Intracardiac flow patterns are shaped by the coupled motion of the cardiac chambers and heart valves and provide important information about cardiac function. However, clinical flow imaging remains limited by exam times, noise, resolution, and incomplete details of the three-dimensional flow. Computational fluid dynamics (CFD) can potentially provide detailed flow quantification and predictive insight into treatment outcomes, but clinical translation requires frameworks that reproduce patient-specific measurements while balancing physiological realism, computational cost, and modeling effort. Herein, we present an image-based, patient-specific computational framework for simulating whole-heart intracardiac hemodynamics that balances physiological fidelity with computational efficiency. The framework first employs machine learning-based segmentation and mesh propagation to reconstruct moving cardiac anatomies from time-resolved images. CFD simulations are then performed to resolve blood flow in deforming domains, while resistive immersed surfaces (RIS) are used to model all four cardiac valves with physiologically realistic opening and closing dynamics. The framework was applied to model hemodynamics in a healthy adult and a pediatric patient with complex congenital heart disease (CHD). In the healthy case, the simulations reproduced physiologic pressure-volume behavior, valve timing, and ventricular vortex formation. In the CHD case, simulated chamber and vessel pressures showed agreement with cardiac catheterization measurements. Simulated flow fields were qualitatively consistent with 4D-Flow MRI, while providing higher-resolution visualization of flow structures that were partially obscured by imaging artifacts. Comparison between the healthy and CHD cases further revealed altered diastolic flow organization and elevated normalized viscous dissipation in the CHD heart.

Purpose:Accurate assessment of the pressure gradient ( Δ P ) across aortic coarctation (CoA) is critical for determining disease severity and the need for intervention. Current non-invasive methods are unreliable, while invasive catheterization remains the clinical gold standard. This study evaluates a novel MRI acquisition strategy, 4D-FlowP, that simultaneously encodes blood velocity and acceleration to enable reliable non-invasive pressure gradient mapping in CoA. Methods:Patient-specific compliant aortic phantoms were created from clinical MRI data of two patients with CoA. Additional geometries were synthetically generated by increasing stenosis severity. Phantoms were studied in an MRI-compatible flow loop under physiologically realistic flow and pressure conditions. Pressure gradients were estimated using conventional 4D-Flow MRI, 4D-FlowP, and fluid-structure interaction (FSI) simulations. Results were compared against ground-truth catheter-based measurements across multiple flow rates and stenosis severities. Results:Conventional 4D-Flow consistently underestimated Δ P (slope = 0.63, R 2 = 0.75 ) relative to catheter measurements. In contrast, 4D-FlowP demonstrated substantially improved agreement (slope = 0.95, R 2 = 0.75 ). FSI simulations showed the highest overall agreement with catheter-derived Δ P (slope = 1.14, R 2 = 0.82 ). Scan times for 4D-FlowP were comparable to 4D-Flow (26 vs. 24 minutes). Conclusion:4D-FlowP enables a more accurate MRI-based pressure gradient mapping in CoA than conventional 4D-Flow, when compared to ground truth catheter measurements. These findings support further in vivo evaluation of 4D-FlowP as a non-invasive alternative for functional assessment of CoA severity.

Background/Objectives: Duchenne Muscular Dystrophy (DMD) is a prevalent fatal genetic disorder, and heart failure is the leading cause of mortality. Peak left ventricular (LV) circumferential strain (Ecc), twist, and circumferential-longitudinal shear angle (θCL) are promising biomarkers for the improved and early diagnosis of incipient heart failure. Our goals were as follows: 1) to characterize a spectrum of functional and rotational LV biomarkers in boys with DMD compared with healthy age-matched controls; and 2) to identify LV biomarkers of early cardiomyopathy in the absence of abnormal LVEF or LGE. Methods: Boys with DMD (N = 43) and age-matched healthy volunteers (N = 16) were prospectively enrolled and underwent a 3T CMR exam after obtaining informed consent. Breath-held MRI tagging was used to estimate left ventricular Ecc at the mid-ventricular level as well as the twist, torsion, and θCL between basal and apical LV short-axis slices. A two-tailed t-test with unequal variance was used to test group-wise differences. Multiple comparisons were performed with Holm–Sidak post hoc correction. Multiple-regression analysis was used to test for correlations among biomarkers. A binomial logistic regression model assessed each biomarker’s ability to distinguish the following: (1) healthy volunteers vs. DMD patients, (2) healthy volunteers vs. LGE(−) DMD patients, and (3) LGE(−) DMD patients vs. LGE(+) DMD patients. Results: There was a significant impairment in the peak mid-wall Ecc [−17.0 ± 4.2% vs. −19.5 ± 1.9%, p < 7.8 × 10−3], peak LV twist (10.4 ± 4.3° vs. 15.6 ± 3.1°, p < 8.1 × 10−4), and peak LV torsion (2.03 ± 0.82°/mm vs. 2.8 ± 0.5°/mm, p < 2.6 × 10−3) of LGE(−) DMD patients when compared to healthy volunteers. There was a further significant reduction in the Ecc, twist, torsion, and θCL for LGE(+) DMD patients when compared to LGE(−) DMD patients. In the LGE(+) DMD patients, age significantly correlated with LVEF (r2 = 0.42, p = 9 × 10−3), peak mid-wall Ecc (r2 = 0.27, p = 0.046), peak LV Twist (r2 = 0.24, p = 0.06), peak LV torsion (r2 = 0.28, p = 0.04), and peak LV θCL (r2 = 0.23, p = 0.07). In the LGE(−) DMD patients, only the peak mid-wall Ecc was significantly correlated with age (r2 = 0.25, p = 0.006). The peak LV twist outperformed the peak mid-wall LV Ecc and EF in distinguishing DMD patients from healthy volunteer groups (AUC = 0.88, 0.80, and 0.72), as well as in distinguishing LGE(−) DMD patients from healthy volunteers (AUC = 0.83, 0.74, and 0.62). The peak LV twist and peak mid-wall LV Ecc performed similarly in distinguishing the LGE(−) and LGE(+) DMD cohorts (AUC = 0.74, 0.77, and 0.79). Conclusions: The peak mid-wall LV Ecc, peak LV twist, peak LV torsion, and peak LV θCL were significantly impaired in advance of the decreased LVEF and the development of focal myocardial fibrosis in boys with DMD and therefore were apparent prior to significant irreversible injury.

BACKGROUND:Myocardial strain is a valuable biomarker for diagnosing and predicting cardiac conditions, offering additional prognostic information to traditional metrics such as ejection fraction. While cardiovascular magnetic resonance (CMR) methods, particularly cine displacement encoding with stimulated echoes (DENSE), are the gold standard for strain estimation, evaluation of regional strain estimation requires precise ground truth. This study introduces DENSE-SIM, an open-source simulation pipeline for generating realistic cine DENSE images with high-resolution known ground-truth strain, enabling evaluation of accuracy and precision in strain analysis pipelines. METHODS:This pipeline is a modular tool designed for simulating cine DENSE images and evaluating strain estimation performance. It comprises four main modules: 1) anatomy generation, for creating end-diastolic cardiac shapes; 2) motion generation, to produce myocardial deformations over time and Lagrangian strain; 3) DENSE image generation, using Bloch equation simulations with realistic noise, spiral sampling, and phase cycling; and 4) strain evaluation. To illustrate the pipeline, a synthetic dataset of 180 short-axis slices was created and analyzed using the commonly used DENSEanalysis tool. The impact of the spatial regularization parameter (k) in DENSEanalysis was evaluated against the ground-truth pixel strain, to particularly assess the resulting bias and variance characteristics. RESULTS:Simulated strain profiles were generated with a myocardial signal-to-noise ratio (SNR) ranging from 3.9 to 17.7. For end-systolic radial strain, DENSEanalysis average signed error (ASE) in Green strain ranged from 0.04 ± 0.09 (true-calculated, mean ± std) for a typical regularization (k = 0.9), to -0.01 ± 0.21 at low regularization (k = 0.1). Circumferential strain ASE ranged from -0.00 ± 0.04 at k = 0.9 to -0.01 ± 0.10 at k = 0.1. This demonstrates that the circumferential strain closely matched the ground truth, while radial strain displayed more significant underestimations, particularly near the endocardium. A lower regularization parameter from 0.3 to 0.6 depending on the myocardial SNR would be more appropriate to estimate the radial strain, as a compromise between noise compensation and global strain accuracy. CONCLUSION:Generating realistic cine DENSE images with high-resolution ground-truth strain and myocardial segmentation enables accurate evaluation of strain analysis tools, while reproducing key in-vivo acquisition features, and will facilitate the future development of deep-learning models for myocardial strain analysis, enhancing clinical CMR workflows.

In recent years, machine learning (ML) based reconstruction has been widely investigated and employed in cardiac magnetic resonance (CMR) imaging. ML-based reconstructions can deliver clinically acceptable image quality under substantially accelerated scans. ML-based reconstruction, however, also requires substantial data and computational time to train the neural network, which is often optimized for a fixed acceleration rate or image contrast. In practice, imaging parameters are often tuned to best suit the diagnosis, which may differ from the training data. This can result in degraded image quality, and multiple trained networks are needed to fulfill the clinical demands. In this study, we propose a foundation model that uses adaptive unrolling, channel-shifting, and Pattern and Contrast-Prompt-UNet (PCP-UNet) to tackle the problem. In particular, the undersampled data goes through a different number of unrolled iterations according to its acceleration rate. Channel-shifting improves reconstructed data quality. The PCP-UNet is equipped with an image contrast and sampling pattern prompt. In vivo CMR experiments were performed using mixed combinations of image contrasts, acceleration rates, and (under)sampling patterns. The proposed foundation model has significantly improved image quality for a wide range of CMR protocols and outperforms the conventional ML-based method.

Cardiac diffusion tensor imaging (cDTI) is susceptible to image distortion while using an echo planar imaging (EPI) readouts. FSL TOPUP and TORTOISE DR-BUDDI are image distortion correction techniques that have been implemented to correct EPI distortion in neurological applications. We sought to establish which EPI-based distortion correction technique is most suitable for cDTI. Using a free-breathing second-order moment-compensated spin-echo technique, cDTI was acquired in healthy volunteers (N = 10) using both blip-up (BU) and blip-down (BD) EPI readouts. These datasets were then distortion corrected using the TOPUP and DR-BUDDI software packages. BU, BD, TOPUP, and DR-BUDDI images were then characterized by (1) geometric fidelity using the Dice Similarity coefficient (DSC), epicardial average Hausdorff distance (AHD epi $$ {}_{epi} $$ ), and endocardial average Hausdorff distance (AHD endo $$ {}_{endo} $$ ); (2) quantitative parametric maps: mean diffusivity (MD), fractional anisotropy (FA), and helix angle pitch (HAP); and (3) 95% confidence intervals in uncertainty measurements for MD ( dMD $$ dMD $$ ), FA ( dFA $$ dFA $$ ), and the primary eigenvector ( d e → 1 $$ d{\overrightarrow{e}}_1 $$ ). Distortion correction displayed improved geometric fidelity for both median DSC (BU = 0.77, BD = 0.81, DR-BUDDI = 0.85, and TOPUP = 0.86) and median AHD epi $$ {}_{epi} $$ (BU = 2.19 mm, BD = 1.94 mm, DR-BUDDI = 1.46 mm, and TOPUP = 1.33 mm). Decreases in median FA were observed for distortion correction data (BU = 0.30, BD = 0.28, DR-BUDDI = 0.27, and TOPUP = 0.26), while median MD fell between conventional BU and BD encoding (BU = 1.58 μm2/ms, BD = 1.70 μm2/ms, DR-BUDDI = 1.65 μm2/ms, and TOPUP = 1.64 μm2/ms). There was little to no change in median HAP between distorted and distortion-corrected data (BU = -0.41, BD = -0.40, DR-BUDDI = -0.41, and TOPUP = -0.36). Distortion correction increased the median for dMD $$ dMD $$ (BU = 0.32, BD = 0.32, DR-BUDDI = 0.36, and TOPUP = 0.41), dFA $$ dFA $$ (BU = 0.18, BD = 0.17, DR-BUDDI = 0.24, and TOPUP = 0.23), and d e → 1 $$ d{\overrightarrow{e}}_1 $$ (BU = 22°, BD = 22°, DR-BUDDI = 44°, and TOPUP = 41°). EPI distortion correction techniques improved geometrical characterization (DSC and AHD epi $$ {AHD}_{epi} $$ ), but decreased precision in measuring uncertainty ( dMD $$ dMD $$ , dFA $$ dFA $$ , and d e → 1 $$ d{\overrightarrow{e}}_1 $$ ) when compared to conventional (distorted) BU and BD acquisitions. When comparing the two distortion correction techniques, TOPUP provides improved results for cDTI applications.

Cardiac diffusion tensor imaging (cDTI) is an emerging meth-od capable of characterizing the microstructural organization of both healthy and diseased myocardium. One of the challenging aspects of a cDTI study is the associated data processing due to various acquisition imperfections that can corrupt the acquired data. We sought to investigate the role of various data processing steps by evaluating an open-source cDTI data processing software, Cardiac Diffusion in Python (CarDpy). In order to achieve this goal, healthy volunteers (N = 40) were imaged. Imaging data was evaluated at six incremental postprocessing steps (POSTs) using the CarDpy pipeline. cDTI metrics such as mean diffusivity (MD), fractional anisotropy (FA), and helix angle range (HAR) were evaluated after each POST. Additionally, the uncertainties of MD (dMD), FA (dFA), and the primary eigenvector (de(1)) were evaluated to quantify the data precision. Statistical testing was performed after each POST in a comparison with the final POST. Empirical measurements of MD displayed stable trends across all POSTs, while a decrease in FA was observed with each incremental step. HAR remained stable after the integration of the POST that incorporated image registration into the data processing pipeline. Uncertainties decreased for all metrics as each incremental POST was added. dMD, dFA, and the de1 had minimal improvements after the POST that incorporated shotrejection into the data processing pipeline. Overall, this study provides an in-depth analysis pertaining to the impact of image processing on cDTI metrics and their corresponding uncertainties.

Abnormal passive stiffness of the heart muscle (myocardium) is evident in the pathophysiology of several cardiovascular diseases, making it an important indicator of heart health. Recent advancements in cardiac imaging and biophysical modeling now enable more effective evaluation of this biomarker. Estimating passive myocardial stiffness can be accomplished through an MRI-based approach that requires comprehensive subject-specific input data. This includes the gross cardiac geometry (e.g. from conventional cine imaging), regional diastolic kinematics (e.g. from tagged MRI), microstructural configuration (e.g. from diffusion tensor imaging), and ventricular diastolic pressure, whether invasively measured or non-invasively estimated. Despite the progress in cardiac biomechanics simulations, developing a framework to integrate multiphase and multimodal cardiac MRI data for estimating passive myocardial stiffness has remained a challenge. Moreover, the sensitivity of estimated passive myocardial stiffness to input data has not been fully explored. This study aims to: (1) develop a framework for integrating subject-specific in vivo MRI data into in silico left ventricular finite element models to estimate passive myocardial stiffness, (2) apply the framework to estimate the passive myocardial stiffness of multiple healthy subjects under assumed filling pressure, and (3) assess the sensitivity of these estimates to loading conditions and myofiber orientations. This work contributes toward the establishment of a range of reference values for material parameters of passive myocardium in healthy human subjects. Notably, in this study, beat-to-beat variation in left ventricular end-diastolic pressure was found to have a greater influence on passive myocardial material parameter estimation than variation in fiber orientation.

PURPOSE:To evaluate the use of pre-excitation gradients for eddy current-nulled convex optimized diffusion encoding (Pre-ENCODE) to mitigate eddy current-induced image distortions in diffusion-weighted MRI (DWI). METHODS:DWI sequences using monopolar (MONO), ENCODE, and Pre-ENCODE were evaluated in terms of the minimum achievable echo time (TE   min $$ {}_{\mathrm{min}} $$ ) and eddy current-induced image distortions using simulations, phantom experiments, and in vivo DWI in volunteers ( N = 6 $$ N=6 $$ ). RESULTS:Pre-ENCODE provided a shorter TE   min $$ {}_{\mathrm{min}} $$ than MONO (71.0 ± $$ \pm $$ 17.7ms vs. 77.6 ± $$ \pm $$ 22.9ms) and ENCODE (71.0 ± $$ \pm $$ 17.7ms vs. 86.2 ± $$ \pm $$ 14.2ms) in 100 % $$ \% $$ of the simulated cases for a commercial 3T MRI system with b-values ranging from 500 to 3000 s/mm   2 $$ {}^2 $$ and in-plane spatial resolutions ranging from 1.0 to 3.0mm   2 $$ {}^2 $$ . Image distortion was estimated by intravoxel signal variance between diffusion encoding directions near the phantom edges and was significantly lower with Pre-ENCODE than with MONO (10.1 % $$ \% $$ vs. 22.7 % $$ \% $$ , p = 6 - 5 $$ p={6}^{-5} $$ ) and comparable to ENCODE (10.1 % $$ \% $$ vs. 10.4 % $$ \% $$ , p = 0 . 12 $$ p=0.12 $$ ). In vivo measurements of apparent diffusion coefficients were similar in global brain pixels (0.37 [0.28,1.45] × 1 0 - 3 $$ \times 1{0}^{-3} $$ mm   2 $$ {}^2 $$ /s vs. 0.38 [0.28,1.45] × 1 0 - 3 $$ \times 1{0}^{-3} $$ mm   2 $$ {}^2 $$ /s, p = 0 . 25 $$ p=0.25 $$ ) and increased in edge brain pixels (0.80 [0.17,1.49] × 1 0 - 3 $$ \times 1{0}^{-3} $$ mm   2 $$ {}^2 $$ /s vs. 0.70 [0.18,1.48] × 1 0 - 3 $$ \times 1{0}^{-3} $$ mm   2 $$ {}^2 $$ /s, p = 0 . 02 $$ p=0.02 $$ ) for MONO compared to Pre-ENCODE. CONCLUSION:Pre-ENCODE mitigated eddy current-induced image distortions for diffusion imaging with a shorter TE   min $$ {}_{\mathrm{min}} $$ than MONO and ENCODE.

PURPOSE:Phase contrast MRI (PC-MRI) is used clinically to measure velocities in the body, but systematic background phase errors caused by magnetic field imperfections corrupt the velocity measurements with offsets that limit clinical utility. This work aims to minimize systematic background phase errors in PC-MRI, thereby maximizing the accuracy of velocity measurements. METHODS:The MRI scanner's background phase errors from eddy currents and mechanical oscillations were modeled using the gradient impulse response function (GIRF). Gradient waveforms were then numerically optimized using the GIRF to create pulse sequences that minimize the background phase errors. The pulse sequences were tested in a static phantom where the predicted response could be compared directly to the measured background velocity. The optimized acquisitions were then tested in human subjects, where flow rates and background errors were compared to conventional PC-MRI. RESULTS:When using the GIRF-optimized gradient waveforms, the predicted background phase was within 0.6 [95% CI = -3.4, 5.4] mm/s of the measured background phase in a static phantom. Excellent agreement was seen for in vivo blood flow values (flow rate agreement r 2 $$ {r}^2 $$ = 0.96), and the background phase was reduced by 78.8 ± $$ \pm $$ 18.7%. CONCLUSION:This work shows that using a GIRF to model the effects of magnetic field imperfections combined with numerically optimized gradient waveforms enables PC-MRI waveforms to be designed to produce a minimal background phase in the most time-efficient manner. The methodology could be adapted for other MRI sequences where similar magnetic field errors affect measurements.

In this work, we measured the acoustic frequency response function of an MRI scanner. The response was then used to design arbitrarily shaped gradient waveforms that minimize the predicted acoustic noise output of the sequence. Two minimization functions were tested and compared to the conventional sequence with two different slew rates. The method was used to generate a GRE sequence with 31% reduced acoustic output for a 17% increase in scan time or 16% reduced acoustic output compared to the slew rate derated sequence, both with no difference in image quality.

To maintain minimal scan times, cardiac Diffusion Tensor Imaging (cDTI) uses an echo-planar imaging (EPI) readout. Off-resonance, that causes geometric distortion in EPI, remains an obstacle that degrades image quality and can affect the underlying quantitative information. In cDTI, the lung/liver/heart interface amplifies the effect of geometric distortion. Distortion correction algorithms, such as TOPUP and DR-BUDDI, have proved to adequately correct distortion in neuroimaging, but limited work has been done for the heart. A first look at comparing these two correction strategies head-to-head was evaluated and resulting in TOPUP as a slightly better tool addressing distortion correction in the heart.