Publications

View details for DOI 10.1007/978-3-032-04947-6_46

View details for Web of Science ID 001596377700046

Abstract

Cardiac diffusion tensor imaging (cDTI) is signal-to-noise ratio (SNR)-limited due to diffusion signal attenuation, long echo times from gradient moment nulling, and moderate myocardial T 2

Abstract

Mapping the brain's fiber network is crucial for understanding its function and malfunction, but resolving nerve trajectories over large fields of view is challenging. Here, we show that computational scattered light imaging (ComSLI) can map fiber networks in histology independent of sample preparation, also in formalin-fixed paraffin-embedded (FFPE) tissues including whole human brain sections. We showcase this method in new and archived, animal and human brain sections, for different sample preparations (in paraffin, deparaffinized, various stains, unstained fresh-frozen). We convert microscopic orientations to microstructure-informed fiber orientation distributions (μFODs). Adapting tractography tools from diffusion magnetic resonance imaging (dMRI), we trace axonal trajectories revealing white and gray matter connectivity. These allow us to identify altered microstructure or deficient tracts in demyelinating or neurodegenerating pathology, and to show key advantages over dMRI, polarization microscopy, and structure tensor analysis. Finally, we map fibers in non-brain tissues, including muscle, bone, and blood vessels, unveiling the tissue's function. Our cost-effective, versatile approach enables micron-resolution studies of intricate fiber networks across tissues, species, diseases, and sample preparations, offering new dimensions to neuroscientific and biomedical research.

View details for DOI 10.1038/s41467-025-64896-9

View details for PubMedID 41193461

View details for PubMedCentralID 1239902

Abstract

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

Abstract

We present a stretched radial trajectory design that enhances image resolution in MRI by expanding k-space coverage without increasing readout duration or scan time. The method dynamically modulates gradient amplitudes as a function of projection angle, achieving square k-space coverage in 2D and cubic coverage in 3D imaging. Validation was conducted using phantom and in vivo experiments on GE and Siemens scanners at 0.55 T and 3 T. Point spread function analysis and reconstructed images demonstrated improved sharpness and clearer visualization of fine structures, including small phantom details and brain vasculature. The approach also increased T1 and T2 mapping accuracy in MRF acquisitions. The proposed strategy requires no additional scan time or gradient hardware capability, making it well-suited for MRI systems with moderate performance. It offers a simple and generalizable means to improve spatial resolution in both structural and quantitative imaging applications.

View details for DOI 10.3390/bioengineering12111152

View details for PubMedID 41301109

View details for PubMedCentralID PMC12649564

Abstract

We introduce a new algorithm to solve a regularized spatial-spectral image estimation problem. Our approach is based on the linearized alternating directions method of multipliers (LADMM), which is a variation of the popular ADMM algorithm. Although LADMM has existed for some time, it has not been very widely used in the computational imaging literature. This is in part because there are many possible ways of mapping LADMM to a specific optimization problem, and it is nontrivial to find a computationally efficient implementation out of the many competing alternatives. We believe that our proposed implementation represents the first application of LADMM to the type of optimization problem considered in this work (involving a linear-mixture forward model, spatial regularization, and nonnegativity constraints). We evaluate our algorithm in a variety of multiparametric MRI partial volume mapping scenarios (diffusion-relaxation, relaxation-relaxation, relaxometry, and fingerprinting), where we consistently observe substantial ( ∼ 3 × - 50 × ) speed improvements. We expect this to reduce barriers to using spatially-regularized partial volume compartment mapping methods. Further, the considerable improvements we observed also suggest the potential value of considering LADMM for a broader set of computational imaging problems.

View details for DOI 10.1109/TCI.2025.3609974

View details for PubMedID 41234480

View details for PubMedCentralID PMC12610334

Abstract

Deep learning-based multi-coil magnetic resonance image reconstruction has been actively investigated and increasingly applied in clinical settings. However, most models are physics-model-based approaches, which either require precalculation of coil sensitivity or fail to achieve optimal performance without taking coil sensitivity into consideration. Inspired by ESPIRiT, we propose a physics-model-independent direct mapping approach, namely DM-Net, which aims for optimal reconstruction without explicitly using coil sensitivity. A densely connected convolutional network is employed, where coil sensitivity and channel correlation are intrinsically exploited. For comparison, other reconstruction models that mimic SENSE and GRAPPA are implemented. DMwS-Net is a direct mapping approach that incorporates precalculated coil sensitivity maps as additional input; and CCR-Nets provide coil-by-coil reconstruction, where individual coil images are jointly predicted and combined with or without coil sensitivity. The proposed models are trained and tested on 5440 images from 17 subjects acquired with 3DFT (Fourier transform). DM-Net achieves superior performance, suggesting the feasibility of excluding precalculated coil sensitivity from model input. Moreover, DM-Net can be applied on k-space data without a fully sampled calibration region, which may improve image quality by sampling high frequency regions more densely. This work expands the scope of DL-based image reconstruction and provides evidence that a direct mapping framework without using coil sensitivity may lead to reliable, simplified and faster reconstruction.

View details for DOI 10.21203/rs.3.rs-7174070/v1

View details for PubMedID 40951291

View details for PubMedCentralID PMC12425054

View details for DOI 10.1162/imag_a_00470

View details for Web of Science ID 001524668400001

Abstract

Developmental cognitive neuroscience aims to shed light on evolving relationshipsbetween brain structure and cognitive development. To this end, quantitativemethods that reliably measure individual differences in brain tissue propertiesare fundamental. Standard qualitative MRI sequences are influenced by scanparameters and hardware-related biases, and also lack physical units, making theanalysis of individual differences problematic. In contrast, quantitative MRIcan measure physical properties of the tissue but with the cost of long scandurations and sensitivity to motion. This poses a critical limitation forstudying young children. Here, we examine the reliability of an efficientquantitative multiparameter mapping method-magnetic resonancefingerprinting (MRF)-in children scanned longitudinally. We focus on T1values in white matter, since quantitative T1 values are known to primarilyreflect myelin content, a key factor in brain development. Forty-nine childrenaged 8-13 years (mean 10.3 years ± 1.4) completed 2 scanningsessions 2-4 months apart. In each session, two 2-min 3D-MRF scans at 1mm isotropic resolution were collected to evaluate the effect of scan durationon image quality and scan-rescan reliability. A separate calibration scanwas used to measure B0 inhomogeneity and correct for bias. We examined theimpact of scan time and B0 inhomogeneity correction on scan-rescanreliability of values in white matter, by comparing single 2-min and combinedtwo 2-min scans, with and without B0 correction. Whole-brain voxel-basedreliability analysis showed that combining two 2-min MRF scans improvedreliability (Pearson's r = 0.87) compared with a single 2-min scan(r = 0.84), while B0 correction had no effect on reliability in whitematter (r = 0.86 and 0.83 4- vs. 2-min). Using diffusion tractography, wesegmented major white matter fiber tracts and examined the profiles ofMRF-derived T1 values along each tract. We found that T1 values from MRF showedsimilar or greater reliability compared with diffusion parameters. Lastly, wefound that R1 (1/T1) values in multiple white matter tracts were significantlycorrelated with age. In sum, MRF-derived T1 values were highly reliable in alongitudinal sample of children and replicated known age effects. Reliability inwhite matter was improved by longer scan duration but was not affected by B0correction, making it a quick and straightforward scan to collect. We proposethat MRF provides a promising avenue for acquiring quantitative brain metrics inchildren and patient populations where scan time and motion are of particularconcern.

View details for DOI 10.1162/imag_a_00470

View details for PubMedID 40800775

View details for PubMedCentralID PMC12319766

Abstract

Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction time using deep learning.This study focuses on accelerating the reconstruction of volumetric multi-axis spiral projection MRF, aiming for whole-brain T1 and T2 mapping, while ensuring a streamlined approach compatible with clinical requirements. To optimize reconstruction time, the traditional method is first revamped with a memory-efficient GPU implementation. Deep Learning Initialized Compressed Sensing (Deli-CS) is then introduced, which initiates iterative reconstruction with a DL-generated seed point, reducing the number of iterations needed for convergence.The full reconstruction process for volumetric multi-axis spiral projection MRF is completed in just 20 min compared to over 2 h for the previously published implementation. Comparative analysis demonstrates Deli-CS's efficiency in expediting iterative reconstruction while maintaining high-quality results.By offering a rapid warm start to the iterative reconstruction algorithm, this method substantially reduces processing time while preserving reconstruction quality. Its successful implementation paves the way for advanced spatio-temporal MRI techniques, addressing the challenge of extensive reconstruction times and ensuring efficient, high-quality imaging in a streamlined manner.

View details for DOI 10.1007/s10334-024-01222-2

View details for PubMedID 39891798

View details for PubMedCentralID 3602925

Abstract

To provide a fast quantitative imaging approach for a 0.55T scanner, where signal-to-noise ratio is limited by the field strength and k-space sampling speed is limited by a lower specification gradient system.We adapted the three-dimensional spiral projection imaging MR fingerprinting approach to 0.55T scanners, with additional features incorporated to improve the image quality of quantitative brain and musculoskeletal imaging, including (i) improved k-space sampling efficiency, (ii) Cramér-Rao lower bound optimized flip-angle pattern for specified T1 and T2 at 0.55 T, (iii) gradient trajectory correction, (iv) attention-based denoising, and (v) motion estimation and correction.The proposed MRF acquisition and reconstruction framework can provide high-quality 1.2-mm isotropic whole-brain quantitative maps and 1-mm isotropic knee quantitative maps, each acquired in 4.5 min. The proposed method was validated in both phantom and in vivo brain and knee studies.By proposing novel methods and integrating advanced techniques, we achieved high-isotropic-resolution MRF on a 0.55T scanner, demonstrating enhanced efficiency, motion resilience, and quantitative accuracy.

View details for DOI 10.1002/mrm.30420

View details for PubMedID 39815710

View details for DOI 10.1109/TCI.2025.3609974

View details for Web of Science ID 001581659300001

View details for DOI 10.1109/ISBI60581.2025.10980956

View details for Web of Science ID 001546451000283

Abstract

BUDA-cEPI has been shown to achieve high-quality, high-resolution diffusion magnetic resonance imaging (dMRI) with fast acquisition time, particularly when used in conjunction with S-LORAKS reconstruction. However, this comes at a cost of more complex reconstruction that is computationally prohibitive. In this work we develop rapid reconstruction pipeline for BUDA-cEPI to pave the way for its deployment in routine clinical and neuroscientific applications. The proposed reconstruction includes the development of ML-based unrolled reconstruction as well as rapid ML-based B0 and eddy current estimations that are needed. The architecture of the unroll network was designed so that it can mimic S-LORAKS regularization well, with the addition of virtual coil channels.BUDA-cEPI RUN-UP - a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also introduced into the reconstruction to effectively take advantage of the smooth phase prior and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS.The introduction of the Virtual Coil concept into the unrolled network was shown to be key to achieving high-quality reconstruction for BUDA-cEPI. With the inclusion of an additional non-diffusion image (b-value = 0 s/mm2), a slight improvement was observed, with the normalized root mean square error further reduced by approximately 5 %. The reconstruction times for S-LORAKS and the proposed unrolled networks were approximately 225 and 3 s per slice, respectively.BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ~88× when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application.

View details for DOI 10.1016/j.mri.2024.110277

View details for PubMedID 39566835

Abstract

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.

View details for DOI 10.1109/RBME.2024.3485022

View details for PubMedID 39437302