Tagged MR Research Articles

TagGen: Diffusion-based generative model for cardiac MR tagging super resolution.

The aim of the work is to develop a cascaded diffusion-based super-resolution model for low-resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag grid quality of low-resolution images. We introduced TagGen, a diffusion-based conditional generative model that uses low-resolution MR tagging images as guidance to generate corresponding high-resolution tagging images. The model was developed on 50 patients with long-axis-view, high-resolution tagging acquisitions. During training, we retrospectively synthesized LR tagging images using an undersampling rate (R) of 3.3 with truncated outer phase-encoding lines. During inference, we evaluated the performance of TagGen and compared it with REGAIN, a generative adversarial network-based super-resolution model that was previously applied to MR tagging. In addition, we prospectively acquired data from 6 subjects with three heartbeats per slice using 10-fold acceleration achieved by combining low-resolution R = 3.3 with GRAPPA-3 (generalized autocalibrating partially parallel acquisitions 3). For synthetic data (R = 3.3), TagGen outperformed REGAIN in terms of normalized root mean square error, peak signal-to-noise ratio, and structural similarity index (p < 0.05 for all). For prospectively 10-fold accelerated data, TagGen provided better tag grid quality, signal-to-noise ratio, and overall image quality than REGAIN, as scored by two (blinded) radiologists (p < 0.05 for all). We developed a diffusion-based generative super-resolution model for MR tagging images and demonstrated its potential to integrate with parallel imaging to reconstruct highly accelerated cine MR tagging images acquired in three heartbeats with enhanced tag grid quality.

Abstract 13652: A Randomised Double-Blind Placebo-Controlled Study of Fenofibrate as a Metabolic Modulator to Augment Cardiac Physiology in Moderate-Severe Aortic Stenosis

Background: Pressure overload in aortic stenosis (AS) is associated with reduced cardiac fatty acid oxidation (FAO) and evidence of myocardial steatosis and impaired myocardial energetics. Here, we investigated whether modulating myocardial metabolism to increase FAO with a PPARα agonist, Fenofibrate in moderate-severe AS had effects on cardiac metabolism and physiology. Methods: 67 participants with asymptomatic moderate-severe AS were prospectively randomised (double-blinded) to receive either Fenofibrate 200mg once daily (N= 54) or placebo (N=13) for 6 months (180 ± 7 days duration). Follow-up data is available in 42 patients. Primary endpoint was reduction in myocardial triglyceride content (MTG) measured using proton magnetic resonance spectroscopy (MRS). Secondary endpoints included cardiac PCr/ATP measured by 31P-MRS and LV strain assessed using MR tagging. Results: There was significant reduction in MTG content (-0.40%, CI -0.74 to -0.06, p= 0.022), with a no significant reduction seen in placebo. (-0.46%, CI -1.1 to 0.17, p= 0.13). A significant increase in venous butyrate levels was seen in the fibrate group (+0.09 mmol/l, CI 0.006 to 0.17, p= 0.036) in line with increased fatty acid oxidation. No change in cardiac energetics was seen in either group. No change in cardiac function was seen in either group, global longitudinal strain (-0.07%, p 0.89 Fibrate vs -0.31%, p 0.67 Placebo), circumferential strain ( -0.15 %, p 0.80 Fibrate vs 1.71%, p 0.07 Placebo). Conclusions: Fenofibrate can potentially upregulate fatty acid oxidation in hypertrophied AS hearts reducing myocardial lipid content, but this did not have any measurable beneficial physiological effect in moderate-severe AS.

Quantification of left ventricular strain and torsion by joint analysis of 3D tagging and cine MR images.

Cardiovascular magnetic resonance (CMR) imaging is the gold standard for the non-invasive assessment of left-ventricular (LV) function. Prognostic value of deformation metrics extracted directly from regular SSFP CMR images has been shown by numerous studies in the clinical setting, but with some limitations to detect torsion of the myocardium. Tagged CMR introduces trackable features in the myocardium that allow for the assessment of local myocardial deformation, including torsion; it is, however, limited in the quantification of radial strain, which is a decisive metric for assessing the contractility of the heart. In order to improve SSFP-only and tagged-only approaches, we propose to combine the advantages of both image types by fusing global shape motion obtained from SSFP images with the local deformation obtained from tagged images. To this end, tracking is first performed on SSFP images, and subsequently, the resulting motion is utilized to mask and track tagged data. Our implementation is based on a recent finite element-based motion tracking tool with mechanical regularization. Joint SSFP and tagged images registration performance is assessed based on deformation metrics including LV strain and twist using human and in-house porcine datasets. Results show that joint analysis of SSFP and 3DTAG images provides better quantification of LV strain and twist as either data source alone.

Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain)

Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical characteristics of organs such as the heart, the liver, and the brain. However, constructing accurate artifact-free pixelwise strain maps at the native resolution of the tagged images has for decades been a challenging unsolved task. In this work, we developed an end-to-end deep-learning framework for pixel-to-pixel mapping of the two-dimensional Eulerian principal strains varvec{{varepsilon }}_{boldsymbol{p1}} and varvec{{varepsilon }}_{boldsymbol{p2}} directly from 1-1 spatial modulation of magnetization (SPAMM) tMRI at native image resolution using convolutional neural network (CNN). Four different deep learning conditional generative adversarial network (cGAN) approaches were examined. Validations were performed using Monte Carlo computational model simulations, and in-vivo datasets, and compared to the harmonic phase (HARP) method, a conventional and validated method for tMRI analysis, with six different filter settings. Principal strain maps of Monte Carlo tMRI simulations with various anatomical, functional, and imaging parameters demonstrate artifact-free solid agreements with the corresponding ground-truth maps. Correlations with the ground-truth strain maps were R = 0.90 and 0.92 for the best-proposed cGAN approach compared to R = 0.12 and 0.73 for the best HARP method for varvec{{varepsilon }}_{boldsymbol{p1}} and varvec{{varepsilon }}_{boldsymbol{p2}}, respectively. The proposed cGAN approach's error was substantially lower than the error in the best HARP method at all strain ranges. In-vivo results are presented for both healthy subjects and patients with cardiac conditions (Pulmonary Hypertension). Strain maps, obtained directly from their corresponding tagged MR images, depict for the first time anatomical, functional, and temporal details at pixelwise native high resolution with unprecedented clarity. This work demonstrates the feasibility of using the deep learning cGAN for direct myocardial and liver Eulerian strain mapping from tMRI at native image resolution with minimal artifacts.

DUAL-CYCLE CONSTRAINED BIJECTIVE VAE-GAN FOR TAGGED-TO-CINE MAGNETIC RESONANCE IMAGE SYNTHESIS.

Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs. Due to tagged MRI's intrinsic low anatomical resolution, another matching set of cine MRI with higher resolution is sometimes acquired in the same scanning session to facilitate tissue segmentation, thus adding extra time and cost. To mitigate this, in this work, we propose a novel dual-cycle constrained bijective VAE-GAN approach to carry out tagged-to-cine MR image synthesis. Our method is based on a variational autoencoder backbone with cycle reconstruction constrained adversarial training to yield accurate and realistic cine MR images given tagged MR images. Our framework has been trained, validated, and tested using 1,768, 416, and 1,560 subject-independent paired slices of tagged and cine MRI from twenty healthy subjects, respectively, demonstrating superior performance over the comparison methods. Our method can potentially be used to reduce the extra acquisition time and cost, while maintaining the same workflow for further motion analyses.

Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strains in Tagged MRI.

Cardiac tagged MR images allow for deformation fields to be measured in the heart by tracking the motion of tag lines throughout the cardiac cycle. Machine learning (ML) algorithms enable accurate and robust tracking of tag lines. Herein, the use of a massive synthetic physics-driven training dataset with known ground truth was used to train an ML network to enable tracking any number of points at arbitrary positions rather than anchored to the tag lines themselves. The tag tracking and strain calculation methods were investigated in a computational deforming cardiac phantom with known (ground truth) strain values. This enabled both tag tracking and strain accuracy to be characterized for a range of image acquisition and tag tracking parameters. The methods were also tested on in vivo volunteer data. Median tracking error was <0.26mm in the computational phantom, and strain measurements were improved in vivo when using the arbitrary point tracking for a standard clinical protocol.

Comparison of cardiac MR feature tracking and myocardial MR tagging for assessment of regional ventricular function

Abstract Background Quantification of regional myocardial function allows risk stratification in heart disease. CMR tagging (TAG) enables the evaluation of segmental cardiac deformation, but it has not reached clinical routine due to the long acquisition and post-processing times. Conversely, CMR feature-tracking (FT) is a post-processing method based on standard cine-MR imaging. Purpose To compare myocardial strain and torsion obtained with CMR-TAG and CMR-FT in healthy volunteers and myocardial infarction (MI). Methods 42 subjects (18 healthy; 24 MI) underwent CMR (1.5T, cine/TAG sequences). Global and segmental (16-segment) circumferential strain (CS), and torsion were measured using FT (CVI42, Canada) and tagging (InTag, France). Inter-method agreement was assessed using 2-way-mixed intraclass correlation coefficient (ICC). Results The agreement for segmental and global CS measurements was good to excellent in both groups (Table). Torsion angle showed excellent (0.763) and good (0.697) agreement for healthy and MI. Conclusion CMR-FT strain and torsion measurements showed high agreement with CMR-tagging. Thus, CMR-FT provides a potential clinical alternative in the assessment of regional ventricular function. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Carlos III Health Institute, Spanish Ministry of Economy and Competiveness; Agencia Valenciana de la Innovaciόn, Generalitat Valenciana

Subclinical effects of long-chain fatty acid β-oxidation deficiency on the adult heart: A case-control magnetic resonance study.

Cardiomyopathy can be a severe complication in patients with long‐chain fatty acid β‐oxidation disorders (LCFAOD), particularly during episodes of metabolic derangement. It is unknown whether latent cardiac abnormalities exist in adult patients. To investigate cardiac involvement in LCFAOD, we used proton magnetic resonance imaging (MRI) and spectroscopy (1H‐MRS) to quantify heart function, myocardial tissue characteristics, and myocardial lipid content in 14 adult patients (two with long‐chain 3‐hydroxyacyl‐CoA dehydrogenase deficiency (LCHADD); four with carnitine palmitoyltransferase II deficiency (CPT2D); and eight with very long‐chain acyl‐CoA dehydrogenase deficiency (VLCADD)) and 14 gender‐, age‐, and BMI‐matched control subjects. Examinations included cine MRI, MR tagging, native myocardial T1 and T2 mapping, and localized 1H‐MRS at 3 Tesla. Left ventricular (LV) myocardial mass (P = .011) and the LV myocardial mass‐to‐volume ratio (P = .008) were higher in patients, while ejection fraction (EF) was normal (P = .397). LV torsion was higher in patients (P = .026), whereas circumferential shortening was similar compared with controls (P = .875). LV hypertrophy was accompanied by high myocardial T1 values (indicative of diffuse fibrosis) in two patients, and additionally a low EF in one case. Myocardial lipid content was similar in patients and controls. We identified subclinical morphological and functional differences between the hearts of LCFAOD patients and matched control subjects using state‐of‐the‐art MR methods. Our results suggest a chronic cardiac disease phenotype and hypertrophic LV remodeling of the heart in LCFAOD, potentially triggered by a mild, but chronic, energy deficiency, rather than by lipotoxic effects of accumulating lipid metabolites.

Detecting liver fibrosis using a machine learning-based approach to the quantification of the heart-induced deformation in tagged MR images.

Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver failure and portal hypertension. Early detection of liver fibrosis helps in improving its treatment and prevents its progression to cirrhosis. In this work, we present a novel noninvasive method to detect liver fibrosis from tagged MRI images using a machine learning-based approach. Specifically, coronal and sagittal tagged MRI imaging are analyzed separately to capture cardiac-induced deformation of the liver. The liver is manually delineated and a novel image feature, namely, the histogram of the peak strain (HPS) value, is computed from the segmented liver region and is used to classify the liver as being either normal or fibrotic. Classification is achieved using a support vector machine algorithm. The in vivo study included 15 healthy volunteers (10 males; age range 30-45years) and 22 patients (15 males; age range 25-50years) with liver fibrosis verified and graded by transient elastography, and 10 patients only had a liver biopsy and were diagnosed with a score of F3-F4. The proposed method demonstrates the usefulness and efficiency of extracting the HPS features from the sagittal slices for patients with moderate fibrosis. Cross-validation of the method showed an accuracy of 83.7% (specificity=86.6%, sensitivity=81.8%).

Improved tagged cardiac MRI myocardium strain analysis by leveraging cine segmentation

Background and objectivesTagged MR images provide an effective way for regional analysis of the myocardium strain. A reliable myocardium strain analysis requires both correct segmentation and accurate motion tracking of the myocardium during the cardiac cycle. While many algorithms have been proposed for accurate tracking of the myocardium in tagged MR images, little focus has been placed on ensuring correct segmentation of the tagged myocardium during the cardiac cycle. Myocardial strain analysis is usually done by segmenting the myocardium in end-diastole, generating a mesh from the segmentation, propagating the mesh through the cardiac cycle using the output deformation field from motion tracking, and measuring strain on the deforming mesh. Due to the imposed tag strips on the anatomy, identification of the myocardium boundaries is challenging in tagged MR images. As a result, there is no guarantee that the propagated mesh is annotating the myocardium accurately through the cardiac cycle. Moreover, clinical studies indicate that incorrect myocardium annotation can result in overestimation of myocardial strains. MethodsWe introduce a method to improve reliability of strain analysis by proposing a mesh which correctly segments the myocardium in tagged MRI by leveraging the available cine MRI segmentation. In particular, we generate a series of mesh proposals using the cine MRI segmentation and find the propagated mesh proposal which gives the most accurate full-cycle myocardium segmentation. ResultsThe mesh selection algorithm was tested on 22 2D MRI scans of diseased and healthy hearts. The proposed algorithm provided more accurate whole-cycle myocardium segmentation compared to the propagated end-diastolic mesh. Regional myocardium strain was measured for 10 3D MRI scans of healthy volunteers using the proposed mesh and the end-diastolic mesh. The measured strain using the proposed mesh was more similar to the expected myocardium strain for a healthy heart than the measured strain using the end-diastolic mesh. ConclusionThe proposed approach provides accurate whole-cycle tagged myocardium segmentation and more reliable myocardium strain analysis.

MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac 18F-FDG imaging

Motion of the myocardium deteriorates the quality and quantitative accuracy of cardiac PET images. We present a method for MR-based cardiac and respiratory motion correction of cardiac PET data and evaluate its impact on estimation of activity and kinetic parameters in human subjects. Three healthy subjects underwent simultaneous dynamic 18F-FDG PET and MRI on a hybrid PET/MR scanner. A cardiorespiratory motion field was determined for each subject using navigator, tagging and golden-angle radial MR acquisitions. Acquired coincidence events were binned into cardiac and respiratory phases using electrocardiogram and list mode-driven signals, respectively. Dynamic PET images were reconstructed with MR-based motion correction (MC) and without motion correction (NMC). Parametric images of 18F-FDG consumption rates (Ki) were estimated using Patlak’s method for both MC and NMC images. MC alleviated motion artifacts in PET images, resulting in improved spatial resolution, improved recovery of activity in the myocardium wall and reduced spillover from the myocardium to the left ventricle cavity. Significantly higher myocardium contrast-to-noise ratio and lower apparent wall thickness were obtained in MC versus NMC images. Likewise, parametric images of Ki calculated with MC data had improved spatial resolution as compared to those obtained with NMC. Consistent with an increase in reconstructed activity concentration in the frames used during kinetic analyses, MC led to the estimation of higher Ki values almost everywhere in the myocardium, with up to 18% increase (mean across subjects) in the septum as compared to NMC. This study shows that MR-based motion correction of cardiac PET results in improved image quality that can benefit both static and dynamic studies.

Segmentation of cardiac tagged MR images using a snake model based on hybrid gradient vector flow

In the segmentation of cardiac tagging magnetic resonance (tMR) images, it is difficult to segment the left ventricle automatically by using the traditional segmentation model because of the interference caused by the tags. A new snake model based on hybrid gradient vector flow (HGVF) is proposed by us to improve this segmentation. Due to the different characteristics between endocardium and epicardium of the left ventricle (LV), several gradient vector flows (GVFs) with distinctive boundary information would be fused to segment these two sub regions individually. For segmentation of endocardium, we construct a new HGVF in snake model fused by three independent GVFs. These flows are respectively exported from the original cardiac tMR image, the tags-removed image and the local-filtered image. On the other hand, since the epicardium is with a nearly-circle shape, we construct the other HGVF which is composed of two different GVFs. One of them is derived from the tags-removed image either and the other one is derived from the ideal circle-shape image. Some experiments have been done to validate our new segmentation model. The average overlap of the endocardium segmentation is 89.67% (its mean absolute distance is 1.86 pixels), and the average overlap of the epicardium segmentation is 95.88% (its mean absolute distance is 1.64 pixels). Experimental results show that the proposed method improves the segmentation performance compared to some available methods effectively.

Comprehensive MRI for the detection of subtle alterations in diastolic cardiac function in apoE/LDLR(-/-) mice with advanced atherosclerosis.

ApoE/LDLR(-/-) mice represent a reliable model of atherosclerosis. However, it is not clear whether cardiac performance is impaired in this murine model of atherosclerosis. Here, we used MRI to characterize cardiac performance in vivo in apoE/LDLR(-/-) mice with advanced atherosclerosis. Six-month-old apoE/LDLR(-/-) mice and age-matched C57BL/6J mice (control) were examined using highly time-resolved cine-MRI [whole-chamber left ventricle (LV) imaging] and MR tagging (three slices: basal, mid-cavity and apical). Global and regional measures of cardiac function included LV volumes, kinetics, time-dependent parameters, strains and rotations. Histological analysis was performed using OMSB (orceine with Martius, Scarlet and Blue) and ORO (oil red-O) staining to demonstrate the presence of advanced coronary atherosclerosis. MR-tagging-based strain analysis in apoE/LDLR(-/-) mice revealed an increased frequency of radial and circumferential systolic stretch (25% and 50% of segments, respectively, p ≤ 0.012), increased radial post-systolic strain index (45% of segments, p = 0.009) and decreased LV untwisting rate (-30.3° (11.6°)/cycle, p = 0.004) when compared with control mice. Maximal strains and LV twist were unchanged. Most of the cine-MRI-based LV functional and anatomical parameters also remained unchanged in apoE/LDLR(-/-) mice, with only a lower filling rate, longer filling time, shorter isovolumetric contraction time and slower heart rate observed in comparison with control mice. The coronary arteries displayed severe atherosclerosis, as evidenced by histological analysis. Using comprehensive MRI methods, we have demonstrated that, despite severe coronary atherosclerosis in six-month-old apoE/LDLR(-/-) mice, cardiac performance including global parameters, twist and strains, was well preserved. Only subtle diastolic alterations, possibly of ischemic background, were uncovered. Copyright © 2016 John Wiley & Sons, Ltd.

Efficient radial tagging CMR exam: A coherent k-space reading and image reconstruction approach.

Cardiac MR tagging techniques, which facilitate the strain evaluation, have not yet been widely adopted in clinics due to inefficiencies in acquisition and postprocessing. This problem may be alleviated by exploiting the coherency in the three steps of tagging: preparation, acquisition, and reconstruction. Herein, we propose a fully polar-based tagging approach that may lead to real-time strain mapping. Radial readout trajectories were used to acquire radial tagging images and a Hankel-based algorithm, referred to as Polar Fourier Transform (PFT), has been adapted for reconstruction of the acquired raw data. In both phantom and human subjects, the overall performance of the method was investigated against radial undersampling and compared with the conventional reconstruction methods. Radially tagged images were reconstructed by the proposed PFT method from as few as 24 spokes with normalized root-mean-square-error of less than 3%. The reconstructed images showed a central focusing behavior, where the undersampling effects were pushed to the peripheral areas out of the central region of interest. Comparing the results with the re-gridding reconstruction technique, superior image quality and high robustness of the method were further established. In addition, a relative increase of 68 ± 2.5% in tagline sharpness was achieved for the PFT images and also higher tagging contrast (72 ± 5.6%), resulted from the well-tolerated undersampling artifacts, was observed in all reconstructions. The proposed approach led to the acceleration of the acquisition process, which was evaluated for up to eight-fold retrospectively from the fully sampled data. This is promising toward real-time imaging, and in contrast to iterative techniques, the method is consistent with online reconstruction. Magn Reson Med 77:1459-1472, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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.

WE-AB-BRA-03: Displacement Vector Field (DVF) Error Correction Using Sparsely Distributed Ground-Truth Displacement Vectors

Purpose: This study aims to develop a technique capable of reducing errors in deformable image registration (DIR) by integrating information of sparsely distributed ground-truth displacement vectors into a dense DVF. Methods: An in-house developed lung motion phantom consisting of an end-of-inhalation (EOI) MR image, a dense DVF derived from tagging MR data, and an end-of-exhalation (EOE) image obtained by deforming the EOI image with the DVF was used. The DVFs to be corrected were obtained by registration between the EOI and the calculated EOE images using Velocity, MIM, ILK and OHS algorithms in DIRART toolbox, and Elastix. A set of sparsely distributed ground-truth points were selected and displacement vectors at those points were treated as ground-truth displacement vectors. Displacement vectors in the DVFs to be corrected were converted into coefficient vectors using Wendland's compactly supported functions. The coefficient map was modified such that the displacement vectors at ground-truth points, determined by the modified map, match the ground-truth displacement vectors. DVFs before and after correction were compared with the ground-truth DVF (phantom DVF), respectively. Pre- and post-correction registration errors were calculated. Results: Reduced registration errors were found in all tested DVFs. Pre- and post-correction 3D errors per voxel for Velocity, MIM, ILK, OHS, and Elastix are 1.27mm vs 1.07mm, 1.56mm vs 1.55mm, 6.33mm vs 4.81mm, 3.53mm vs 2.94mm, 2mm vs 1.67mm. Reduced registration errors are also uniformly found in every direction (p<0.01). Conclusion: We have implemented a hybrid registration technique that can potentially reduce registration errors by introducing information of sparsely distributed ground-truth displacement vectors into a dense DVF. Simulation results show that ground-truth points less than 0.5% of total number of voxels can enhance the accuracy of DVFs. Further studies on integration of biomechanical models and fine-tuning of parameters are expected to provide further correction.

Accelerated acquisition of tagged MRI for cardiac motion correction in simultaneous PET-MR: phantom and patient studies.

Degradation of image quality caused by cardiac and respiratory motions hampers the diagnostic quality of cardiac PET. It has been shown that improved diagnostic accuracy of myocardial defect can be achieved by tagged MR (tMR) based PET motion correction using simultaneous PET-MR. However, one major hurdle for the adoption of tMR-based PET motion correction in the PET-MR routine is the long acquisition time needed for the collection of fully sampled tMR data. In this work, the authors propose an accelerated tMR acquisition strategy using parallel imaging and/or compressed sensing and assess the impact on the tMR-based motion corrected PET using phantom and patient data. Fully sampled tMR data were acquired simultaneously with PET list-mode data on two simultaneous PET-MR scanners for a cardiac phantom and a patient. Parallel imaging and compressed sensing were retrospectively performed by GRAPPA and kt-FOCUSS algorithms with various acceleration factors. Motion fields were estimated using nonrigid B-spline image registration from both the accelerated and fully sampled tMR images. The motion fields were incorporated into a motion corrected ordered subset expectation maximization reconstruction algorithm with motion-dependent attenuation correction. Although tMR acceleration introduced image artifacts into the tMR images for both phantom and patient data, motion corrected PET images yielded similar image quality as those obtained using the fully sampled tMR images for low to moderate acceleration factors (<4). Quantitative analysis of myocardial defect contrast over ten independent noise realizations showed similar results. It was further observed that although the image quality of the motion corrected PET images deteriorates for high acceleration factors, the images were still superior to the images reconstructed without motion correction. Accelerated tMR images obtained with more than 4 times acceleration can still provide relatively accurate motion fields and yield tMR-based motion corrected PET images with similar image quality as those reconstructed using fully sampled tMR data. The reduction of tMR acquisition time makes it more compatible with routine clinical cardiac PET-MR studies.

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.

Regional Heterogeneity in 3D Myocardial Shortening in Hypertensive Left Ventricular Hypertrophy: A Cardiovascular CMR Tagging Substudy to the Life Study.

BackgroundIncreased relative wall thickness in hypertensive left ventricular hypertrophy (LVH) has been shown by echocardiography to allow preserved shortening at the endocardium despite depressed LV midwall circumferential shortening (MWCS). Depressed MWCS is an adverse prognostic indicator, but whether this finding reflects reduced global or regional LV myocardial function, as assessed by three-dimensional (3D) myocardial strain, is unknown.Methods and ResultsCardiac Magnetic Resonance (CMR) tissue tagging permits direct evaluation of regional 3D intramyocardial strain, independent of LV geometry. We evaluated 21 hypertensive patients with electrocardiographic LVH in the LIFE study and 8 normal controls using 3D MR tagging and echocardiography. Patients had higher MR LV mass than normals (116 ± 40 versus 63 ± 6 g/m2, P = 0.002). Neither echocardiographic fractional shortening (32 ± 6 versus 33% ± 3%), LVEF (63% versus 64%) or mean end-systolic stress (175 ± 27 versus 146 ± 28 g/cm2) were significantly different, yet global MWCS was decreased by both echocardiography (13.4 ± 2.8 versus 18.2% ± 1.5%, P < 0.001) and MR (16.8 ± 3.6 versus 21.6% ± 3.0%, P < 0.005). 3D MR MWCS was lower at the base versus apex (P = 0.002) in LVH and greater in lateral and anterior regions versus septal and posterior regions (P < 0.001), contributing to the higher mean global MWCS by MR than echo. MR longitudinal strain was severely depressed in LVH patients (11.0 ± 3.3 versus 16.5% ± 2.5%, P < 0.001) and apical twist was increased (17.5 ± 4.3 versus 13.7 ± 3.7, P < 0.05). Importantly, both circumferential and longitudinal shortening correlated with LV relative wall thickness (R > 0.60, P = 0.001 for both).ConclusionsIn patients with hypertensive LVH, despite normal LV function via echocardiography or CMR, CMR intramyocardial tagging show depressed global MWCS while 3D MR strain revealed marked underlying regional heterogeneity of LV dysfunction.

Optimization of tagged MRI for quantification of liver stiffness using computer simulated data.

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.