Characterization of dynamic changes in cardiac microstructure after reperfused ST-elevation myocardial infarction by biphasic diffusion tensor cardiovascular magnetic resonance.

BACKGROUND AND AIMS: Microstructural disturbances underlie dysfunctional contraction and adverse left ventricular (LV) remodelling after ST-elevation myocardial infarction (STEMI). Biphasic diffusion tensor cardiovascular magnetic resonance (DT-CMR) quantifies dynamic reorientation of sheetlets (E2A) from diastole to systole during myocardial thickening, and markers of tissue integrity [mean diffusivity (MD) and fractional anisotropy (FA)]. This study investigated whether microstructural alterations identified by biphasic DT-CMR: (i) enable contrast-free detection of acute myocardial infarction (MI); (ii) associate with severity of myocardial injury and contractile dysfunction; and (iii) predict adverse LV remodelling. METHODS: Biphasic DT-CMR was acquired 4 days (n = 70) and 4 months (n = 66) after reperfused STEMI and in healthy volunteers (HVOLs) (n = 22). Adverse LV remodelling was defined as an increase in LV end-diastolic volume ≥ 20% at 4 months. MD and FA maps were compared with late gadolinium enhancement images. RESULTS: Widespread microstructural disturbances were detected post-STEMI. In the acute MI zone, diastolic E2A was raised and systolic E2A reduced, resulting in reduced E2A mobility (all P < .001 vs. adjacent and remote zones and HVOLs). Acute global E2A mobility was the only independent predictor of adverse LV remodelling (odds ratio .77; 95% confidence interval .63-.94; P = .010). MD and FA maps had excellent sensitivity and specificity (all > 90%) and interobserver agreement for detecting MI presence and location. CONCLUSIONS: Biphasic DT-CMR identifies microstructural alterations in both diastole and systole after STEMI, enabling detection of MI presence and location as well as predicting adverse LV remodelling. DT-CMR has potential to provide a single contrast-free modality for MI detection and prognostication of patients after acute STEMI.

Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance.

PURPOSE: The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments. METHODS: Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction. RESULTS: Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correction methods, with referenceless methods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600 smm - 2 $$ {\mathrm{smm}}^{-2} $$ . It is worth noting that as the strength of the diffusion encoding increases, the performance gap between the referenceless method and the navigator-based method diminishes. CONCLUSION: Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans.

The effects of field strength on stimulated echo and motion-compensated spin-echo diffusion tensor cardiovascular magnetic resonance sequences.

BACKGROUND: In-vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) is an emerging technique for microstructural tissue characterization in the myocardium. Most studies are performed at 3T, where higher signal-to-noise ratio (SNR) should benefit this signal-starved method. However, a few studies have suggested that DT-CMR is possible at 1.5T, where echo planar imaging artifacts may be less severe and 1.5T hardware is more widely available. METHODS: We recruited 20 healthy volunteers and performed mid-ventricular short-axis DT-CMR at 1.5T and 3T. Acquisitions were performed at peak systole and end-diastole using both stimulated echo acquisition mode (STEAM) and motion-compensated spin-echo (MCSE) sequences at matched spatial resolutions. DT-CMR parameters were averaged over the left ventricle and compared between 1.5T and 3T sequences using both datasets with and without the blow reference data included. RESULTS: Eleven (1.5T) and 12 (3T) diastolic MCSE acquisitions were rejected as the helix angle (HA) demonstrated <50% normal appearance circumferentially or the acquisition was abandoned due to poor image quality; a maximum of one acquisition was rejected for other datasets. Subjective HA map quality was significantly better at 3T than 1.5T for STEAM (p < 0.05), but not for MCSE and other DT-CMR quality measures were consistent with improvements in STEAM at 3T over 1.5T. When blow data were excluded, no significant differences in mean diffusivity were observed between field strengths, but fractional anisotropy was significantly higher at 1.5T than 3T for STEAM systole (p < 0.05). Absolute second eigenvector orientation (E2A, sheetlet angle) was significantly higher at 1.5T than 3T for MCSE systole and STEAM diastole, but significantly lower for STEAM systole (all p < 0.05). Transmural HA distribution was less steep at 1.5T than 3T for STEAM diastole data (p < 0.05). SNR was higher at 3T than 1.5T for all acquisitions (p < 0.05). CONCLUSION: While 3T provides benefits in terms of SNR, both STEAM and MCSE can be performed at 1.5T. However, MCSE is unreliable in diastole at both field strengths and STEAM benefits from the improved SNR at 3T over 1.5T. Future clinical research studies may be able to leverage the wider availability of 1.5T CMR hardware where MCSE acquisitions are desirable.

STEAM-SASHA: a novel approach for blood- and fat-suppressed native T1 measurement in the right ventricular myocardium.

OBJECTIVE: The excellent blood and fat suppression of stimulated echo acquisition mode (STEAM) can be combined with saturation recovery single-shot acquisition (SASHA) in a novel STEAM-SASHA sequence for right ventricular (RV) native T1 mapping. MATERIALS AND METHODS: STEAM-SASHA splits magnetization preparation over two cardiac cycles, nulling blood signal and allowing fat signal to decay. Breath-hold T1 mapping was performed in a T1 phantom and twice in 10 volunteers using STEAM-SASHA and a modified Look-Locker sequence at peak systole at 3T. T1 was measured in 3 RV regions, the septum and left ventricle (LV). RESULTS: In phantoms, MOLLI under-estimated while STEAM-SASHA over-estimated T1, on average by 3.0% and 7.0% respectively, although at typical 3T myocardial T1 (T1 > 1200 ms) STEAM-SASHA was more accurate. In volunteers, T1 was higher using STEAM-SASHA than MOLLI in the LV and septum (p = 0.03, p = 0.006, respectively), but lower in RV regions (p > 0.05). Inter-study, inter-observer and intra-observer coefficients of variation in all regions were < 15%. Blood suppression was excellent with STEAM-SASHA and noise floor effects were minimal. DISCUSSION: STEAM-SASHA provides accurate and reproducible T1 in the RV with excellent blood and fat suppression. STEAM-SASHA has potential to provide new insights into pathological changes in the RV in future studies.

Realistic numerical simulations of diffusion tensor cardiovascular magnetic resonance: The effects of perfusion and membrane permeability.

PURPOSE: To study the sensitivity of diffusion tensor cardiovascular magnetic resonance (DT-CMR) to microvascular perfusion and changes in cell permeability. METHODS: Monte Carlo (MC) random walk simulations in the myocardium have been performed to simulate self-diffusion of water molecules in histology-based media with varying extracellular volume fraction (ECV) and permeable membranes. The effect of microvascular perfusion on simulations of the DT-CMR signal has been incorporated by adding the contribution of particles traveling through an anisotropic capillary network to the diffusion signal. The simulations have been performed considering three pulse sequences with clinical gradient strengths: monopolar stimulated echo acquisition mode (STEAM), monopolar pulsed-gradient spin echo (PGSE), and second-order motion-compensated spin echo (MCSE). RESULTS: Reducing ECV intensifies the diffusion restriction and incorporating membrane permeability reduces the anisotropy of the diffusion tensor. Widening the intercapillary velocity distribution results in increased measured diffusion along the cardiomyocytes long axis when the capillary networks are anisotropic. Perfusion amplifies the mean diffusivity for STEAM while the opposite is observed for short diffusion encoding time sequences (PGSE and MCSE). CONCLUSION: The effect of perfusion on the measured diffusion tensor is reduced using an increased reference b-value. Our results pave the way for characterization of the response of DT-CMR to microstructural changes underlying cardiac pathology and highlight the higher sensitivity of STEAM to permeability and microvascular circulation due to its longer diffusion encoding time.

Automated segmentation of long and short axis DENSE cardiovascular magnetic resonance for myocardial strain analysis using spatio-temporal convolutional neural networks.

BACKGROUND: Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images. METHODS: 2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude data in short- and long-axis images. A dataset of 360 short-axis and 124 long-axis slices was used to train the networks, from a combination of healthy subjects and patients with various conditions (hypertrophic and dilated cardiomyopathy, myocardial infarction, myocarditis). Segmentation performance was evaluated using ground-truth manual labels, and a strain analysis using conventional methods was performed to assess strain agreement with manual segmentation. Additional validation was performed using an externally acquired dataset to compare the inter- and intra-scanner reproducibility with respect to conventional methods. RESULTS: Spatio-temporal models gave consistent segmentation performance throughout the cine sequence, while 2D architectures often failed to segment end-diastolic frames due to the limited blood-to-myocardium contrast. Our models achieved a DICE score of 0.83 ± 0.05 and a Hausdorff distance of 4.0 ± 1.1 mm for short-axis segmentation, and 0.82 ± 0.03 and 7.9 ± 3.9 mm respectively for long-axis segmentations. Strain measurements obtained from automatically estimated myocardial contours showed good to excellent agreement with manual pipelines, and remained within the limits of inter-user variability estimated in previous studies. CONCLUSION: Spatio-temporal deep learning shows increased robustness for the segmentation of cine DENSE images. It provides excellent agreement with manual segmentation for strain extraction. Deep learning will facilitate the analysis of DENSE data, bringing it one step closer to clinical routine.

Biophysical screening and characterisation in medicinal chemistry.

In the last two decades the use of biophysical assays and methods in medicinal chemistry has increased significantly, to meet the demands of the novel targets and modalities that drug discoverers are looking to tackle. The desire to obtain accurate affinities, kinetics, thermodynamics and structural data as early as possible in the drug discovery process has fuelled this innovation. This review introduces the principles underlying the techniques in common use and provides a perspective on the weaknesses and strengths of different methods. Case studies are used to further illustrate some of the applications in medicinal chemistry and a discussion of the emerging biophysical methods on the horizon is presented.

Development of a cardiovascular magnetic resonance-compatible large animal isolated heart model for direct comparison of beating and arrested hearts.

Cardiac motion results in image artefacts and quantification errors in many cardiovascular magnetic resonance (CMR) techniques, including microstructural assessment using diffusion tensor cardiovascular magnetic resonance (DT-CMR). Here, we develop a CMR-compatible isolated perfused porcine heart model that allows comparison of data obtained in beating and arrested states. Ten porcine hearts (8/10 for protocol optimisation) were harvested using a donor heart retrieval protocol and transported to the remote CMR facility. Langendorff perfusion in a 3D-printed chamber and perfusion circuit re-established contraction. Hearts were imaged using cine, parametric mapping and STEAM DT-CMR at cardiac phases with the minimum and maximum wall thickness. High potassium and lithium perfusates were then used to arrest the heart in a slack and contracted state, respectively. Imaging was repeated in both arrested states. After imaging, tissue was removed for subsequent histology in a location matched to the DT-CMR data using fiducial markers. Regular sustained contraction was successfully established in six out of 10 hearts, including the final five hearts. Imaging was performed in four hearts and one underwent the full protocol, including colocalised histology. The image quality was good and there was good agreement between DT-CMR data in equivalent beating and arrested states. Despite the use of autologous blood and dextran within the perfusate, T2 mapping results, DT-CMR measures and an increase in mass were consistent with development of myocardial oedema, resulting in failure to achieve a true diastolic-like state. A contiguous stack of 313 5-µm histological sections at and a 100-µm thick section showing cell morphology on 3D fluorescent confocal microscopy colocalised to DT-CMR data were obtained. A CMR-compatible isolated perfused beating heart setup for large animal hearts allows direct comparisons of beating and arrested heart data with subsequent colocalised histology, without the need for onsite preclinical facilities.

Validation of cardiac diffusion tensor imaging sequences: A multicentre test-retest phantom study.

Cardiac diffusion tensor imaging (DTI) is an emerging technique for the in vivo characterisation of myocardial microstructure, and there is a growing need for its validation and standardisation. We sought to establish the accuracy, precision, repeatability and reproducibility of state-of-the-art pulse sequences for cardiac DTI among 10 centres internationally. Phantoms comprising 0%-20% polyvinylpyrrolidone (PVP) were scanned with DTI using a product pulsed gradient spin echo (PGSE; N = 10 sites) sequence, and a custom motion-compensated spin echo (SE; N = 5) or stimulated echo acquisition mode (STEAM; N = 5) sequence suitable for cardiac DTI in vivo. A second identical scan was performed 1-9 days later, and the data were analysed centrally. The average mean diffusivities (MDs) in 0% PVP were (1.124, 1.130, 1.113) x 10-3  mm2 /s for PGSE, SE and STEAM, respectively, and accurate to within 1.5% of reference data from the literature. The coefficients of variation in MDs across sites were 2.6%, 3.1% and 2.1% for PGSE, SE and STEAM, respectively, and were similar to previous studies using only PGSE. Reproducibility in MD was excellent, with mean differences in PGSE, SE and STEAM of (0.3 ± 2.3, 0.24 ± 0.95, 0.52 ± 0.58) x 10-5  mm2 /s (mean ± 1.96 SD). We show that custom sequences for cardiac DTI provide accurate, precise, repeatable and reproducible measurements. Further work in anisotropic and/or deforming phantoms is warranted.

Accelerating Cardiac Diffusion Tensor Imaging With a U-Net Based Model: Toward Single Breath-Hold.

BACKGROUND: In vivo cardiac diffusion tensor imaging (cDTI) characterizes myocardial microstructure. Despite its potential clinical impact, considerable technical challenges exist due to the inherent low signal-to-noise ratio. PURPOSE: To reduce scan time toward one breath-hold by reconstructing diffusion tensors for in vivo cDTI with a fitting-free deep learning approach. STUDY TYPE: Retrospective. POPULATION: A total of 197 healthy controls, 547 cardiac patients. FIELD STRENGTH/SEQUENCE: A 3 T, diffusion-weighted stimulated echo acquisition mode single-shot echo-planar imaging sequence. ASSESSMENT: A U-Net was trained to reconstruct the diffusion tensor elements of the reference results from reduced datasets that could be acquired in 5, 3 or 1 breath-hold(s) (BH) per slice. Fractional anisotropy (FA), mean diffusivity (MD), helix angle (HA), and sheetlet angle (E2A) were calculated and compared to the same measures when using a conventional linear-least-square (LLS) tensor fit with the same reduced datasets. A conventional LLS tensor fit with all available data (12 ± 2.0 [mean ± sd] breath-holds) was used as the reference baseline. STATISTICAL TESTS: Wilcoxon signed rank/rank sum and Kruskal-Wallis tests. Statistical significance threshold was set at P = 0.05. Intersubject measures are quoted as median [interquartile range]. RESULTS: For global mean or median results, both the LLS and U-Net methods with reduced datasets present a bias for some of the results. For both LLS and U-Net, there is a small but significant difference from the reference results except for LLS: MD 5BH (P = 0.38) and MD 3BH (P = 0.09). When considering direct pixel-wise errors the U-Net model outperformed significantly the LLS tensor fit for reduced datasets that can be acquired in three or just one breath-hold for all parameters. DATA CONCLUSION: Diffusion tensor prediction with a trained U-Net is a promising approach to minimize the number of breath-holds needed in clinical cDTI studies. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

Reproducibility of global and segmental myocardial strain using cine DENSE at 3 T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with heart disease.

BACKGROUND: While multiple cardiovascular magnetic resonance (CMR) methods provide excellent reproducibility of global circumferential and global longitudinal strain, achieving highly reproducible segmental strain is more challenging. Previous single-center studies have demonstrated excellent reproducibility of displacement encoding with stimulated echoes (DENSE) segmental circumferential strain. The present study evaluated the reproducibility of DENSE for measurement of whole-slice or global circumferential (Ecc), longitudinal (Ell) and radial (Err) strain, torsion, and segmental Ecc at multiple centers. METHODS: Six centers participated and a total of 81 subjects were studied, including 60 healthy subjects and 21 patients with various types of heart disease. CMR utilized 3 T scanners, and cine DENSE images were acquired in three short-axis planes and in the four-chamber long-axis view. During one imaging session, each subject underwent two separate DENSE scans to assess inter-scan reproducibility. Each subject was taken out of the scanner and repositioned between the scans. Intra-user, inter-user-same-site, inter-user-different-site, and inter-user-Human-Deep-Learning (DL) comparisons assessed the reproducibility of different users analyzing the same data. Inter-scan comparisons assessed the reproducibility of DENSE from scan to scan. The reproducibility of whole-slice or global Ecc, Ell and Err, torsion, and segmental Ecc were quantified using Bland-Altman analysis, the coefficient of variation (CV), and the intraclass correlation coefficient (ICC). CV was considered excellent for CV ≤ 10%, good for 10% < CV ≤ 20%, fair for 20% < CV ≤ 40%, and poor for CV > 40. ICC values were considered excellent for ICC > 0.74, good for ICC 0.6 < ICC ≤ 0.74, fair for ICC 0.4 < ICC ≤ 0.59, poor for ICC < 0.4. RESULTS: Based on CV and ICC, segmental Ecc provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL reproducibility and good-excellent inter-scan reproducibility. Whole-slice Ecc and global Ell provided excellent intra-user, inter-user-same-site, inter-user-different-site, inter-user-Human-DL and inter-scan reproducibility. The reproducibility of torsion was good-excellent for all comparisons. For whole-slice Err, CV was in the fair-good range, and ICC was in the good-excellent range. CONCLUSIONS: Multicenter data show that 3 T CMR DENSE provides highly reproducible whole-slice and segmental Ecc, global Ell, and torsion measurements in healthy subjects and heart disease patients.

Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping.

BACKGROUND: Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis. METHODS: Convolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain. RESULTS: LV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87 ± 0.04, a Hausdorff distance of 2.7 ± 1.0 pixels, and a mean surface distance of 0.41 ± 0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38 ± 0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p < 0.05), respectively. Bland-Altman analyses showed biases of 0.00 ± 0.03 and limits of agreement of - 0.04 to 0.05 or better for deep learning-based fully-automatic global and segmental end-systolic circumferential strain vs. conventional user-assisted methods. CONCLUSIONS: Deep learning enables fully-automatic global and segmental circumferential strain analysis of DENSE CMR providing excellent agreement with conventional user-assisted methods. Deep learning-based automatic strain analysis may facilitate greater clinical use of DENSE for the quantification of global and segmental strain in patients with cardiac disease.

Automating in vivo cardiac diffusion tensor postprocessing with deep learning-based segmentation.

PURPOSE: In this work we develop and validate a fully automated postprocessing framework for in vivo diffusion tensor cardiac magnetic resonance (DT-CMR) data powered by deep learning. METHODS: A U-Net based convolutional neural network was developed and trained to segment the heart in short-axis DT-CMR images. This was used as the basis to automate and enhance several stages of the DT-CMR tensor calculation workflow, including image registration and removal of data corrupted with artifacts, and to segment the left ventricle. Previously collected and analyzed scans (348 healthy scans and 144 cardiomyopathy patient scans) were used to train and validate the U-Net. All data were acquired at 3 T with a STEAM-EPI sequence. The DT-CMR postprocessing and U-Net training/testing were performed with MATLAB and Python TensorFlow, respectively. RESULTS: The U-Net achieved a median Dice coefficient of 0.93 [0.92, 0.94] for the segmentation of the left-ventricular myocardial region. The image registration of diffusion images improved with the U-Net segmentation (P < .0001), and the identification of corrupted images achieved an F1 score of 0.70 when compared with an experienced user. Finally, the resulting tensor measures showed good agreement between an experienced user and the fully automated method. CONCLUSION: The trained U-Net successfully automated the DT-CMR postprocessing, supporting real-time results and reducing human workload. The automatic segmentation of the heart improved image registration, resulting in improvements of the calculated DT parameters.

Motion-Induced Signal Loss in In Vivo Cardiac Diffusion-Weighted Imaging.

LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:319-320.

Diffusion tensor cardiovascular magnetic resonance in hypertrophic cardiomyopathy: a comparison of motion-compensated spin echo and stimulated echo techniques.

OBJECTIVES: Diffusion tensor cardiovascular magnetic resonance (DT-CMR) interrogates myocardial microstructure. Two frequently used in vivo DT-CMR techniques are motion-compensated spin echo (M2-SE) and stimulated echo acquisition mode (STEAM). Whilst M2-SE is strain-insensitive and signal to noise ratio efficient, STEAM has a longer diffusion time and motion compensation is unnecessary. Here we compare STEAM and M2-SE DT-CMR in patients. MATERIALS AND METHODS: Biphasic DT-CMR using STEAM and M2-SE, late gadolinium imaging and pre/post gadolinium T1-mapping were performed in a mid-ventricular short-axis slice, in ten hypertrophic cardiomyopathy (HCM) patients at 3 T. RESULTS: Adequate quality data were obtained from all STEAM, but only 7/10 (systole) and 4/10 (diastole) M2-SE acquisitions. Compared with STEAM, M2-SE yielded higher systolic mean diffusivity (MD) (p = 0.02) and lower fractional anisotropy (FA) (p = 0.02, systole). Compared with segments with neither hypertrophy nor late gadolinium, segments with both had lower systolic FA using M2-SE (p = 0.02) and trend toward higher MD (p = 0.1). The negative correlation between FA and extracellular volume fraction was stronger with STEAM than M2-SE (r2 = 0.29, p < 0.001 STEAM vs. r2 = 0.10, p = 0.003 M2-SE). DISCUSSION: In HCM, only STEAM reliably assesses biphasic myocardial microstructure. Higher MD and lower FA from M2-SE reflect the shorter diffusion times. Further work will relate DT-CMR parameters and microstructural changes in disease.

Novel insights into in-vivo diffusion tensor cardiovascular magnetic resonance using computational modeling and a histology-based virtual microstructure.

PURPOSE: To develop histology-informed simulations of diffusion tensor cardiovascular magnetic resonance (DT-CMR) for typical in-vivo pulse sequences and determine their sensitivity to changes in extra-cellular space (ECS) and other microstructural parameters. METHODS: We synthesised the DT-CMR signal from Monte Carlo random walk simulations. The virtual tissue was based on porcine histology. The cells were thickened and then shrunk to modify ECS. We also created idealised geometries using cuboids in regular arrangement, matching the extra-cellular volume fraction (ECV) of 16-40%. The simulated voxel size was 2.8 × 2.8 × 8.0 mm3 for pulse sequences covering short and long diffusion times: Stejskal-Tanner pulsed-gradient spin echo, second-order motion-compensated spin echo, and stimulated echo acquisition mode (STEAM), with clinically available gradient strengths. RESULTS: The primary diffusion tensor eigenvalue increases linearly with ECV at a similar rate for all simulated geometries. Mean diffusivity (MD) varies linearly, too, but is higher for the substrates with more uniformly distributed ECS. Fractional anisotropy (FA) for the histology-based geometry is higher than the idealised geometry with low sensitivity to ECV, except for the long mixing time of the STEAM sequence. Varying the intra-cellular diffusivity (DIC ) results in large changes of MD and FA. Varying extra-cellular diffusivity or using stronger gradients has minor effects on FA. Uncertainties of the primary eigenvector orientation are reduced using STEAM. CONCLUSIONS: We found that the distribution of ECS has a measurable impact on DT-CMR parameters. The observed sensitivity of MD and FA to ECV and DIC has potentially interesting applications for interpreting in-vivo DT-CMR parameters.

High resolution in-vivo DT-CMR using an interleaved variable density spiral STEAM sequence.

PURPOSE: Diffusion tensor cardiovascular magnetic resonance (DT-CMR) has a limited spatial resolution. The purpose of this study was to demonstrate high-resolution DT-CMR using a segmented variable density spiral sequence with correction for motion, off-resonance, and T2*-related blurring. METHODS: A single-shot stimulated echo acquisition mode (STEAM) echo-planar-imaging (EPI) DT-CMR sequence at 2.8 × 2.8 × 8 mm3 and 1.8 × 1.8 × 8 mm3 was compared to a single-shot spiral at 2.8 × 2.8 × 8 mm3 and an interleaved spiral sequence at 1.8 × 1.8 × 8 mm3 resolution in 10 healthy volunteers at peak systole and diastasis. Motion-induced phase was corrected using the densely sampled central k-space data of the spirals. STEAM field maps and T2* measures were obtained using a pair of stimulated echoes each with a double spiral readout, the first used to correct the motion-induced phase of the second. RESULTS: The high-resolution spiral sequence produced similar DT-CMR results and quality measures to the standard-resolution sequence in both cardiac phases. Residual differences in fractional anisotropy and helix angle gradient between the resolutions could be attributed to spatial resolution and/or signal-to-noise ratio. Data quality increased after both motion-induced phase correction and off-resonance correction, and sharpness increased after T2* correction. The high-resolution EPI sequence failed to provide sufficient data quality for DT-CMR reconstruction. CONCLUSION: In this study, an in vivo DT-CMR acquisition at 1.8 × 1.8 mm2 in-plane resolution was demonstrated using a segmented spiral STEAM sequence. Motion-induced phase and off-resonance corrections are essential for high-resolution spiral DT-CMR. Segmented variable density spiral STEAM was found to be the optimal method for acquiring high-resolution DT-CMR data.

Diffusion tensor cardiovascular magnetic resonance with a spiral trajectory: An in vivo comparison of echo planar and spiral stimulated echo sequences.

PURPOSE: Diffusion tensor cardiovascular MR (DT-CMR) using stimulated echo acquisition mode (STEAM) with echo-planar-imaging (EPI) readouts is a low signal-to-noise-ratio (SNR) technique and therefore typically has a low spatial resolution. Spiral trajectories are more efficient than EPI, and could increase the SNR. The purpose of this study was to compare the performance of a novel STEAM spiral DT-CMR sequence with an equivalent established EPI technique. METHODS: A STEAM DT-CMR sequence was implemented with a spiral readout and a reduced field of view. An in vivo comparison of DT-CMR parameters and data quality between EPI and spiral was performed in 11 healthy volunteers imaged in peak systole and diastasis at 3 T. The SNR was compared in a phantom and in vivo. RESULTS: There was a greater than 49% increase in the SNR in vivo and in the phantom measurements (in vivo septum, systole: SNREPI = 8.0 ± 2.2, SNRspiral = 12.0 ± 2.7; diastasis: SNREPI = 8.1 ± 1.6, SNRspiral = 12.0 ± 3.7). There were no significant differences in helix angle gradient (HAG) (systole: HAGEPI = -0.79 ± 0.07 °/%; HAGspiral = -0.74 ± 0.16 °/%; P = 0.11; diastasis: HAGEPI = -0.63 ± 0.05 °/%; HAGspiral = -0.56 ± 0.14 °/%; P = 0.20), mean diffusivity (MD) in systole (MDEPI = 0.99 ± 0.06 × 10-3 mm2 /s, MDspiral = 1.00 ± 0.09 × 10-3 mm2 /s, P = 0.23) and secondary eigenvector angulation (E2A) (systole: E2AEPI = 61 ± 10 °; E2Aspiral = 63 ± 10 °; P = 0.77; diastasis: E2AEPI = 18 ± 11 °; E2Aspiral = 15 ± 8 °; P = 0.20) between the sequences. There was a small difference (≈ 20%) in fractional anisotropy (FA) (systole: FAEPI = 0.49 ± 0.03, FAspiral = 0.41 ± 0.04; P < 0.01; diastasis: FAEPI = 0.66 ± 0.05, FAspiral = 0.55 ± 0.03; P < 0.01) and mean diffusivity in diastasis (10%; MDEPI = 1.00 ± 0.12 × 10-3 mm2 /s, MDspiral = 1.10 ± 0.09 × 10-3 mm2 /s, P = 0.02). CONCLUSION: This is the first study to demonstrate DT-CMR STEAM using a spiral trajectory. The SNR was increased by using a spiral rather than the more established EPI readout, and the DT-CMR parameters were largely similar between the two sequences. Magn Reson Med 80:648-654, 2018. © 2017 International Society for Magnetic Resonance in Medicine.