Advanced MRI Techniques and Applications

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

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.

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.

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.

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.

This paper optimizes the FLAWS MRI sequence with compressed sensing to reduce acquisition time while maintaining image quality and enables T1-weighted imaging and mapping at 3T.

The paper measures glutamate metabolism in the human brain using 1H FID-MRSI after oral administration of [1-13C]Glc, revealing labeling changes in multiple metabolites with high spatial and temporal resolution.

This paper develops and applies a VASO protocol to study laminar responses and tonotopy in the human auditory cortex at 7 Tesla.

This paper improves the robustness of 3D multi-shot EPI for fMRI at 7T by combining a new sampling trajectory with a structured low-rank reconstruction method, achieving improved tSNR and acceleration.

This paper develops a two-stage deep learning model to reduce motion artifacts in liver DCE-MRI, achieving better results than existing methods.

The paper develops a mechanistic model integrating neurovascular and neurometabolic couplings to describe and predict metabolic and BOLD responses to visual stimuli.

This paper optimizes a non-contrast MRI technique to generate 4D angiographic and perfusion images of the whole head from a single scan using radial imaging and arterial spin labeling.

The paper introduces DANSE, a deep learning method for fast and noise-suppressed estimation of qGRE MRI metrics, outperforming traditional methods in speed and accuracy.

HALO is a software tool that provides real-time head alignment feedback to improve consistency across MRI sessions, reducing susceptibility noise in longitudinal studies.

The paper provides a high-resolution T2* MRI dataset of the human brain at 7 Tesla, revealing cortical substructures and consistent depth-dependent T2* values across subjects.

This paper develops unsupervised deep learning methods for frequency and phase correction of MRS signals, showing comparable performance to supervised approaches and improved efficiency.

The paper investigates neurometabolic changes with age using MR spectroscopy in a cross-sectional cohort, finding age correlations for several metabolites but not others.

This paper introduces TQ-SILiCON, a method that fuses quantitative susceptibility maps with T1-weighted images to improve brain tissue contrast in primates, enhancing segmentation of subcortical and white matter structures.

This paper conducts a systematic review and meta-analysis of fMRS studies on GABA, Glu, and Glx, finding small to moderate effect sizes for Glu and Glx but no significant effects for GABA.

This study evaluates ME-ICA denoising on task-based fMRI, finding it improves model fitting performance compared to single echo and optimal combination without ICA.

The paper measures cardiac-aligned fast fMRI signals to assess blood and CSF flow dynamics in the brain using SMS acquisition methods.

The paper links quantitative MRI parameters to cell type-specific gene expression in human neocortex, showing associations with GABA- and glutamatergic neurons and other cell types.

The paper evaluates and corrects slice-direction geometric distortion in high-resolution 7T EPI using reversed slice-select gradient acquisitions and gradient reversal-based correction.

GLMsingle improves single-trial fMRI response estimates using custom HRFs, noise regressors, and ridge regression, enhancing reliability and decoding performance across datasets.

The paper develops a model to predict depth-dependent VASO and BOLD responses in human primary visual cortex and finds that CBV changes mainly occur in small arterioles and capillaries.

The paper investigates the relationship between cerebral blood flow (CBF) and negative BOLD responses (NBR) in the human visual cortex, comparing them to positive BOLD responses (PBR) using multi-echo imaging techniques.

The paper introduces NIfTI-MRS, a standardized format for magnetic resonance spectroscopy data to improve sharing and integration across the MRS community.

This paper presents quantitative metabolite maps of 12 metabolites in human brains at 9.4 T, using voxel-specific T1-relaxation corrections for water and metabolites.

This paper introduces a goal-specific MRI harmonization framework that improves downstream prediction performance by integrating application-specific constraints during harmonization.