Haikun Qi

Objective: To develop a real-time method for designing gradient waveforms for arbitrary k-space trajectories that are time-optimal and hardware-compliant. Methods: The gradient waveform is solved recursively under both the slew-rate and the trajectory constraints, which form a quadratic equation. The gradient constraint is enforced by thresholding the L2-norm of the gradient vectors. To ensure the existence of the solution, gradient magnitude is thresholded by the escape velocity. A Discrete-Time Forward and Backward Sweep strategy is then applied to further constrain the slew-rate. Trajectory and gradient reparameterization strategies are adopted to enhance the generality and preserve the sampling accuracy. The proposed method is compared with the conventional optimal control method across seven commonly adopted non-Cartesian trajectories. Imaging feasibility of the designed time-optimal gradient waveform was demonstrated by phantom and in vivo imaging experiments. Results: The proposed method achieves a >89% reduction in computation time and a >98% reduction in slew-rate error simultaneously. The computation time of the proposed method is shorter than the gradient duration for all tested cases, validating the real-time capability of the proposed method. Conclusions: The proposed method enables real-time and hardware-compliant gradient waveform design, achieving significant reductions in computation time and slew-rate overshoot compared to the previous method. Significance: This is the first method achieving real-time gradient waveform design for arbitrary k-space trajectories.

Objective. The autocalibration-signal (ACS) region plays a pivotal role in dynamic magnetic resonance imaging (MRI) reconstruction, yet its full potential remains under-exploited in current generative frameworks, which rely mainly on data-driven priors while overlooking ACS-embedded physical information. This work aims to propose a novel multi-frequency aware diffusion model guided by structural priors derived from the ACS region to enhance the quality of dynamic MRI reconstruction. Approach. Specifically, the intrinsic phase consistency and spatial smoothness within the ACS region are leveraged to construct a robust prior that stabilizes the diffusion trajectory. This prior further guides the restoration of high-frequency components, thereby improving temporal coherence and structural fidelity. Furthermore, the non-ACS region is divided into two complementary frequency bands, each jointly modeled with the ACS region to establish frequency-conditioned diffusion trajectories across the frequency domain. To further refine reconstruction performance, we incorporate low-rank regularization across time frames and enforce data-consistency constraints, which effectively suppress motion artifacts while preserving fine anatomical details. Main results. Quantitative evaluations demonstrate that the proposed method achieves competitive reconstruction quality across various sampling patterns and acceleration factors. Under Radial sampling at an acceleration factor of R = 8, it attains PSNR of 32.53 ± 0.34 dB, NMSE of 0.011 ± 0.001 and SSIM of 0.8583 ± 0.0057, outperforming all competing methods. Notably, the proposed method achieves competitive performance comparable to supervised models without requiring any fully-sampled data or external training labels. Significance. The proposed method enables high-quality dynamic MRI reconstruction by exploiting intrinsic ACS information, offering a practical solution without fully-sampled training data.

Background: Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is the clinical standard for myocardial scar assessment, yet scar quantification from LGE images remains labor-intensive. Automating this process needs to overcome two key challenges: the scarcity of annotated LGE datasets and inter-observer variability in manual reference region selection. We therefore aimed to develop and externally validate an automated pipeline that combines cross-modal myocardial segmentation with probabilistic remote-region selection to quantify scar burden and transmurality on LGE-CMR in patients with acute myocardial infarction (AMI). Methods: This retrospective multicenter study included 420 AMI patients from three centers (August 2017–July 2023) with clinically confirmed AMI and diagnostic-quality cine and LGE-CMR; non-ischemic enhancement patterns were excluded. After image quality control, 281 patients (1,729 slices) from center 1 were partitioned at the patient level into cross-modal adaptation (n=218, 1,409 slices) and expert-correcting (n=63, 320 slices) subsets; 30 patients (216 slices) from the other 2 centers served as the independent external test set. Reference myocardial contours and scar masks were delineated by an experienced CMR expert using manual full-width at half-maximum (FWHM) with operator-selected remote regions. The framework couples cross-modal semi-supervised left ventricular (LV) myocardial segmentation (cine → LGE transfer with expert-corrected fine-tuning) with a modified FWHM module that automatically selects remote-myocardium and scar seed regions via a segment-wise enhancement probability predictor. Segmentation was evaluated by Dice similarity coefficient, intersection over union (IoU), sensitivity, positive predictive value (PPV), 95th percentile Hausdorff distance (HD95), and average surface distance (ASD); agreement of scar burden and transmurality was evaluated by Pearson correlation, intraclass correlation coefficient (ICC), and Bland-Altman analysis. Results: Across the external test cohorts (n=30 patients, 216 LGE slices), fine-tuning improved LV segmentation (mean of myocardium, blood pool, and scar): Dice 0.68→0.77, IoU 0.58→0.68, sensitivity 0.62→0.72, PPV 0.64→0.80, HD95 7.58→4.50 mm, and ASD 2.68→1.55 mm. Automated scar burden quantification showed strong agreement with expert measurements, achieving Pearson correlation r=0.93 (PConclusions: By integrating cross-modal semi-supervised LV myocardium segmentation with automatic reference region selection for FWHM thresholding, our framework enables reproducible, fully automated myocardial scar quantification on LGE CMR and may facilitate standardized analysis in multi-center studies and longitudinal clinical follow-up.

Coronary microvascular dysfunction (CMD) is prevalent in diabetes. Ten-eleven translocation-2 (TET2) as the major demethylase in endothelial cells (ECs) is decreased in diabetic CMD, and the role warrants further exploration. In this study, a multi-modality imaging, consist of transthoracic Doppler echocardiography and artery spin labeling cardiac magnetic resonance, is assessed for coronary microvascular function. The expression of TET2 is down-regulated in the heart of diabetic CMD mice. ECs TET2 conditional knockout increases the severity of CMD in diabetic mice. Mechanistically, TET2 deficiency declined the expression of CMPK2, a mtDNA synthetase. Additionally, the mitochondrial electron transport chain complexes encoded by mitochondrial DNA (mtDNA) are down-regulated, which contributes to the excessive production of reactive oxygen species. This, in turn, exacerbated mitochondrial dysfunction, manifesting as mitochondrial membrane potential depolarization, aberrant opening of the mitochondrial permeability transition pore, and structural abnormalities in mitochondria. Therapeutic research demonstrates that vitamin C improves ECs mitochondrial function in diabetic CMD through the TET2–CMPK2 pathway, revealing its potential clinical therapeutic value. In conclusion, we show that loss of endothelial TET2 impairs endothelial mitochondrial function and exacerbated diabetic CMD by regulating the expression of mitochondria enzyme CMPK2.

Background: The application of ultrahigh-field cardiac magnetic resonance imaging (MRI), such as 9.4 T imaging, in mouse models remains challenging, especially under rapid heart rates (500–600 beats per minute), and its feasibility and reproducibility have yet to be thoroughly studied. Therefore, this study aimed to evaluate the feasibility and reproducibility of combining cardiac magnetic resonance feature tracking (CMR-FT)-derived strain parameters and conventional cardiac MRI parameters to image an acute experimental autoimmune myocarditis (EAM) mouse model under a 9.4 T ultrahigh field system and to assess its potential for the early diagnosis of acute EAM. Methods: This retrospective study (conducted from December 2023 to December 2024) included 45 male BALB/c mice (30 EAM mice and 15 controls). EAM mice were injected with Complete Freund’s Adjuvant (CFA; Sigma-Aldrich) to induce myocarditis. The control mice were treated with equal amounts of normal saline at the same time and position as those in experimental mice. CMR was performed with a 9.4 T scanner (Biospec 94/30; Bruker BioSpin) at 7 days, which included precontrast T1, postcontrast T1 and T2, and extracellular volume fraction (ECV) mapping. Left ventricular (LV) strain was evaluated via feature tracking. The diagnostic performance of CMR was evaluated through receiver operating characteristic (ROC) analysis. Results: A total of 45 mice (30 EAM mice and 15 controls) were included. All parameters were feasible in the mice that underwent CMR, and there was excellent reliability, as indicated by intraclass correlation coefficients greater than 0.9 for both intra- and interobserver agreement across all parameters. Intra- and interobserver agreement was analyzed by intraclass correlation coefficients (ICC). EAM mice demonstrated significantly impaired strain parameters compared to healthy controls [global radial strain (GRS): 34.07%±2.49% vs. 38.20%±2.76%, Pvs. –21.4%±2.37%, P=0.001; global longitudinal strain: –17.03%±1.64% vs. –19.04%±2.03%; P=0.001]. In ROC analyses, combining GRS with T2 generated the best parameter for identifying acute myocarditis (area under the curve, 0.882; sensitivity, 88.7%; specificity, 82.0%) and provided incremental diagnostic value. Conclusions: The feasibility of LV strain parameters combined with CMR conventional sequences in identifying EAM in mice at 9.4 T CMR has good reproducibility. For mice with preserved LV ejection fraction, the combination of T2 and GRS can significantly increase the ability to predict EAM within 7 days and provides incremental value as compared to conventional CMR parameters.

Density Compensation Function (DCF) is widely used in non-Cartesian MRI reconstruction, either for direct Non-Uniform Fast Fourier Transform (NUFFT) reconstruction or for iterative undersampled reconstruction. Current state-of-the-art methods involve time-consuming tens of iterations, which is one of the main hurdles for widespread application of the highly efficient non-Cartesian MRI. In this paper, we propose an efficient, non-iterative method to calculate DCF for arbitrary non-Cartesian $k$-space trajectories using Fast Fourier Deconvolution. Simulation experiments demonstrate that the proposed method is able to yield DCF for 3D non-Cartesian reconstruction in around 20 seconds, achieving orders of magnitude speed improvement compared to the state-of-the-art method while achieving similar reconstruction quality.

Purpose: To develop a rapid 2D free‐running myocardial mapping technique that is robust to through‐plane respiratory motion. Methods: A free‐running golden angle radial sequence consisting of encoding and self‐navigated auto motion calibration (SNAC) was developed. The encoding adopted inversion recovery (IR) prepared interleaved multi‐slice acquisition with optimized inter‐slice gap to ensure a uniform excitation of the middle slice regardless of through‐plane respiratory motion. The flip angles were alternated between the odd and even IR readouts to correct flip‐angle errors. SNAC was designed to calibrate the through‐plane motion with the respiratory self‐navigation signal extracted from the free‐running sequence, and integrate the multi‐slice data into a through‐plane motion‐corrected 2D slice for mapping reconstruction. Numerical simulations were performed to optimize the key sequence parameters, followed by phantom and in‐vivo imaging to validate the accuracy and repeatability. Results: Numerical simulations yielded the dual flip angles minimizing estimation errors and the adjacent slice offset achieving uniformity in the superimposed slice profile. Phantom experiments demonstrated a strong correlation between the proposed and reference method, and no dependence of the free‐running estimation on heart rates. Adding through‐plane respiratory motion correction significantly improved the in‐vivo mapping sharpness and visual quality. Validated against the conventional breath‐hold mapping technique, the motion corrected free‐running method achieved comparable mapping quality and repeatability. Conclusion: Respiratory motion is effectively suppressed in the proposed 2D free‐running method, which achieves superior mapping with acceptable repeatability in a short scan time of 46 s.