In vivo diffusion MRI of the human heart using a 300 mT/m gradient system.

PURPOSE: This work reports for the first time on the implementation and application of cardiac diffusion-weighted MRI on a Connectom MR scanner with a maximum gradient strength of 300 mT/m. It evaluates the benefits of the increased gradient performance for the investigation of the myocardial microstructure. METHODS: Cardiac diffusion-weighted imaging (DWI) experiments were performed on 10 healthy volunteers using a spin-echo sequence with up to second- and third-order motion compensation ( M 2 $$ {M}_2 $$ and M 3 $$ {M}_3 $$ ) and b = 100 , 450 $$ b=100,450 $$ , and 1000 s / m m 2 $$ \mathrm{s}/\mathrm{m}{\mathrm{m}}^2 $$ (twice the b max $$ {b}_{\mathrm{max}} $$ commonly used on clinical scanners). Mean diffusivity (MD), fractional anisotropy (FA), helix angle (HA), and secondary eigenvector angle (E2A) were calculated for b = [100, 450] s / m m 2 $$ \mathrm{s}/\mathrm{m}{\mathrm{m}}^2 $$ and b = [100, 1000] s / m m 2 $$ \mathrm{s}/\mathrm{m}{\mathrm{m}}^2 $$ for both M 2 $$ {M}_2 $$ and M 3 $$ {M}_3 $$ . RESULTS: The MD values with M 3 $$ {M}_3 $$ are slightly higher than with M 2 $$ {M}_2 $$ with Δ MD = 0 . 05 ± 0 . 05 [ × 1 0 - 3 mm 2 / s ] ( p = 4 e - 5 ) $$ \Delta \mathrm{MD}=0.05\pm 0.05\kern0.3em \left[\times 1{0}^{-3}\kern0.3em {\mathrm{mm}}^2/\mathrm{s}\right]\kern0.3em \left(p=4e-5\right) $$ for b max = 450 s / mm 2 $$ {b}_{\mathrm{max}}=450\kern0.3em \mathrm{s}/{\mathrm{mm}}^2 $$ and Δ MD = 0 . 03 ± 0 . 03 [ × 1 0 - 3 mm 2 / s ] ( p = 4 e - 4 ) $$ \Delta \mathrm{MD}=0.03\pm 0.03\kern0.3em \left[\times \kern0.3em 1{0}^{-3}\kern0.3em {\mathrm{mm}}^2/\mathrm{s}\right]\kern0.3em \left(p=4e-4\right) $$ for b max = 1000 s / mm 2 $$ {b}_{\mathrm{max}}=1000\kern0.3em \mathrm{s}/{\mathrm{mm}}^2 $$ . A reduction in MD is observed by increasing the b max $$ {b}_{\mathrm{max}} $$ from 450 to 1000 s / mm 2 $$ \mathrm{s}/{\mathrm{mm}}^2 $$ ( Δ MD = 0 . 06 ± 0 . 04 [ × 1 0 - 3 mm 2 / s ] ( p = 1 . 6 e - 9 ) $$ \Delta \mathrm{MD}=0.06\pm 0.04\kern0.3em \left[\times \kern0.3em 1{0}^{-3}\kern0.3em {\mathrm{mm}}^2/\mathrm{s}\right]\kern0.3em \left(p=1.6e-9\right) $$ for M 2 $$ {M}_2 $$ and Δ MD = 0 . 08 ± 0 . 05 [ × 1 0 - 3 mm 2 / s ] ( p = 1 e - 9 ) $$ \Delta \mathrm{MD}=0.08\pm 0.05\kern0.3em \left[\times \kern0.3em 1{0}^{-3}\kern0.3em {\mathrm{mm}}^2/\mathrm{s}\right]\kern0.3em \left(p=1e-9\right) $$ for M 3 $$ {M}_3 $$ ). The difference between FA, E2A, and HA was not significant in different schemes ( p > 0 . 05 $$ p>0.05 $$ ). CONCLUSION: This work demonstrates cardiac DWI in vivo with higher b-value and higher order of motion compensated diffusion gradient waveforms than is commonly used. Increasing the motion compensation order from M 2 $$ {M}_2 $$ to M 3 $$ {M}_3 $$ and the maximum b-value from 450 to 1000 s / mm 2 $$ \mathrm{s}/{\mathrm{mm}}^2 $$ affected the MD values but FA and the angular metrics (HA and E2A) remained unchanged. Our work paves the way for cardiac DWI on the next-generation MR scanners with high-performance gradient systems.

Second-Order Motion-Compensated Echo-Planar Cardiac Diffusion-Weighted MRI: Usefulness of Compressed Sensitivity Encoding.

BACKGROUND: Cardiac diffusion-weighted imaging (DWI) using second-order motion-compensated spin echo (M2C) can provide noninvasive in-vivo microstructural assessment, but limited by relatively low signal-to-noise ratio (SNR). Echo-planar imaging (EPI) with compressed sensitivity encoding (EPICS) could address these issues. PURPOSE: To combine M2C DWI and EPCIS (M2C EPICS DWI), and compare image quality for M2C DWI. STUDY TYPE: Prospective. POPULATION: Ten ex-vivo hearts, 10 healthy volunteers (females, 5 [50%]; mean ± SD of age, 25 ± 4 years), and 12 patients with diseased hearts (female, 1 [8.3%]; mean ± SD of age, 44 ± 16 years; including coronary artery heart disease, congenital heart disease, dilated cardiomyopathy, amyloidosis, and myocarditis). FIELD STRENGTH/SEQUENCE: 3-T, M2C EPICS DWI, and M2C DWI. ASSESSMENT: The apparent SNR (aSNR) and the rating scores were used to evaluate and compared image quality of all three groups. The aSNR was calculated using aSNR = Mean intensity myocardium / Standard deviation myocardium $$ \mathrm{aSNR}={\mathrm{Mean}\ \mathrm{intensity}}_{\mathrm{myocardium}}/{\mathrm{Standard}\ \mathrm{deviation}}_{\mathrm{myocardium}} $$ , and the myocardium was segmented manually. Three observers independently rated subjective image quality using a 5-point Likert scale. STATISTICAL TESTS: Bland-Altman analysis and paired t-tests. The threshold for statistical significance was set at P < 0.05. RESULTS: In healthy volunteers, the aSNR with a b-value of 450 s/mm2 acquired by M2C EPICS DWI was significantly higher than M2C DWI at in-plane resolutions of 3.0 × 3.0, 2.5 × 2.5, and 2.0 × 2.0 mm2. In patients with diseased hearts, the aSNR ofM2C EPICS DWI was also significantly higher than that for M2C DWI (bias of M2C EPICS-M2C = 1.999, 95% limits of agreement, 0.362 to 3.636; mean ± SD, 7.80 ± 1.37 vs. 5.80 ± 0.81). The ADC values of M2C EPICS was significantly higher than M2C DWI in in-vivo hearts. Over 80% of the images with rating scores for M2C EPICS DWI were higher than M2C DWI in in-vivo hearts. DATA CONCLUSION: Cardiac imaging by M2C EPICS DWI may demonstrate better overall image quality and higher aSNR than M2C DWI. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Cardiac diffusion-weighted and tensor imaging: a Society for Cardiovascular Magnetic Resonance (SCMR) special interest group consensus statement.

Thanks to recent developments in Cardiovascular magnetic resonance (CMR), cardiac diffusion-weighted magnetic resonance is fast emerging in a range of clinical applications. Cardiac diffusion-weighted imaging (cDWI) and diffusion tensor imaging (cDTI) now enable investigators and clinicians to assess and quantify the 3D microstructure of the heart. Free-contrast DWI is uniquely sensitized to the presence and displacement of water molecules within the myocardial tissue, including the intra-cellular, extra-cellular and intra-vascular spaces. CMR can determine changes in microstructure by quantifying: a) mean diffusivity (MD) -measuring the magnitude of diffusion; b) fractional anisotropy (FA) - specifying the directionality of diffusion; c) helix angle (HA) and transverse angle (TA) -indicating the orientation of the cardiomyocytes; d) E2A and E2A mobility - measuring the alignment and systolic-diastolic mobility of the sheetlets, respectively. This document provides recommendations for both clinical and research cDWI and cDTI, based on published evidence when available and expert consensus when not. It introduces the cardiac microstructure focusing on the cardiomyocytes and their role in cardiac physiology and pathophysiology. It highlights methods, observations and recommendations in terminology, acquisition schemes, post-processing pipelines, data analysis and interpretation of the different biomarkers. Despite the ongoing challenges discussed in the document and the need for ongoing technical improvements, it is clear that cDTI is indeed feasible, can be accurately and reproducibly performed and, most importantly, can provide unique insights into myocardial pathophysiology.

Comparison of interpolation methods of predominant cardiomyocyte orientation from in vivo and ex vivo cardiac diffusion tensor imaging data.

Cardiac electrophysiology and cardiac mechanics both depend on the average cardiomyocyte long-axis orientation. In the realm of personalized medicine, knowledge of the patient-specific changes in cardiac microstructure plays a crucial role. Patient-specific computational modelling has emerged as a tool to better understand disease progression. In vivo cardiac diffusion tensor imaging (cDTI) is a vital tool to non-destructively measure the average cardiomyocyte long-axis orientation in the heart. However, cDTI suffers from long scan times, rendering volumetric, high-resolution acquisitions challenging. Consequently, interpolation techniques are needed to populate bio-mechanical models with patient-specific average cardiomyocyte long-axis orientations. In this work, we compare five interpolation techniques applied to in vivo and ex vivo porcine input data. We compare two tensor interpolation approaches, one rule-based approximation, and two data-driven, low-rank models. We demonstrate the advantage of tensor interpolation techniques, resulting in lower interpolation errors than do low-rank models and rule-based methods adapted to cDTI data. In an ex vivo comparison, we study the influence of three imaging parameters that can be traded off against acquisition time: in-plane resolution, signal to noise ratio, and number of acquired short-axis imaging slices.

Analysis and correction of off-resonance artifacts in echo-planar cardiac diffusion tensor imaging.

PURPOSE: Cardiac diffusion tensor imaging using EPI readout is prone to image distortions in the presence of field inhomogeneities. In this work, a framework to analyze and correct image distortions in cardiac diffusion tensor imaging is presented. METHODS: A multi-coil reconstruction framework was implemented to enable field map-based off-resonance correction. Numerical simulations were used to examine reconstruction performance for EPI phase-encode directions blip up-down and down-up for different degrees of off-resonance gradients and varying field map resolution. The impact of coil encoding was analyzed using the g-factor and normalized RMSE. Finally, the proposed method was tested on free-breathing in vivo cardiac diffusion tensor imaging data acquired in healthy subjects at 3 Tesla. RESULTS: Depending on the local field map gradient strength and polarity and the selected phase-encode direction, field inhomogeneities lead to either local spatial compression or stretching with standard image reconstruction. Although spatial compression results in loss of image resolution upon field map-based reconstruction, spatial stretching can be recovered once multiple receive coils are utilized. Multi-coil reconstruction was found to reduce the normalized RMSE from 34.3% to 8.1% for image compression, and 33.6% to 1.8% for image stretching, with resulting average g-factors 14.7 ± 2.9 and 1.2 ± 0.1, respectively. In vivo, multi-coil field map-based reconstruction yielded improved alignment of angle maps with anatomical cine data. CONCLUSION: Multi-coil, field map-based image reconstruction for echo-planar cardiac diffusion tensor imaging allows accurate image reconstruction provided that the phase-encode direction and polarity is chosen to principally align with the direction and polarity of the prominent gradients of field inhomogeneities.

Accelerating cardiac diffusion tensor imaging combining local low-rank and 3D TV constraint.

OBJECTIVE: Diffusion tensor magnetic resonance imaging (DT-MRI, or DTI) is a promising technique for invasively probing biological tissue structures. However, DTI is known to suffer from much longer acquisition time with respect to conventional MRI and the problem is worsened when dealing with in vivo acquisitions. Therefore, faster DTI for both ex vivo and in vivo scans is highly desired. MATERIALS AND METHODS: This paper proposes a new compressed sensing (CS) reconstruction method that employs local low-rank (LLR) model and three-dimensional (3D) total variation (TV) constraint to reconstruct cardiac diffusion-weighted (DW) images from highly undersampled k-space data. The LLR model takes the set of DW images corresponding to different diffusion gradient directions as a 3D image volume and decomposes the latter into overlapping 3D blocks. Then, the 3D blocks are stacked as two-dimensional (2D) matrix. Finally, low-rank property is applied to each block matrix and the 3D TV constraint to the 3D image volume. The underlying constrained optimization problem is finally solved using the first-order fast method. The proposed method is evaluated on real ex vivo cardiac DTI data as a prerequisite to in vivo cardiac DTI applications. RESULTS: The results on real human ex vivo cardiac DTI images demonstrate that the proposed method exhibits lower reconstruction errors for DTI indices, including fractional anisotropy (FA), mean diffusivities (MD), transverse angle (TA), and helix angle (HA), compared to existing CS-based DTI image reconstruction techniques. CONCLUSION: The proposed method provides better reconstruction quality and more accurate DTI indices in comparison with the state-of-the-art CS-based DW image reconstruction methods.

The Influence of Radial Undersampling Schemes on Compressed Sensing in Cardiac DTI.

Diffusion tensor imaging (DTI) is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Undersampling of k-space data provides an effective way to reduce the amount of data to acquire while maintaining image quality. Radial undersampling is one of the most popular non-Cartesian k-space sampling schemes, since it has relatively lower sensitivity to motion than Cartesian trajectories, and artifacts from linear reconstruction are more noise-like. Therefore, radial imaging is a promising strategy of undersampling to accelerate acquisitions. The purpose of this study is to investigate various radial sampling schemes as well as reconstructions using compressed sensing (CS). In particular, we propose two randomly perturbed radial undersampling schemes: golden-angle and random angle. The proposed methods are compared with existing radial undersampling methods, including uniformity-angle, randomly perturbed uniformity-angle, golden-angle, and random angle. The results on both simulated and real human cardiac diffusion weighted (DW) images show that, for the same amount of k-space data, randomly sampling around a random radial line results in better reconstruction quality for DTI indices, such as fractional anisotropy (FA), mean diffusivities (MD), and that the randomly perturbed golden-angle undersampling yields the best results for cardiac CS-DTI image reconstruction.

Bayesian intravoxel incoherent motion parameter mapping in the human heart.

BACKGROUND: Intravoxel incoherent motion (IVIM) imaging of diffusion and perfusion in the heart suffers from high parameter estimation error. The purpose of this work is to improve cardiac IVIM parameter mapping using Bayesian inference. METHODS: A second-order motion-compensated diffusion weighted spin-echo sequence with navigator-based slice tracking was implemented to collect cardiac IVIM data in early systole in eight healthy subjects on a clinical 1.5 T CMR system. IVIM data were encoded along six gradient optimized directions with b-values of 0-300 s/mm2. Subjects were scanned twice in two scan sessions one week apart to assess intra-subject reproducibility. Bayesian shrinkage prior (BSP) inference was implemented to determine IVIM parameters (diffusion D, perfusion fraction F and pseudo-diffusion D*). Results were compared to least-squares (LSQ) parameter estimation. Signal-to-noise ratio (SNR) requirements for a given fitting error were assessed for the two methods using simulated data. Reproducibility analysis of parameter estimation in-vivo using BSP and LSQ was performed. RESULTS: BSP resulted in reduced SNR requirements when compared to LSQ in simulations. In-vivo, BSP analysis yielded IVIM parameter maps with smaller intra-myocardial variability and higher estimation certainty relative to LSQ. Mean IVIM parameter estimates in eight healthy subjects were (LSQ/BSP): 1.63 ± 0.28/1.51 ± 0.14·10-3 mm2/s for D, 13.13 ± 19.81/13.11 ± 5.95% for F and 201.45 ± 313.23/13.11 ± 14.53·10-3 mm2/s for D ∗. Parameter variation across all volunteers and measurements was lower with BSP compared to LSQ (coefficient of variation BSP vs. LSQ: 9% vs. 17% for D, 45% vs. 151% for F and 111% vs. 155% for D ∗). In addition, reproducibility of the IVIM parameter estimates was higher with BSP compared to LSQ (Bland-Altman coefficients of repeatability BSP vs. LSQ: 0.21 vs. 0.26·10-3 mm2/s for D, 5.55 vs. 6.91% for F and 15.06 vs. 422.80·10-3 mm2/s for D*). CONCLUSION: Robust free-breathing cardiac IVIM data acquisition in early systole is possible with the proposed method. BSP analysis yields improved IVIM parameter maps relative to conventional LSQ fitting with fewer outliers, improved estimation certainty and higher reproducibility. IVIM parameter mapping holds promise for myocardial perfusion measurements without the need for contrast agents.

Spin echo versus stimulated echo diffusion tensor imaging of the in vivo human heart.

PURPOSE: To compare signal-to-noise ratio (SNR) efficiency and diffusion tensor metrics of cardiac diffusion tensor mapping using acceleration-compensated spin-echo (SE) and stimulated echo acquisition mode (STEAM) imaging. METHODS: Diffusion weighted SE and STEAM sequences were implemented on a clinical 1.5 Tesla MR system. The SNR efficiency of SE and STEAM was measured (b = 50-450 s/mm(2) ) in isotropic agar, anisotropic diffusion phantoms and the in vivo human heart. Diffusion tensor analysis was performed on mean diffusivity, fractional anisotropy, helix and transverse angles. RESULTS: In the isotropic phantom, the ratio of SNR efficiency for SE versus STEAM, SNRt (SE/STEAM), was 2.84 ± 0.08 for all tested b-values. In the anisotropic diffusion phantom the ratio decreased from 2.75 ± 0.05 to 2.20 ± 0.13 with increasing b-value, similar to the in vivo decrease from 2.91 ± 0.43 to 2.30 ± 0.30. Diffusion tensor analysis revealed reduced deviation of helix angles from a linear transmural model and reduced transverse angle standard deviation for SE compared with STEAM. Mean diffusivity and fractional anisotropy were measured to be statistically different (P < 0.001) between SE and STEAM. CONCLUSION: Cardiac DTI using motion-compensated SE yields a 2.3-2.9× increase in SNR efficiency relative to STEAM and improved accuracy of tensor metrics. The SE method hence presents an attractive alternative to STEAM based approaches. Magn Reson Med 76:862-872, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Optimal diffusion weighting for in vivo cardiac diffusion tensor imaging.

PURPOSE: To investigate the influence of the diffusion weighting on in vivo cardiac diffusion tensor imaging (cDTI) and obtain optimal parameters. METHODS: Ten subjects were scanned using stimulated echo acquisition mode echo planar imaging with six b-values, from 50 to 950 s·mm(-2) , plus b = 15 s·mm(-2) reference. The relationship between b-value and both signal loss and signal-to-noise ratio measures was investigated. Mean diffusivity, fractional anisotropy, and helical angle maps were calculated using all possible b-value pairs to investigate the effects of diffusion weighting on the main and reference data. RESULTS: Signal decay at low b-values was dominated by processes with high apparent diffusion coefficients, most likely microvascular perfusion. This effect could be avoided by diffusion weighting of the reference images. Parameter maps were improved with increased b-value until the diffusion-weighted signal approached the noise floor. For the protocol used in this study, b = 750 s·mm(-2) combined with 150 s·mm(-2) diffusion weighting of the reference images proved optimal. CONCLUSION: Mean diffusivity, fractional anisotropy, and helical angle from cDTI are influenced by the b-value of the main and reference data. Using optimal values improves parameter maps and avoids microvascular perfusion effects. This optimized protocol should provide greater sensitivity to pathological changes in parameter maps.

Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study.

In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) of × 2 and × 4 , with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AF × 2 or most DT parameters at AF × 4 , and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF × 2 and AF × 4 . However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF × 8 , the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.

In vivo diffusion tensor MRI of the human heart: reproducibility of breath-hold and navigator-based approaches.

The aim of this study was to implement a quantitative in vivo cardiac diffusion tensor imaging (DTI) technique that was robust, reproducible, and feasible to perform in patients with cardiovascular disease. A stimulated-echo single-shot echo-planar imaging (EPI) sequence with zonal excitation and parallel imaging was implemented, together with a novel modification of the prospective navigator (NAV) technique combined with a biofeedback mechanism. Ten volunteers were scanned on two different days, each time with both multiple breath-hold (MBH) and NAV multislice protocols. Fractional anisotropy (FA), mean diffusivity (MD), and helix angle (HA) fiber maps were created. Comparison of initial and repeat scans showed good reproducibility for both MBH and NAV techniques for FA (P > 0.22), MD (P > 0.15), and HA (P > 0.28). Comparison of MBH and NAV FA (FAMBHday1 = 0.60 ± 0.04, FANAVday1 = 0.60 ± 0.03, P = 0.57) and MD (MDMBHday1 = 0.8 ± 0.2 × 10(-3) mm(2) /s, MDNAVday1 = 0.9 ± 0.2 × 10(-3) mm(2) /s, P = 0.07) values showed no significant differences, while HA values (HAMBHday1Endo = 22 ± 10°, HAMBHday1Mid-Endo = 20 ± 6°, HAMBHday1Mid-Epi = -1 ± 6°, HAMBHday1Epi = -17 ± 6°, HANAVday1Endo = 7 ± 7°, HANAVday1Mid-Endo = 13 ± 8°, HANAVday1Mid-Epi = -2 ± 7°, HANAVday1Epi = -14 ± 6°) were significantly different. The scan duration was 20% longer with the NAV approach. Currently, the MBH approach is the more robust in normal volunteers. While the NAV technique still requires resolution of some bulk motion sensitivity issues, these preliminary experiments show its potential for in vivo clinical cardiac diffusion tensor imaging and for delivering high-resolution in vivo 3D DTI tractography of the heart.

Accelerated cardiac diffusion tensor imaging using deep neural network.

Cardiac diffusion tensor imaging (DTI) is a noninvasive method for measuring the microstructure of the myocardium. However, its long scan time significantly hinders its wide application. In this study, we developed a deep learning framework to obtain high-quality DTI parameter maps from six diffusion-weighted images (DWIs) by combining deep-learning-based image generation and tensor fitting, and named the new framework FG-Net. In contrast to frameworks explored in previous deep-learning-based fast DTI studies, FG-Net generates inter-directional DWIs from six input DWIs to supplement the loss information and improve estimation accuracy for DTI parameters. FG-Net was evaluated using two datasets ofex vivohuman hearts. The results showed that FG-Net can generate fractional anisotropy, mean diffusivity maps, and helix angle maps from only six raw DWIs, with a quantification error of less than 5%. FG-Net outperformed conventional tensor fitting and black-box network fitting in both qualitative and quantitative metrics. We also demonstrated that the proposed FG-Net can achieve highly accurate fractional anisotropy and helix angle maps in DWIs with differentb-values.

Self-supervised structural similarity-based convolutional neural network for cardiac diffusion tensor image denoising.

BACKGROUND: Diffusion tensor imaging (DTI) is a promising technique for non-invasively investigating the myocardial fiber structures of human heart. However, low signal-to-noise ratio (SNR) has been a major limit of cardiac DTI to prevent us from detecting myocardium structure accurately. Therefore, it is important to remove the effect of noise on diffusion weighted (DW) images. PURPOSE: Although the conventional and deep learning-based denoising methods have shown the potential to deal with effectively the noise in DW images, most of them are redundant information dependent or require the noise-free images as golden standard. In addition, the existed DW image denoising methods often suffer from problems of over-smoothing. To address these issues, we propose a self-supervised learning model, structural similarity based convolutional neural network with edge-weighted loss (SSECNN), to remove the noise effectively in cardiac DTI. METHODS: Considering that the DW images acquired along different diffusion directions have structural similarity, and the noise in these DW images is independent and identically distributed, the structural similarity-based matching algorithm is proposed to search for the most similar DW images. Such similar noisy DW image pairs are then used as the input and target of the denoising network SSECNN, which consists of several convolutional and residual blocks. Through the self-supervised training with these image pairs, the network can restore the clean DW images and retain the correlations between the denoised DW images along different directions. To avoid the over-smoothing problem, we design a novel edge-weighted loss which enables the network to adaptively adjust the loss weights with iterations and therefore to improve the detail preserve ability of the model. To verify the superiority of the proposed method, comparisons with state-of-the-art (SOTA) denoising methods are performed on both synthetic and real acquired DTI datasets. RESULTS: Experimental results show that SSECNN can effectively reduce the noise in the DW images while preserving detailed texture and edge information and therefore achieve better performance in DTI reconstruction. For synthetic dataset, compared to the SOTA method, the root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structure similarity index measure (SSIM) between the denoised DW images obtained with SSECNN and noise-free DW images are improved by 6.94%, 1.98%, and 0.76% respectively when the noise level is 10%. As for the acquired cardiac DTI dataset, the SSECNN method could significantly improve SNR and contrast to noise ratio (CNR) of cardiac DW images and achieve more regular helix angle (HA) and transverse angle (TA) maps. The ablation experimental results validate that using the structure similarity-based method to search the similar DW image pairs yield the smallest loss, and with the help of the edge-weighted loss, the denoised DW images and diffusion metric maps can preserve more details. CONCLUSIONS: The proposed SSECNN method can fully explore the similarity between the DW images along different diffusion directions. Using such similarity and an edge-weighted loss enable us to denoise cardiac DTI effectively in a self-supervised manner. Our method can overcome the redundancy information dependence and over-smoothing problem of the SOTA methods.

Diffusion-tensor magnetic resonance imaging of the human heart in health and in acute myocardial infarction using diffusion-weighted echo-planar imaging technique with spin-echo signals.

Introduction: Originally thought unsuitable due to proneness to myocardial motion and susceptibility artefacts, spin-echo echo planar imaging (SE-EPI) has gained attention for the cardiac diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) offering higher SNR and lower achievable echo time (TE). Aim: The application of DTI for patients with acute myocardial infarction (AMI) using our methodology developed on the basis of the SE-EPI sequence. Material and methods: Twelve patients with AMI and six healthy controls were enrolled in the preliminary DTI study within the CIRCULATE STRATEGMED 2 project. Our method relied on a pilot ECG-triggered DTI examination, based on which the initial evaluation was possible and allowed proper manipulation of TE (64/47 ms for patients/control), repetition time (TR) and ECG trigger delay in the consecutive DTI. Results: The study demonstrated that by using our algorithm it was possible to obtain DWI images showing infarct zones identified on T1-weighted images with late gadolinium-enhancement (LGE) with division into subtle and severe damage. Quantitative DTI showed increased mean diffusivity (MD) and decreased fractional anisotropy (FA) in the infarct compared to remote tissue. The application of B-matrix spatial distribution (BSD) calibration allowed the improvement of FA. Conclusions: Our algorithm is suitable for qualitative assessment of infarction zones with different severity. The analysis of the quantitative DTI showed that despite the lack of motion compensation blocks in the applied SE-EPI sequence, it was possible to approach the diffusion tensor parameter values reported for the myocardium.

Diffusion-weighted imaging and diffusion tensor imaging of the heart in vivo: major developments.

Diffusion-weighted magnetic resonance imaging (DWI) is a powerful diagnostic tool. Contrast in DWI images is dictated by the differences in diffusion of water in tissues, which depends on the tissue type, hydration and fluid composition. Therefore DWI can differentiate between hard and soft tissues, as well as visualize their condition, such as edema, necrosis or fibrosis. Diffusion tensor imaging (DTI) is a DWI technique which additionally delivers information about the microstructure. In cardiovascular applications DWI/DTI can non-invasively characterize the acute to chronic phase of the area at risk and microstructural dynamics without the need to use contrast agents. However, cardiac DWI/DTI differs from other applications due to serious anatomic and technologic challenges. Over the years, scientists have stepped up overcoming more and more advanced obstacles associated with complex 3D myocardial motions, breathing, blood flow and perfusion. The aim of this article is to review milestone technologic advances in DWI/DTI of the heart in vivo. The discussed development begins with the adjustment of the diffusion imaging block to the electrocardiogram-based most quiescent phase, next considers different pulse sequence designs for first-, second- and higher-order motion compensation and SNR improvement, and ends up with prospects for further developments. Reviewed papers show great progress in this research area, but the gap between the scientific development and common clinical practice is tremendous. Cardiac DWI/DTI has promising clinical relevance and its addition to routine imaging techniques of patients with heart disease may empower clinical diagnosis.

Evaluation of Right Ventricular Function and Myocardial Microstructure in Fetal Hypoplastic Left Heart Syndrome.

Right ventricular (RV) function is one of the critical factors affecting the prognosis of fetuses with hypoplastic left heart syndrome (HLHS). Our study objectives included assessment of cardiac function and comprehensive measurement of cardiac microstructure. We retrospectively studied 42 fetuses diagnosed as HLHS by echocardiography. Myocardial deformation of the right ventricular wall was calculated automatically in offline software. Postmortem cardiac imaging for three control fetal hearts and four HLHS specimens was performed by a 9.4T DTI scanner. Myocardial deformation parameters of the RV (including strain, strain rate, and velocity) were significantly lower in HLHS fetuses (all p < 0.01). FA values increased (0.18 ± 0.01 vs. 0.21 ± 0.02; p < 0.01) in HLHS fetuses, but MD reduced (1.3 ± 0.15 vs. 0.88 ± 0.13; p < 0.001). The HLHS fetuses' RV lateral base wall (−7.31 ± 51.91 vs. −6.85 ± 31.34; p = 0.25), middle wall (1.71 ± 50.92 vs. −9.38 ± 28.18; p < 0.001), and apical wall (−6.19 ± 46.61 vs. −11.16 ± 29.86, p < 0.001) had HA gradient ascent but HA gradient descent in the anteroseptal wall (p < 0.001) and inferoseptal wall (p < 0.001). RV basal lateral wall HA degrees were correlated with RVGLS (R2 = 0.97, p = 0.02). MD values were positively correlated with RVGLS (R2 = 0.93, p = 0.04). Our study found morphological and functional changes of the RV in HLHS fetuses, and cardiac function was related to the orientation patterns of myocardial fibers. It may provide insight into understanding the underlying mechanisms of impaired RV performance in HLHS.

Diffusion biomarkers in chronic myocardial infarction.

Cardiac diffusion tensor magnetic resonance imaging (cDTI) allows estimating the aggregate cardiomyocyte architecture in healthy subjects and its remodeling as a result of cardiac disease. In this study, cDTI was used to quantify microstructural changes occurring in swine (N=7) six to ten weeks after myocardial infarction. Each heart was extracted and imaged ex vivo with 1mm isotropic spatial resolution. Microstructural changes were quantified in the border zone and infarct region by comparing diffusion tensor invariants - fractional anisotropy (FA), mode, and mean diffusivity (MD) - radial diffusivity, and diffusion tensor eigenvalues with the corresponding values in the remote myocardium. MD and radial diffusivity increased in the infarct and border regions with respect to the remote myocardium (p<0.01). In contrast, FA and mode decreased in the infarct and border regions (p<0.01). Diffusion tensor eigenvalues also increased in the infarct and border regions, with a larger increase in the secondary and tertiary eigenvalues.

TIME RESOLVED DISPLACEMENT-BASED REGISTRATION OF IN VIVO CDTI CARDIOMYOCYTE ORIENTATIONS.

In vivo cardiac microstructure acquired using cardiac diffusion tensor imaging (cDTI) is a critical component of patient-specific models of cardiac electrophysiology and mechanics. In order to limit bulk motion artifacts and acquisition time, cDTI microstructural data is acquired at a single cardiac phase necessitating registration to the reference configuration on which the patient-specific computational models are based. Herein, we propose a method to register subject-specific microstructural data to an arbitrary cardiac phase using measured cardiac displacements. We validate our approach using a subject-specific computational phantom based on data from human subjects. Compared to a geometry-based non-rigid registration method, the displacement-based registration leads to improved accuracy (less than 1° versus 10° average median error in cardiomyocyte angular differences) and tighter confidence interval (3° versus 65° average upper limit of the 95% confidence interval).