration methods are applied. This paper proposes a new phase-based 3-D motion estimation technique that first computes harmonic phase volumes from interpolated tagged slices and then matches them using an image registration framework. The approach uses several concepts from diffeomorphic image registration with a key novelty that defines a symmetric similarity metric on harmonic phase volumes from multiple orientations. The material property of harmonic phase solves the aperture problem of optical flow and intensity-based methods and is robust to tag fading. A harmonic magnitude volume is used in enforcing incompressibility in the tissue regions. The estimated motion fields are dense, incompressible, diffeomorphic, and inverse-consistent at a 3-D voxel level. The method was evaluated using simulated phantoms, human brain data in mild head accelerations, human tongue data during speech, and an open cardiac data set. The method shows comparable accuracy to three existing methods while demonstrating low computation time and robustness to tag fading and noise.

Mathematical Development and Computational Analysis of Harmonic Phase-Magnetic Resonance Imaging (HARP-MRI) Based on Bloch Nuclear Magnetic Resonance (NMR) Diffusion Model for Myocardial Motion

Harmonic Phase-Magnetic Resonance Imaging (HARP-MRI) is a tagged image analysis method that can measure myocardial motion and strain in near real-time and is considered a potential candidate to make magnetic resonance tagging clinically viable. However, analytical expressions of radially tagged transverse magnetization in polar coordinates (which is required to appropriately describe the shape of the heart) have not been explored because the physics required to directly connect myocardial deformation of tagged Nuclear Magnetic Resonance (NMR) transverse magnetization in polar geometry and the appropriate harmonic phase parameters are not yet available. The analytical solution of Bloch NMR diffusion equation in spherical geometry with appropriate spherical wave tagging function is important for proper analysis and monitoring of heart systolic and diastolic deformation with relevant boundary conditions. In this study, we applied Harmonic Phase MRI method to compute the difference between tagged and untagged NMR transverse magnetization based on the Bloch NMR diffusion equation and obtained radial wave tagging function for analysis of myocardial motion. The analytical solution of the Bloch NMR equations and the computational simulation of myocardial motion as developed in this study are intended to significantly improve healthcare for accurate diagnosis, prognosis and treatment of cardiovascular related deceases at the lowest cost because MRI scan is still one of the most expensive anywhere. The analysis is fundamental and significant because all Magnetic Resonance Imaging techniques are based on the Bloch NMR flow equations.

A graph theoretic approach for computing 3D+time biventricular cardiac strain from tagged MRI data

Tagged magnetic resonance imaging (tMRI) is a well-established method for evaluating regional mechanical function of the heart. Many techniques have been developed to compute 2D or 3D cardiac deformation and strain from tMRI images. In this paper, we present a new method for measuring 3D plus time biventricular myocardial strain from tMRI data. The method is composed of two parts. First, we use a Gabor filter bank to extract tag points along tag lines. Second, each tag point is classified to one of a set of indexed reference tag lines using a point classification with graph cuts (PCGC) algorithm and a motion compensation technique. 3D biventricular deformation and strain is computed at each image time frame from the classified tag points using a previously published finite difference method. The strain computation is fully automatic after myocardial contours are defined near end-diastole and end-systole. An in-vivo dataset composed of 30 human imaging studies with a range of pathologies was used for validation. Strains computed with the PCGC method with no manual corrections were compared to strains computed from both manually placed tag points and a manually-corrected unwrapped phase method. A typical cardiac imaging study with 10 short-axis slices and 6 long-axis slices required 30 min for contouring followed by 44 min of automated processing. The results demonstrate that the proposed method can reconstruct accurate 3D plus time cardiac strain maps with minimal user intervention.

Artifacts reduction in strain maps of tagged magnetic resonance imaging using harmonic phase

Tagged Magnetic Resonance Imaging (MRI) is a noninvasive technique for examining myocardial function and deformation. Tagged MRI can also be used in quasi-static MR elastography to acquire strain maps of other biological soft tissues. Harmonic phase (HARP) provides automatic and rapid analysis of tagged MR images for the quantification and visualization of myocardial strain. We propose a new artifact reduction method in strain maps. Image intensity of the DC component is estimated and subtracted from spatial modulation of magnetization (SPAMM) tagged MR images. DC peak interference in harmonic phase extraction is greatly reduced after DC component subtraction. The proposed method is validated using both simulated and MR acquired tagged images. Strain maps are obtained with better accuracy and smoothness after DC component subtraction.

The role of shape complexity in the detection of closed contours

The detection of contours in noise has been extensively studied, but the detection of closed contours, such as the boundaries of whole objects, has received relatively little attention. Closed contours pose substantial challenges not present in the simple (open) case, because they form the outlines of whole shapes and thus take on a range of potentially important configural properties. In this paper we consider the detection of closed contours in noise as a probabilistic decision problem. Previous work on open contours suggests that contour complexity, quantified as the negative log probability (Description Length, DL) of the contour under a suitably chosen statistical model, impairs contour detectability; more complex (statistically surprising) contours are harder to detect. In this study we extended this result to closed contours, developing a suitable probabilistic model of whole shapes that gives rise to several distinct though interrelated measures of shape complexity. We asked subjects to detect either natural shapes (Exp. 1) or experimentally manipulated shapes (Exp. 2) embedded in noise fields. We found systematic effects of global shape complexity on detection performance, demonstrating how aspects of global shape and form influence the basic process of object detection.

Hybrid intensity- and phase-based optical flow tracking of tagged MRI

Accurate tracking of the myocardium tissues in tagged Magnetic Resonance Images (MRI) is essential for evaluating the cardiac function. Current tracking methods utilize either the image intensity or the image phase as landmarks that can be tracked. In either case, the performance is vulnerable to the image quality and the fading of the tag lines. In this work, we propose a hybrid optical flow tracking method that combines both the intensity and the phase features of the image. The method is validated using numerical cardiac phantom as well as real MRI data experiments. Both experiments showed that the proposed method outperforms current intensity-based optical flow tracking and the phase-based HARP method with maximum error of 1 pixel at extreme conditions of tag fading.

Estimating dense cardiac 3D motion using sparse 2D tagged MRI cross-sections

In this work, we describe a new method, an extension of the Large Deformation Diffeomorphic Metric Mapping to estimate three-dimensional deformation of tagged Magnetic Resonance Imaging Data. Our approach relies on performing non-rigid registration of tag planes that were constructed from set of initial reference short axis tag grids to a set of deformed tag curves. We validated our algorithm using in-vivo tagged images of normal mice. The mapping allows us to compute root mean square distance error between simulated tag curves in a set of long axis image planes and the acquired tag curves in the same plane. Average RMS error was 0.31 ± 0.36(SD) mm, which is approximately 2.5 voxels, indicating good matching accuracy.

Tag removal in cardiac tagged MRI images using coupled dictionary learning

Tagged Magnetic Resonance Imaging (tMRI) is considered to be the gold standard for quantitative assessment of the cardiac local functions. However, the tagging patterns and low myocardium-to-blood-pool contrast of tagged images bring great challenges to cardiac image processing and analysis tasks such as myocardium segmentation and tracking. Hence, there has been growing interest in techniques for removing tagging lines. In this work, a method for removing tagging patterns in tagged MR images using a coupled dictionary learning (CDL) model is proposed. In this model, identical sparse representations are assumed for image patches in the tagged MRI and corresponding cine MRI image spaces. First, we learn a dictionary for the tagged MRI image space. Then, we compute a dictionary for the cine MRI image space so that corresponding tagged and cine patches have the same sparse codes in terms of their respective dictionaries. Finally, in order to produce the de-tagged (cine version) of a test tagged image, the sparse codes of the tagged patches and the trained cine dictionary are used together to construct the de-tagged patches. We have tested this tag removal method on a dataset of tagged cardiac MR images. Our experimental results compared favorably with a recently proposed tag removal method that removes tags in the frequency domain using an optimal band-stop filter of harmonic peaks.

3D+time left ventricular strain by unwrapping harmonic phase with graph cuts

In previous work, a three-dimensional left ventricular strain throughout the cardiac cycle was reconstructed using a prolate spheroidal B-spline (PSB) method with displacement measurements obtained from unwrapped tagged MRI (tMRI) harmonic phase images. Manually placed branch cuts were required for each harmonic phase image to resolve phase inconsistencies and to guide the phase unwrapping (mSUP), which is both labor intensive and time consuming and therefore not proper for clinic application. In this paper, we present an automated graph cuts based phase unwrapping method for myocardium displacement measurement (caSUP) which can be used to compute 3D+time cardiac strain. A set of 8 human studies were used to optimize parameters of the energy function and another set of 32 human studies were used to validate the proposed method by comparing resulted strains with those from mSUP and a feature-based (FB) method using the same PSB strain reconstruction. The automated caSUP strains were close to the manual strains and only required 6 minutes after myocardium segmentation versus - 2 hours for the manual method.

Quantification of regional myocardial wall motion by cardiovascular magnetic resonance

Cardiovascular magnetic resonance (CMR) is a versatile tool that also allows comprehensive and accurate measurement of both global and regional myocardial contraction. Quantification of regional wall motion parameters, such as strain, strain rate, twist and torsion, has been shown to be more sensitive to early-stage functional alterations. Since the invention of CMR tagging by magnetization saturation in 1988, several CMR techniques have been developed to enable the measurement of regional myocardial wall motion, including myocardial tissue tagging, phase contrast mapping, displacement encoding with stimulated echoes (DENSE), and strain encoded (SENC) imaging. These techniques have been developed with their own advantages and limitations. In this review, two widely used and closely related CMR techniques, i.e., tissue tagging and DENSE, will be discussed from the perspective of pulse sequence development and image-processing techniques. The clinical and preclinical applications of tissue tagging and DENSE in assessing wall motion mechanics in both normal and diseased hearts, including coronary artery diseases, hypertrophic cardiomyopathy, aortic stenosis, and Duchenne muscular dystrophies, will be discussed.

Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI

Dynamic MRI has been widely used to track the motion of the tongue and measure its internal deformation during speech and swallowing. Accurate segmentation of the tongue is a prerequisite step to define the target boundary and constrain the tracking to tissue points within the tongue. Segmentation of 2D slices or 3D volumes is challenging because of the large number of slices and time frames involved in the segmentation, as well as the incorporation of numerous local deformations that occur throughout the tongue during motion. In this paper, we propose a semi-automatic approach to segment 3D dynamic MRI of the tongue. The algorithm steps include seeding a few slices at one time frame, propagating seeds to the same slices at different time frames using deformable registration, and random walker segmentation based on these seed positions. This method was validated on the tongue of five normal subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semi-automatic segmentations of a total of 130 volumes showed an average dice similarity coefficient (DSC) score of 0.92 with less segmented volume variability between time frames than in manual segmentations.

Distribution of normal human left ventricular myofiber stress at end diastole and end systole: a target for in silico design of heart failure treatments

Ventricular wall stress is believed to be responsible for many physical mechanisms taking place in the human heart, including ventricular remodeling, which is frequently associated with heart failure. Therefore, normalization of ventricular wall stress is the cornerstone of many existing and new treatments for heart failure. In this paper, we sought to construct reference maps of normal ventricular wall stress in humans that could be used as a target for in silico optimization studies of existing and potential new treatments for heart failure. To do so, we constructed personalized computational models of the left ventricles of five normal human subjects using magnetic resonance images and the finite-element method. These models were calibrated using left ventricular volume data extracted from magnetic resonance imaging (MRI) and validated through comparison with strain measurements from tagged MRI (950 ± 170 strain comparisons/subject). The calibrated passive material parameter values were C0 = 0.115 ± 0.008 kPa and B0 = 14.4 ± 3.18; the active material parameter value was Tmax = 143 ± 11.1 kPa. These values could serve as a reference for future construction of normal human left ventricular computational models. The differences between the predicted and the measured circumferential and longitudinal strains in each subject were 3.4 ± 6.3 and 0.5 ± 5.9%, respectively. The predicted end-diastolic and end-systolic myofiber stress fields for the five subjects were 2.21 ± 0.58 and 16.54 ± 4.73 kPa, respectively. Thus these stresses could serve as targets for in silico design of heart failure treatments.

Speech MRI: morphology and function

Magnetic Resonance Imaging (MRI) plays an increasing role in the study of speech. This article reviews the MRI literature of anatomical imaging, imaging for acoustic modelling and dynamic imaging. It describes existing imaging techniques attempting to meet the challenges of imaging the upper airway during speech and examines the remaining hurdles and future research directions.

Tissue-point motion tracking in the tongue from cine MRI and tagged MRI

PURPOSE Accurate tissue motion tracking within the tongue can help professionals diagnose and treat vocal tract-related disorders, evaluate speech quality before and after surgery, and conduct various scientific studies. The authors compared tissue tracking results from 4 widely used deformable registration (DR) methods applied to cine magnetic resonance imaging (MRI) with harmonic phase (HARP)-based tracking applied to tagged MRI. METHOD Ten subjects repeated the phrase "a geese" multiple times while sagittal images of the head were collected at 26 Hz, first in a tagged MRI data set and then in a cine MRI data set. HARP tracked the motion of 8 specified tissue points in the tagged data set. Four DR methods including diffeomorphic demons and free-form deformations based on cubic B-spline with 3 different similarity measures were used to track the same 8 points in the cine MRI data set. Individual points were tracked and length changes of several muscles were calculated using the DR- and HARP-based tracking methods. RESULTS The results showed that the DR tracking errors were nonsystematic and varied in direction, amount, and timing across speakers and within speakers. Comparison of HARP and DR tracking with manual tracking showed better tracking results for HARP except at the tongue surface, where mistracking caused greater errors in HARP than DR. CONCLUSIONS Tissue point tracking using DR tracking methods contains nonsystematic tracking errors within and across subjects, making it less successful than tagged MRI tracking within the tongue. However, HARP sometimes mistracks points at the tongue surface of tagged MRI because of its limited bandpass filter and tag pattern fading, so that DR has better success measuring surface tissue points on cine MRI than HARP does. Therefore, a hybrid method is being explored.

Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance

BACKGROUND

The most time consuming and limiting step in three dimensional (3D) cine displacement encoding with stimulated echoes (DENSE) MR image analysis is the demarcation of the left ventricle (LV) from its surrounding anatomical structures. The aim of this study is to implement a semi-automated segmentation algorithm for 3D cine DENSE CMR using a guide point model approach.

METHODS

A 3D mathematical model is fitted to guide points which were interactively placed along the LV borders at a single time frame. An algorithm is presented to robustly propagate LV epicardial and endocardial surfaces of the model using the displacement information encoded in the phase images of DENSE data. The accuracy, precision and efficiency of the algorithm are tested.

RESULTS

The model-defined contours show good accuracy when compared to the corresponding manually defined contours as similarity coefficients Dice and Jaccard consist of values above 0.7, while false positive and false negative measures show low percentage values. This is based on a measure of segmentation error on intra- and inter-observer spatial overlap variability. The segmentation algorithm offers a 10-fold reduction in the time required to identify LV epicardial and endocardial borders for a single 3D DENSE data set.

CONCLUSION

A semi-automated segmentation method has been developed for 3D cine DENSE CMR. The algorithm allows for contouring of the first cardiac frame where blood-myocardium contrast is almost nonexistent and reduces the time required to segment a 3D DENSE data set significantly.

Automated motion estimation for 2-D cine DENSE MRI

Cine displacement encoding with stimulated echoes (DENSE) is a magnetic resonance (MR) method that directly encodes tissue displacement into MR phase images. This technique has successfully interrogated many forms of tissue motion, but is most commonly used to evaluate cardiac mechanics. Currently, motion analysis from cine DENSE images requires manually delineated anatomical structures. An automated analysis would improve measurement throughput, simplify data interpretation, and potentially access important physiological information during the MR exam. In this paper, we present the first fully automated solution for the estimation of tissue motion and strain from 2-D cine DENSE data. Results using both simulated and human cardiac cine DENSE data indicate good agreement between the automated algorithm and the standard semi-manual analysis method.

MR assessment of regional myocardial mechanics

Regional myocardial function can be measured by several MR techniques including tissue tagging, phase velocity mapping, and more recently, displacement encoding with stimulated echoes (DENSE) and strain encoding (SENC). Each of these techniques was developed separately and has undergone significant change since its original implementation. As a result, in the current literature, the common features and the differences between the techniques and what they measure are often unclear and confusing. This review article delivers an extensively referenced introductory text which clarifies the current methodology from the starting point of the Bloch equations. By doing this in a consistent way for each method, the similarities and differences between them are highlighted. In addition, their capabilities and limitations are discussed, together with their relative advantages and disadvantages. While the focus is on sequence design and development, the principal parameters measured by each technique are also summarized, together with brief results, with the reader being directed to the extensive literature on data processing and clinical applications for more detail.

Accurate two-dimensional cardiac strain calculation using adaptive windowed Fourier transform and Gabor wavelet transform

PURPOSE

Cardiac strain calculated from tagged magnetic resonance (MR) images provides clinicians information about abnormalities of heart-wall motion in patients. It is important to develop an accurate method to determine the cardiac strain efficiently. An adaptive windowed harmonic phase (AWHARP) method is proposed for cardiac strain calculation.

MATERIALS AND METHODS

AWHARP is based on adaptive windowed Fourier transform (AWFT) and 2D Gabor wavelet transform (2D-GWT). The AWFT provides a spatially varying representation of the signal spectra, which allows the harmonic phase (HARP) image to be extracted with high accuracy. Instantaneous spatial frequencies are calculated using 2D-GWT, and the widths of the adaptive windows are then determined according to the instantaneous spatial frequencies for multi-resolution analysis of phase extraction. The proposed method was studied using simulated images and patients' MR images. Both single tagged images (SPAMM) and subtracted tagged images (CSPAMM) were generated using our simulation method, and their results calculated using AWHARP and HARP methods were compared. Normal and pathological tagged MR images were also processed to evaluate the performance of our method.

RESULTS

Our experimental results show that the accuracies of phase and strain images calculated using the AWHARP method are higher than that calculated using the HARP method especially for large tag line deformation. The improvement in accuracies can be up to 3.2 strain (E1) and 17.3 calculation from MR images reveals that the cardiac strain in the end-systolic state is significantly reduced for patients with hypertrophic cardiomyopathy (HCM) compared to that of healthy subjects.

CONCLUSION

The proposed AWHARP is an accurate and efficient method for cardiac strain estimation from MR images. This new algorithm can help clinicians to detect left ventricle dysfunctions and myocardial diseases with accurate cardiac strain analysis.

Cardiac motion and deformation recovery from MRI: a review

Magnetic resonance imaging (MRI) is a highly advanced and sophisticated imaging modality for cardiac motion tracking and analysis, capable of providing 3D analysis of global and regional cardiac function with great accuracy and reproducibility. In the past few years, numerous efforts have been devoted to cardiac motion recovery and deformation analysis from MR image sequences. Many approaches have been proposed for tracking cardiac motion and for computing deformation parameters and mechanical properties of the heart from a variety of cardiac MR imaging techniques. In this paper, an updated and critical review of cardiac motion tracking methods including major references and those proposed in the past ten years is provided. The MR imaging and analysis techniques surveyed are based on cine MRI, tagged MRI, phase contrast MRI, DENSE, and SENC. This paper can serve as a tutorial for new researchers entering the field.

Incompressible deformation estimation algorithm (IDEA) from tagged MR images

Measuring the 3D motion of muscular tissues, e.g., the heart or the tongue, using magnetic resonance (MR) tagging is typically carried out by interpolating the 2D motion information measured on orthogonal stacks of images. The incompressibility of muscle tissue is an important constraint on the reconstructed motion field and can significantly help to counter the sparsity and incompleteness of the available motion information. Previous methods utilizing this fact produced incompressible motions with limited accuracy. In this paper, we present an incompressible deformation estimation algorithm (IDEA) that reconstructs a dense representation of the 3D displacement field from tagged MR images and the estimated motion field is incompressible to high precision. At each imaged time frame, the tagged images are first processed to determine components of the displacement vector at each pixel relative to the reference time. IDEA then applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. Through this process, IDEA yields a dense estimate of a 3D displacement field that matches our observations and also corresponds to an incompressible motion. The method was validated with both numerical simulation and in vivo human experiments on the heart and the tongue.

Motion tracking and strain map computation for quasi-static magnetic resonance elastography

This paper presents a new imaging method for quasi-static magnetic resonance elastography (MRE). Tagged magnetic resonance (MR) imaging of human lower leg was acquired with probe indentation using a MR-compatible actuation system. Indentation force was recorded for soft tissue elasticity reconstruction. Motion tracking and strain map of human lower leg are calculated using a harmonic phase (HARP)-based method. Simulated tagged MR images were constructed and analyzed to validate the HARP-based method. Our results show that the proposed imaging method can be used to generate accurate motion distribution and strain maps of the targeted soft tissue.

Reversible jump MCMC methods for fully automatic motion analysis in tagged MRI

Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method for studying regional heart dynamics. It offers great potential for quantitative analysis of a variety of kine(ma)tic parameters, but its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we evaluate the performance of four frequently used concepts found in the literature (optical flow, harmonic phase (HARP) magnetic resonance imaging, active contour fitting, and non-rigid image registration) for cardiac motion analysis in 2D tMRI image sequences, using both synthetic image data (with ground truth) and real data from preclinical (small animal) and clinical (human) studies. In addition we propose a new probabilistic method for tag tracking that serves as a complementary step to existing methods. The new method is based on a Bayesian estimation framework, implemented by means of reversible jump Markov chain Monte Carlo (MCMC) methods, and combines information about the heart dynamics, the imaging process, and tag appearance. The experimental results demonstrate that the new method improves the performance of even the best of the four previous methods. Yielding higher consistency, accuracy, and intrinsic tag reliability assessment, the proposed method allows for improved analysis of cardiac motion.

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

PURPOSE

We propose a novel approach for PET respiratory motion correction using tagged-MRI and simultaneous PET-MRI acquisitions.

METHODS

We use a tagged-MRI acquisition followed by motion tracking in the phase domain to estimate the nonrigid deformation of biological tissues during breathing. In order to accurately estimate motion even in the presence of noise and susceptibility artifacts, we regularize the traditional HARP tracking strategy using a quadratic roughness penalty on neighboring displacement vectors (R-HARP). We then incorporate the motion fields estimated with R-HARP in the system matrix of an MLEM PET reconstruction algorithm formulated both for sinogram and list-mode data representations. This approach allows reconstruction of all detected coincidences in a single image while modeling the effect of motion both in the emission and the attenuation maps. At present, tagged-MRI does not allow estimation of motion in the lungs and our approach is therefore limited to motion correction in soft tissues. Since it is difficult to assess the accuracy of motion correction approaches in vivo, we evaluated the proposed approach in numerical simulations of simultaneous PET-MRI acquisitions using the NCAT phantom. We also assessed its practical feasibility in PET-MRI acquisitions of a small deformable phantom that mimics the complex deformation pattern of a lung that we imaged on a combined PET-MRI brain scanner.

RESULTS

Simulations showed that the R-HARP tracking strategy accurately estimated realistic respiratory motion fields for different levels of noise in the tagged-MRI simulation. In simulations of tumors exhibiting increased uptake, contrast estimation was 20% more accurate with motion correction than without. Signal-to-noise ratio (SNR) was more than 100% greater when performing motion-corrected reconstruction which included all counts, compared to when reconstructing only coincidences detected in the first of eight gated frames. These results were confirmed in our proof-of-principle PET-MRI acquisitions, indicating that our motion correction strategy is accurate, practically feasible, and is therefore ready to be tested in vivo.

CONCLUSIONS

This work shows that PET motion correction using motion fields measured with tagged-MRI in simultaneous PET-MRI acquisitions can be made practical for clinical application and that doing so has the potential to remove motion blur in whole-body PET studies of the torso.

Myocardial tagging by cardiovascular magnetic resonance: evolution of techniques--pulse sequences, analysis algorithms, and applications

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.

Cardiovascular magnetic resonance imaging in experimental models

Cardiovascular magnetic resonance (CMR) imaging is the modality of choice for clinical studies of the heart and vasculature, offering detailed images of both structure and function with high temporal resolution.

Small animals are increasingly used for genetic and translational research, in conjunction with models of common pathologies such as myocardial infarction. In all cases, effective methods for characterising a wide range of functional and anatomical parameters are crucial for robust studies.

CMR is the gold-standard for the non-invasive examination of these models, although physiological differences, such as rapid heart rate, make this a greater challenge than conventional clinical imaging. However, with the help of specialised magnetic resonance (MR) systems, novel gating strategies and optimised pulse sequences, high-quality images can be obtained in these animals despite their small size.

In this review, we provide an overview of the principal CMR techniques for small animals for example cine, angiography and perfusion imaging, which can provide measures such as ejection fraction, vessel anatomy and local blood flow, respectively. In combination with MR contrast agents, regional dysfunction in the heart can also be identified and assessed. We also discuss optimal methods for analysing CMR data, particularly the use of semi-automated tools for parameter measurement to reduce analysis time. Finally, we describe current and emerging methods for imaging the developing heart, aiding characterisation of congenital cardiovascular defects.

Advanced small animal CMR now offers an unparalleled range of cardiovascular assessments. Employing these methods should allow new insights into the structural, functional and molecular basis of the cardiovascular system.

3D left ventricular strain from unwrapped harmonic phase measurements

PURPOSE

To validate a method for measuring 3D left ventricular (LV) strain from phase-unwrapped harmonic phase (HARP) images derived from tagged cardiac magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A set of 40 human subjects were imaged with tagged MRI. In each study the HARP phase was computed and unwrapped in each short-axis and long-axis image. Inconsistencies in unwrapped phase were resolved using branch cuts manually placed with a graphical user interface. 3D strain maps were computed for all imaged timeframes in each study. The strain from unwrapped phase (SUP) and displacements were compared to those estimated by a feature-based (FB) technique and a HARP technique.

RESULTS

3D strain was computed in each timeframe through systole and mid-diastole in approximately 30 minutes per study. The standard deviation of the difference between strains measured by the FB and the SUP methods was less than 5% of the average of the strains from the two methods. The correlation between peak circumferential strain measured using the SUP and HARP techniques was over 83%.

CONCLUSION

The SUP technique can reconstruct full 3D strain maps from tagged MR images through the cardiac cycle in a reasonable amount of time and user interaction compared to other 3D analysis methods.

Oblique 3D MRI tags for the estimation of true 3D cardiac motion parameters

Aim of this study is to demonstrate the advantages of oblique 3D tags in cardiac magnetic resonance imaging (MRI) and the potential to accurately describe the complex motion of the myocardial wall. 3D cardiac Cine data were densely tagged with 3D oblique tags. The latter were tracked using Gabor analysis and active geometries. From the tag intersections, common 2D parameters such as long axis shortening, radial shortening and rotation were evaluated on a global as well as detailed local level. Finally, the same data were used to estimate left ventricular volume change and myocardial stress/strain. We have successfully tracked dense 3D tags and evaluated common parameters on a detailed local level. In addition, inherently 3D parameters could be estimated. Global motion data are in accordance with previously published data. Oblique tags allow for unambiguous localization of the tag plane in all MRI slices and in any time frame. In contrast to HARP, our tag tracking methodology allows for tracking of the tags even when they are dense. Motion parameters can be extracted in greater detail. Moreover, the intersections of dense oblique 3D tags provide a natural basis for a finite element model of the heart. Straight forward access to the 3D characteristics of the cardiac motion is provided.

Motion estimation of tagged cardiac magnetic resonance images using variational techniques

This work presents a new method for motion estimation of tagged cardiac magnetic resonance sequences based on variational techniques. The variational method has been improved by adding a new term in the optical flow equation that incorporates tracking points with high stability of phase. Results were obtained through simulated and real data, and were validated by manual tracking and with respect to a reference state-of-the-art method: harmonic phase imaging (HARP). The error, measured in pixels per frame, obtained with the proposed variational method is one order of magnitude smaller than the one achieved by the reference method, and it requires a lower computational cost.

Myocardial tissue tagging with cardiovascular magnetic resonance

Cardiovascular magnetic resonance (CMR) is currently the gold standard for assessing both global and regional myocardial function. New tools for quantifying regional function have been recently developed to characterize early myocardial dysfunction in order to improve the identification and management of individuals at risk for heart failure. Of particular interest is CMR myocardial tagging, a non-invasive technique for assessing regional function that provides a detailed and comprehensive examination of intra-myocardial motion and deformation. Given the current advances in gradient technology, image reconstruction techniques, and data analysis algorithms, CMR myocardial tagging has become the reference modality for evaluating multidimensional strain evolution in the human heart. This review presents an in depth discussion on the current clinical applications of CMR myocardial tagging and the increasingly important role of this technique for assessing subclinical myocardial dysfunction in the setting of a wide variety of myocardial disease processes.

Estimating Myocardial Motion by 4D Image Warping

A method for spatio-temporally smooth and consistent estimation of cardiac motion from MR cine sequences is proposed. Myocardial motion is estimated within a 4-dimensional (4D) registration framework, in which all 3D images obtained at different cardiac phases are simultaneously registered. This facilitates spatio-temporally consistent estimation of motion as opposed to other registration-based algorithms which estimate the motion by sequentially registering one frame to another. To facilitate image matching, an attribute vector (AV) is constructed for each point in the image, and is intended to serve as a "morphological signature" of that point. The AV includes intensity, boundary, and geometric moment invariants (GMIs). Hierarchical registration of two image sequences is achieved by using the most distinctive points for initial registration of two sequences and gradually adding less-distinctive points to refine the registration. Experimental results on real data demonstrate good performance of the proposed method for cardiac image registration and motion estimation. The motion estimation is validated via comparisons with motion estimates obtained from MR images with myocardial tagging.

Left ventricular torsion: an expanding role in the analysis of myocardial dysfunction

During left ventricular (LV) torsion, the base rotates in an overall clockwise direction and the apex rotates in a counterclockwise direction when viewed from apex to base. LV torsion is followed by rapid untwisting, which contributes to ventricular filling. Because LV torsion is directly related to fiber orientation, it might depict subclinical abnormalities in heart function. Recently, ultrasound speckle tracking was introduced for quantification of LV torsion. This fast, widely available technique may contribute to a more rapid introduction of LV torsion as a clinical tool for detection of myocardial dysfunction. However, knowledge of the exact function and structure of the heart is fundamental for understanding the value of LV torsion. LV torsion has been investigated with different measurement methods during the past 2 decades, using cardiac magnetic resonance as the gold standard. The results obtained over the years are helpful for developing a standardized method to quantify LV torsion and have facilitated the interpretation and value of LV torsion before it can be used as a clinical tool.

Segmentation of myocardial boundaries in tagged cardiac MRI using active contours: a gradient-based approach integrating texture analysis

The noninvasive assessment of cardiac function is of first importance for the diagnosis of cardiovascular diseases. Among all medical scanners only a few enables radiologists to evaluate the local cardiac motion. Tagged cardiac MRI is one of them. This protocol generates on Short-Axis (SA) sequences a dark grid which is deformed in accordance with the cardiac motion. Tracking the grid allows specialists a local estimation of cardiac geometrical parameters within myocardium. The work described in this paper aims to automate the myocardial contours detection in order to optimize the detection and the tracking of the grid of tags within myocardium. The method we have developed for endocardial and epicardial contours detection is based on the use of texture analysis and active contours models. Texture analysis allows us to define energy maps more efficient than those usually used in active contours methods where attractor is often based on gradient and which were useless in our case of study, for quality of tagged cardiac MRI is very poor.

Role of MRI in patient selection for CRT

Magnetic resonance imaging has great potential for aiding in the selection of patients who will respond to CRT. MRI is the only imaging tool that can simultaneously assess mechanical dyssynchrony, determine the amount and location of myocardial scar tissue, and map the location of cardiac venous anatomy-three important factors in predicting a patient's response to CRT. The goal of this manuscript is to review the MRI methods that can be used in the selection of patients for CRT.

Motion in cardiovascular MR imaging

Modern rapid magnetic resonance (MR) imaging techniques have led to widespread use of the modality in cardiac imaging. Despite this progress, many MR studies suffer from image degradation due to involuntary motion during the acquisition. This review describes the type and extent of the motion of the heart due to the cardiac and respiratory cycles, which create image artifacts. Methods of eliminating or reducing the problems caused by the cardiac cycle are discussed, including electrocardiogram gating, subject-specific acquisition windows, and section tracking. Similarly, for respiratory motion of the heart, techniques such as breath holding, respiratory gating, section tracking, phase-encoding ordering, subject-specific translational models, and a range of new techniques are considered.

Motion-guided segmentation for cine DENSE MRI

Defining myocardial contours is often the most time-consuming portion of dynamic cardiac MRI image analysis. Displacement encoding with stimulated echoes (DENSE) is a quantitative MRI technique that encodes tissue displacement into the phase of the complex MRI images. Cine DENSE provides a time series of these images, thus facilitating the non-invasive study of myocardial kinematics. Epicardial and endocardial contours need to be defined at each frame on cine DENSE images for the quantification of regional displacement and strain as a function of time. This work presents a reliable and effective two-dimensional semi-automated segmentation technique that uses the encoded motion to project a manually-defined region of interest through time. Contours can then easily be extracted for each cardiac phase. This method boasts several advantages, including, (1) parameters are based on practical physiological limits, (2) contours are calculated for the first few cardiac phases, where it is difficult to visually distinguish blood from myocardium, and (3) the method is independent of the shape of the tissue delineated and can be applied to short- or long-axis views, and on arbitrary regions of interest. Motion-guided contours were compared to manual contours for six conventional and six slice-followed mid-ventricular short-axis cine DENSE datasets. Using an area measure of segmentation error, the accuracy of the segmentation algorithm was shown to be similar to inter-observer variability. In addition, a radial segmentation error metric was introduced for short-axis data. The average radial epicardial segmentation error was 0.36+/-0.08 and 0.40+/-0.10 pixels for slice-followed and conventional cine DENSE, respectively, and the average radial endocardial segmentation error was 0.46+/-0.12 and 0.46+/-0.16 pixels for slice following and conventional cine DENSE, respectively. Motion-guided segmentation employs the displacement-encoded phase shifts intrinsic to DENSE MRI to accurately propagate a single set of pre-defined contours throughout the remaining cardiac phases.

Passive ventricular mechanics modelling using MRI of structure and function

Patients suffering from dilated cardiomyopathy or myocardial infarction can develop left ventricular (LV) diastolic impairment. The LV remodels its structure and function to adapt to pathophysiological changes in geometry and loading conditions and this remodeling process can alter the passive ventricular mechanics. In order to better understand passive ventricular mechanics, a LV finite element model was developed to incorporate physiological and mechanical information derived from in vivo magnetic resonance imaging (MRI) tissue tagging, in vivo LV cavity pressure recording and ex vivo diffusion tensor MRI (DTMRI) of a canine heart. MRI tissue tagging enables quantitative evaluation of cardiac mechanical function with high spatial and temporal resolution, whilst the direction of maximum water diffusion (the primary eigenvector) in each voxel of a DTMRI directly correlates with the myocardial fibre orientation. This model was customized to the geometry of the canine LV during diastasis by fitting the segmented epicardial and endocardial surface data from tagged MRI using nonlinear finite element fitting techniques. Myofibre orientations, extracted from DTMRI of the same heart, were incorporated into this geometric model using a free form deformation methodology. Pressure recordings, temporally synchronized to the tissue tagging MRI data, were used to simulate the LV deformation during diastole. Simulation of the diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI. This integrated physiological model will allow more insight into the regional passive diastolic mechanics of the LV on an individualized basis, thereby improving our understanding of the underlying structural basis of mechanical dysfunction in pathological conditions.

Preliminary validation of angle-independent myocardial elastography using MR tagging in a clinical setting

Myocardial elastography (ME), a radio-frequency (RF) based speckle tracking technique, was employed in order to image the entire two-dimensional (2D) transmural deformation field in full echocardiographic views and was validated against tagged magnetic resonance imaging (tMRI) in normal as well as reperfused (i.e., treated myocardial infarction [MI]) human left ventricles. RF ultrasound and tMRI frames were acquired at the papillary muscle level in 2D short-axis (SA) views at the frame rates of 136 (fps; real-time) and 33 fps (electrocardiogram [ECG]-gated), respectively. In ME, in-plane, 2D (lateral and axial) incremental displacements were iteratively estimated using one-dimensional (1D) cross-correlation and recorrelation techniques in a 2D search with a 1D matching kernel. In tMRI, cardiac motion was estimated by a template-matching algorithm on a 2D grid-shaped mesh. In both ME and tMRI, cumulative 2D displacements were obtained and then used to estimate 2D Lagrangian finite systolic strains, from which polar (i.e., radial and circumferential) strains, namely angle-independent measures, were further obtained through coordinate transformation. Principal strains, which are angle-independent and less centroid-dependent than polar strains, were also computed and imaged based on the 2D finite strains using methodology previously established. Both qualitatively and quantitatively, angle-independent ME is shown to be capable of (1) estimating myocardial deformation in good agreement with tMRI estimates in a clinical setting and of (2) differentiating abnormal from normal myocardium in a full left-ventricular view. The principal strains were concluded to be a potential diagnostic measure for detection of cardiac disease with reduced centroid dependence.

Tag separation in cardiac tagged MRI

In this paper we introduce a tag separation method for better cardiac boundary segmentation and tag tracking. Our approach is based on two observations in the cardiac tagged MR images: 1) the tag patterns have a regular texture; 2) the cardiac images without tag patterns are piecewise smooth with sparse gradients. These observations motivate us to use two dictionaries, one based on the Discrete Cosine Transform for representing tag patterns and the other based on the Wavelet Transform for representing the underlying cardiac image without tag patterns. The two dictionaries are built such that they can lead to sparse representations of the tag patterns and of the piece-wise smooth regions without tag patterns. With the two dictionaries, a new tag separation approach is proposed to simultaneously optimize w.r.t. the two sparse representations, where optimization is directed by the Total Variation regularization scheme. While previous methods have focused on tag removal, our approach to acquiring both optimally-decomposed tag-only image and the cardiac image without tags simultaneously can be used for better tag tracking and cardiac boundary segmentation. We demonstrate the superior performance of the proposed approach through extensive experiments on large sets of cardiac tagged MR images.

Maximum a posteriori strategy for the simultaneous motion and material property estimation of the heart

In addition to its technical merits as a challenging nonrigid motion and structural integrity analysis problem, quantitative estimation of cardiac regional functions and material characteristics has significant physiological and clinical value. We developed a stochastic finite-element framework for the simultaneous recovery of myocardial motion and material parameters from medical image sequences with an extended Kalman filter approach, and we have shown that this simultaneous estimation strategy achieves more accurate and robust results than separated motion and material estimation efforts. In this paper, we present a new computational strategy for the framework based upon the maximum a posteriori estimation principles, realized through the extended Kalman smoother, that produces a sequence of kinematics state and material parameter estimation of the entire myocardium, including the endocardial, epicardial, and midwall tissues. The system dynamics equations of the heart are constructed using a biomechanical model with stochastic parameters, and the tissue material and deformation parameters are jointly estimated from the periodic imaging data. Noise-corrupted synthetic image sequences with known kinematics and material parameters are used to assess the accuracy and robustness of the framework. Experiments with canine magnetic resonance tagging and phase-contrast image sequences have been conducted with very promising results, as validated through comparison to the histological staining of postmortem myocardium.

Adaptive postprocessing techniques for myocardial tissue tracking with displacement-encoded MR imaging

The purpose of this study was to prospectively assess the effects of two adaptive postprocessing techniques on the evaluation of myocardial function with displacement-encoded magnetic resonance (MR) imaging, including sensitivity for abnormal wall motion, with two-dimensional echocardiography as the reference standard. Sixteen patients (11 men, five women; age range, 26-74 years) and 12 volunteers (six men, six women; age range, 29-53 years) underwent breath-hold MR imaging. Institutional review board approval and informed consent were obtained. Adaptive phase-unwrapping and spatial filtering techniques were compared with conventional phase-unwrapping and spatial filtering techniques. Use of the adaptive techniques led to a reduced rate of failure with the phase-unwrapping technique from 18.9% to 0.6% (P < .001), resulted in lower variability of segmental strain measurements among healthy volunteers (P < .001 to P = .02), and increased the sensitivity of quantitative detection of abnormal segments in patients from 82.5% to 87.7% (P = .034). The adaptive techniques improved the semiautomated postprocessing of displacement-encoded cardiac images and increased the sensitivity of detection of abnormal wall motion in patients.

SUPPLEMENTAL MATERIAL

http://radiology.rsnajnls.org/cgi/content/full/246/1/229/DC1.

Quantitative comparison of 2D and 3D circumferential strain using MRI tagging in normal and LBBB hearts

The response to cardiac resynchronization therapy (CRT), which is applied to patients with heart failure (HF) and left bundle-branch block (LBBB), can be predicted from the mechanical dyssynchrony measured on circumferential strain. Circumferential strain can be assessed by either 2D or 3D strain analysis. In this study was evaluated the difference between 2D and 3D circumferential strain using MR tagging with high temporal resolution (14 ms). Six healthy volunteers and five patients with LBBB were evaluated. We compared the 2D and 3D circumferential strains by computing the mechanical dyssynchrony and the cross correlation (r) between 2D and 3D strain curves, and by quantifying the differences in peak circumferential shortening, time to onset, and time to peak of shortening. The obtained maximum r(2) values were 0.97 +/- 0.03 and 0.87 +/- 0.16 for the healthy and LBBB populations, respectively, and thus showed a good similarity between 2D and 3D strain curves. No significant difference was observed between 2D and 3D in time to onset, time to peak, or peak circumferential shortening. Thus, to measure dyssynchrony, 2D strain analysis will suffice. Since 2D analysis is easier to implement than 3D analysis, this finding brings the application of MRI tagging and strain analysis closer to the clinical routine.