Advanced MRI Techniques and Applications

Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA

mrQA is an open-source tool that automatically assesses MRI protocol compliance across datasets, revealing widespread non-compliance in neuroimaging studies.

Continuous automated analysis workflow for MRS studies

This paper introduces an automated MRS analysis workflow that reduces manual steps and integrates with neuroimaging standards, improving efficiency and scalability.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy Studies of Health and Disease

The paper conducts a meta-analysis of brain MRS studies to establish metabolite concentration ranges and relaxation rates for use in simulations and as references.

Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning

This paper introduces a deep learning approach for spectral registration in MRS data, showing improved accuracy in frequency and phase correction compared to existing methods.

SLIPMAT: a pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data

SLIPMAT extracts high-quality, tissue-specific spectral profiles from 1H MRSI data, revealing metabolite differences between healthy individuals.

BOLD response is more than just magnitude: improving detection sensitivity through capturing hemodynamic profiles

This paper improves fMRI analysis by capturing hemodynamic profiles instead of relying on a canonical HRF, showing better sensitivity and cross-study reproducibility.

Sampling strategies and integrated reconstruction for reducing distortion and boundary slice aliasing in high-resolution 3D diffusion MRI

This paper introduces a method to reduce distortion and boundary slice aliasing in 3D diffusion MRI without increasing scan time, achieving high-resolution imaging with improved anatomical fidelity.

Evaluation of Kernel Low-Rank Compressed Sensing in preclinical Diffusion Magnetic Resonance Imaging

This study evaluates Kernel Low-Rank Compressed Sensing for preclinical diffusion MRI, showing improved performance over conventional methods at higher acceleration factors.

High-resolution Dynamic Human Brain Neural Activity Recording Using 3T MRI

This paper introduces TRIGGER, a 3T MRI technique for high-resolution neural activity recording in awake humans, showing millisecond temporal resolution and hemispheric response timing.

The Bergen Breakfast Scanning Club dataset: a deep brain imaging dataset

The paper introduces a deep brain imaging dataset from the Bergen Breakfast Scanning Club project, detailing data acquisition and preprocessing protocols for three subjects over one year.

Imaging the columnar functional organization of human area MT+ to axis-of-motion stimuli using VASO at 7 Tesla

This study uses CBV-sensitive fMRI to quantify columnar organization in human hMT+ for axis-of-motion stimuli, showing higher specificity with VASO over BOLD.

Hi-Fi fMRI: High-resolution, fast-sampled and sub-second whole-brain functional MRI at 3T in humans

This paper introduces a high-resolution, fast-sampled fMRI technique for whole-brain BOLD signal measurement, demonstrating improved spatial and temporal resolution in human studies.

Alpha-180 spin-echo based line-scanning method for high resolution laminar-specific fMRI

This paper introduces a new fMRI method called SELINE to improve laminar-specific BOLD signal detection by reducing vein effects and improving spatial resolution.

APART-QSM: an improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method

This paper introduces APART-QSM, an improved quantitative susceptibility mapping method that separates paramagnetic and diamagnetic susceptibility sources with higher accuracy than previous methods.

Repeated MRI scans of the human brain: diurnal oscillations in healthy adults and bipolar disorder patients

This study shows diurnal oscillations exist in MRI metrics of healthy adults and bipolar disorder patients, with similar rhythmicity parameters except for greater phase variation in cerebral blood flow in patients.

Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range

The paper presents a large-scale MRI database of marmosets across a wide age range to study brain aging and its factors.

Site effects depth denoising and signal enhancement using dual-projection based ICA model

This paper introduces a dual-projection based ICA denoising method to remove scanner/site effects in multi-site MRI data, showing improved performance over traditional ICA and GLM methods.

Perfusion estimation using MRI-based measurements and a porous media flow model

The paper develops a method to estimate tissue perfusion using MRI data and a porous media flow model, validated with synthetic data from complex models.

Changes in brain metabolite levels across childhood

The paper examines age-related changes in brain metabolite levels in children aged 2-11 years using MRS, finding age-related increases and decreases in several key metabolites in specific brain regions.

Hemodynamic timing in resting-state and breathing-task BOLD fMRI

This study compares hemodynamic delay estimates between resting-state and breathing-task fMRI data in healthy adults, finding poor overall agreement but improved agreement in specific brain regions.

Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD

This study examines layer dependence of monocular and binocular responses in human V1 using VASO and BOLD at 7T, finding that VASO better differentiates columnar responses and BOLD signal is confounded by pial veins.

Layer-Specific Vulnerability is a Mechanism of Topographic Map Aging

The paper characterizes age-related changes in microstructure of topographic maps in the primary motor cortex using MRI data from younger and older adults.

Physics-informed Deep Learning Approach to Quantification of Human Brain Metabolites from Magnetic Resonance Spectroscopy Data

This paper introduces a self-supervised deep learning method for quantifying metabolite concentrations in MR spectroscopy data, showing comparable accuracy to traditional methods with faster processing.

Improving magnetic resonance spectroscopy in the brainstem periaqueductal grey using spectral registration

This paper improves 1H-MRS quality in the brainstem periaqueductal grey using spectral registration, showing increased SNR and reduced linewidth and CRLBs.

Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction

This paper introduces Deli-CS, a method that accelerates spatio-temporal MRI reconstruction using deep learning-initialized compressed sensing, reducing time and resource usage while maintaining quality.

Using synthetic MR images for field map-less distortion correction

This paper introduces Synth, a field map-less distortion correction method for fMRI data, showing comparable or better performance than field map-based approaches in pediatric and adult datasets.

A domain-agnostic MR reconstruction framework using a randomly weighted neural network

This paper introduces a domain-agnostic MR reconstruction framework using a randomly weighted neural network that achieves performance comparable to state-of-the-art methods with minimal training data.

Comparing end-tidal CO2, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI

This paper compares alternative reference signals for mapping cerebrovascular reactivity using breath-hold task BOLD fMRI, showing that RVT and GM-BOLD can produce comparable CVR maps when PETCO2 quality is insufficient.

Statistical analyses of motion-corrupted MRI relaxometry data

This paper introduces QUIQI, a method to restore homoscedasticity in MRI relaxometry data by using weighted least squares, improving statistical analysis robustness.

Feasibility of 3T layer-dependent fMRI with GE-BOLD using NORDIC and phase regression

This study demonstrates the feasibility of layer-dependent fMRI at 3T using NORDIC denoising and phase regression, achieving reliable layer profiles in the primary motor cortex.