Cardiac Diffusion: Technique and Practical Applications Nielles‐Vallespin, Sonia; Scott, Andrew; Ferreira, Pedro ...
Journal of magnetic resonance imaging,
August 2020, 2020-08-00, 20200801, Volume:
52, Issue:
2
Journal Article
Peer reviewed
The 3D microarchitecture of the cardiac muscle underlies the mechanical and electrical properties of the heart. Cardiomyocytes are arranged helically through the depth of the wall, and their ...shortening leads to macroscopic torsion, twist, and shortening during cardiac contraction. Furthermore, cardiomyocytes are organized in sheetlets separated by shear layers, which reorientate, slip, and shear during macroscopic left ventricle (LV) wall thickening. Cardiac diffusion provides a means for noninvasive interrogation of the 3D microarchitecture of the myocardium. The fundamental principle of MR diffusion is that an MRI signal is attenuated by the self‐diffusion of water in the presence of large diffusion‐encoding gradients. Since water molecules are constrained by the boundaries in biological tissue (cell membranes, collagen layers, etc.), depicting their diffusion behavior elucidates the shape of the myocardial microarchitecture they are embedded in. Cardiac diffusion therefore provides a noninvasive means to understand not only the dynamic changes in cardiac microstructure of healthy myocardium during cardiac contraction but also the pathophysiological changes in the presence of disease. This unique and innovative technology offers tremendous potential to enable improved clinical diagnosis through novel microstructural and functional assessment. in vivo cardiac diffusion methods are immediately translatable to patients, opening new avenues for diagnostic investigation and treatment evaluation in a range of clinically important cardiac pathologies. This review article describes the 3D microstructure of the LV, explains in vivo and ex vivo cardiac MR diffusion acquisition and postprocessing techniques, as well as clinical applications to date.
Level of Evidence: 1
Technical Efficacy: Stage 3
J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:348–368.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Purpose
Quantitative myocardial perfusion mapping has advantages over qualitative assessment, including the ability to detect global flow reduction. However, it is not clinically available and ...remains a research tool. Building upon the previously described imaging sequence, this study presents algorithm and implementation of an automated solution for inline perfusion flow mapping with step by step performance characterization.
Methods
Proposed workflow consists of motion correction (MOCO), arterial input function blood detection, intensity to gadolinium concentration conversion, and pixel‐wise mapping. A distributed kinetics model, blood‐tissue exchange model, is implemented, computing pixel‐wise maps of myocardial blood flow (mL/min/g), permeability‐surface‐area product (mL/min/g), blood volume (mL/g), and interstitial volume (mL/g).
Results
Thirty healthy subjects (11 men; 26.4 ± 10.4 years) were recruited and underwent adenosine stress perfusion cardiovascular MR. Mean MOCO quality score was 3.6 ± 0.4 for stress and 3.7 ± 0.4 for rest. Myocardial Dice similarity coefficients after MOCO were significantly improved (P < 1e‐6), 0.87 ± 0.05 for stress and 0.86 ± 0.06 for rest. Arterial input function peak gadolinium concentration was 4.4 ± 1.3 mmol/L at stress and 5.2 ± 1.5 mmol/L at rest. Mean myocardial blood flow at stress and rest were 2.82 ± 0.47 mL/min/g and 0.68 ± 0.16 mL/min/g, respectively. The permeability‐surface‐area product was 1.32 ± 0.26 mL/min/g at stress and 1.09 ± 0.21 mL/min/g at rest (P < 1e‐3). Blood volume was 12.0 ± 0.8 mL/100 g at stress and 9.7 ± 1.0 mL/100 g at rest (P < 1e‐9), indicating good adenosine vasodilation response. Interstitial volume was 20.8 ± 2.5 mL/100 g at stress and 20.3 ± 2.9 mL/100 g at rest (P = 0.50).
Conclusions
An inline perfusion flow mapping workflow is proposed and demonstrated on normal volunteers. Initial evaluation demonstrates this fully automated solution for the respiratory MOCO, arterial input function left ventricle mask detection, and pixel‐wise mapping, from free‐breathing myocardial perfusion imaging.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
To study the sensitivity of diffusion tensor cardiovascular magnetic resonance (DT-CMR) to microvascular perfusion and changes in cell permeability.
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).
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).
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.
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Abstract Background Cardiomyocytes are organized in microstructures termed sheetlets that reorientate during left ventricular thickening. Diffusion tensor cardiac magnetic resonance (DT-CMR) may ...enable noninvasive interrogation of in vivo cardiac microstructural dynamics. Dilated cardiomyopathy (DCM) is a condition of abnormal myocardium with unknown sheetlet function. Objectives This study sought to validate in vivo DT-CMR measures of cardiac microstructure against histology, characterize microstructural dynamics during left ventricular wall thickening, and apply the technique in hypertrophic cardiomyopathy (HCM) and DCM. Methods In vivo DT-CMR was acquired throughout the cardiac cycle in healthy swine, followed by in situ and ex vivo DT-CMR, then validated against histology. In vivo DT-CMR was performed in 19 control subjects, 19 DCM, and 13 HCM patients. Results In swine, a DT-CMR index of sheetlet reorientation (E2A) changed substantially (E2A mobility ∼46°). E2A changes correlated with wall thickness changes (in vivo r2 = 0.75; in situ r2 = 0.89), were consistently observed under all experimental conditions, and accorded closely with histological analyses in both relaxed and contracted states. The potential contribution of cyclical strain effects to in vivo E2A was ∼17%. In healthy human control subjects, E2A increased from diastole (18°) to systole (65°; p < 0.001; E2A mobility = 45°). HCM patients showed significantly greater E2A in diastole than control subjects did (48°; p < 0.001) with impaired E2A mobility (23°; p < 0.001). In DCM, E2A was similar to control subjects in diastole, but systolic values were markedly lower (40°; p < 0.001) with impaired E2A mobility (20°; p < 0.001). Conclusions Myocardial microstructure dynamics can be characterized by in vivo DT-CMR. Sheetlet function was abnormal in DCM with altered systolic conformation and reduced mobility, contrasting with HCM, which showed reduced mobility with altered diastolic conformation. These novel insights significantly improve understanding of contractile dysfunction at a level of noninvasive interrogation not previously available in humans.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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