Advanced MRI Techniques and Applications

Harmonizing Inter-Site Differences in T1-Weighted Images Using CycleGAN

This paper compares CycleGAN and ComBat for harmonizing T1-weighted MRI images across sites, finding CycleGAN preserves image quality better with minimal preprocessing.

Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation

This paper identifies and mitigates Fuzzy Ripple artifacts in high-resolution fMRI, enabling sub-millimeter resolution and faster acquisition protocols.

Development of Proton Density Fat Fraction Micro-MRI for Non-Invasive Quantitative Assessment of Bone Marrow Changes with Age and Radiation in Mouse Models

This paper develops a PDFF MRI technique to quantify bone marrow fat fraction in mice, showing significant differences with age and radiation, validated by histology.

Data Processing in Multidimensional MRI For BiomarkerIdentification: Is It Necessary?

This study compares the effectiveness of using raw MD-MRI signals versus reconstructed spectra for statistical regression in biomarker identification, concluding that signals are more effective without prior information.

Beyond ROIs: Laminar fMRI Mapping with Cylarim

Cylarim enables unbiased laminar fMRI mapping by eliminating reliance on predefined ROIs, allowing large-scale, depth-resolved analysis of cortical activity.

Temporal fMRI Dynamics Map Dopamine Physiology

The paper maps dopamine physiology using hemodynamic latency from fMRI, showing distinct patterns in the nucleus accumbens and linking them to addictive behaviors and substance use disorders.

Quantifying brain-wide cerebrospinal fluid flow dynamics using slow-flow-sensitized phase-contrast MRI

This paper introduces a phase-contrast MRI technique to quantify slow cerebrospinal fluid flow in the human brain, showing cardiac pulsation drives brain-wide flow more than respiration.

T1-weighted fMRI in mouse visual cortex using an Iron Oxide Nanoparticle contrast agent and Ultrashort Echo Time (UTE) imaging at 9.4 T

This paper tests T1-weighted fMRI with iron oxide nanoparticles and UTE imaging at 9.4T to detect functional hyperaemia in mouse visual cortex, showing positive signal changes in both parenchyma and pial surface.

T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data

This paper introduces a rapid fMRI acquisition method, T1234, that provides distortion-matched structural reference data, improving registration accuracy with functional scans.

Variable Cerebral Blood Flow Responsiveness to Acute Hypoxic Hypoxia

This study assesses cerebral blood flow responses to hypoxia and hypercapnia using MRI, finding poor reliability in hypoxic CVR and paradoxical responses not explained by CO2 changes.

MRI R2* captures inflammation in disconnected brain structures after stroke: a translational study

This study shows that MRI R2* changes reflect inflammation in disconnected brain regions after stroke, both in humans and mice, linking disconnection to iron accumulation and microglial activation.

Noise decorrelation optimizes SNR of GABA-edited MRS data: A comparison of RF coil combination methods

The paper compares RF coil combination methods for MRS data, finding that noise-decorrelation methods improve SNR for detecting low concentration metabolites like GABA.

Challenging the classical view of CSF flow: measuring CSF net velocity in the human subarachnoid space with 7T MRI

This study challenges the classical view of CSF flow by measuring net CSF velocity in the human subarachnoid space using 7T MRI and finding no significant net flow toward the superior sagittal sinus.

The Good, the Bad, and the Ugly: Segmentation-Based Quality Control of Structural Magnetic Resonance Images

This paper introduces a quality assessment framework for structural MRI images using tissue classification to identify and mitigate motion artifacts and other quality issues.

Ultra-High Resolution TR-external EPIK with Deep Learning Image Reconstruction for Enhanced Characterisation of Cortical Depth-dependent Neural Activity

This paper improves submillimetre fMRI image reconstruction using deep learning, achieving higher SNR than conventional methods in cortical depth-dependent neural activity studies.

Awake infant fMRI: Insights from more than 750 scanning sessions

This study analyzes 766 awake infant fMRI sessions to identify factors affecting data collection and retention, offering insights to improve future research.

Development of a non-invasive novel individual marmoset holder for evaluation by awake functional magnetic resonance brain imaging

The paper develops a non-invasive marmoset holder for small-bore MRI to enable awake fMRI studies and evaluates its ability to capture auditory responses.

DeepPrep: An accelerated, scalable, and robust pipeline for neuroimaging preprocessing empowered by deep learning

DeepPrep accelerates neuroimaging preprocessing by 11-fold using deep learning, outperforming current state-of-the-art methods in scalability and robustness.

Cerebral blood perfusion across biological systems and the human lifespan

This study maps cerebral blood perfusion across the human lifespan and links it to brain structure, function, and disease vulnerability using ASL data from 1,305 individuals.

ISME - Incoherent Sampling of Multi-Echo data to minimize cardiac-induced noise in brain maps of R2* and magnetic susceptibility

The paper introduces ISME, a sampling strategy that reduces cardiac-induced noise in brain maps of R2* and magnetic susceptibility, improving reproducibility.

Task Activation Results in Regional 13C-Lactate Signal Increase in the Human Brain

The paper uses HP-13C MRI to show increased 13C-lactate signal in brain regions activated during a visual task compared to a control condition.

The Proton Resonance Enhancement for CEST imaging and Shift Exchange (PRECISE) family of RF pulse shapes for Chemical Exchange Saturation Transfer MRI

The paper develops a new RF pulse shape for CEST MRI using gradient ascent optimization, showing improved contrast in phantoms and live mice compared to Gaussian and Fermi pulses.

Zero-echo time imaging achieves whole brain activity mapping without ventral signal loss in mice

This paper demonstrates that zero-echo time (ZTE) imaging can map whole brain activity in mice without signal loss in the amygdala, showing acute and latent activation patterns following noxious stimulation.

Chemical shift and relaxation regularisation improve the accuracy of 1H MR spectroscopy analysis

This study shows that regularisation improves the accuracy of 1H MR spectroscopy analysis by reducing parameter instability and enhancing agreement between different methods.

An Open Access Resource for Marmoset Neuroscientific Apparatus

The paper provides open-access CAD files and software for marmoset neuroscience apparatus, reducing the need for individual lab development.

Distinguishing microgliosis and tau deposition in the mouse brain using paramagnetic and diamagnetic susceptibility source separation

This study uses quantitative susceptibility mapping to distinguish microgliosis and tau deposition in a mouse model of tauopathy by analyzing paramagnetic and diamagnetic susceptibility components.

Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns

This paper introduces a method to distinguish BOLD and non-BOLD signals in fMRI by analyzing cross-cortical depth delay patterns, demonstrating its effectiveness with visual task data.

A non-invasive approach to awake mouse fMRI compatible with multi-modal techniques

This paper introduces a non-invasive restraint for awake mouse fMRI that is compatible with multi-modal techniques, showing comparable data quality to conventional methods.

Characterizing BOLD activation patterns in the human hippocampus with laminar fMRI

This study investigates how venous bias affects hippocampal laminar fMRI responses and establishes a benchmark experiment for robust single-subject activation.

Modeling dynamic inflow effects in fMRI to quantify cerebrospinal fluid flow

This paper develops a dynamic model and deep learning framework to estimate cerebrospinal fluid velocity from fMRI data, enabling physically interpretable flow measurements.