This study introduces a new wearable device—designed like a pair of glasses—that improves the quality of MRI scans of the eyes. MRI image quality is often described using the��signal-to-noise ratio (SNR), which compares the useful signal (clear image information) to background noise; higher SNR means clearer, more detailed images. Imaging the eyes is particularly challenging, especially at very high magnetic field strengths (such as 7 Tesla), where maintaining good image quality across both the eyes and the brain can be difficult.

The researchers created lightweight, 3D-printed “resonator glasses” that contain small electronic components called��LC loop resonators��(circuits that can enhance MRI signal locally). These resonators work wirelessly by interacting with the existing MRI head coil, meaning no modifications to the scanner hardware are needed. The team tested the device in both lab setups (phantoms, which simulate human tissue) and real human scans. They found that the glasses significantly improved image clarity in the eye region—boosting SNR by up to three times—while not reducing image quality in the rest of the brain.

Overall, this device offers a simple, low-cost way to enhance eye imaging during MRI scans without interfering with standard brain imaging. This could make it easier to study eye conditions or perform combined eye–brain imaging in clinical and research settings.

FIGURE 1

Circuit diagram (A) and CAD design (B) of the wireless resonator glasses.

]]> /valiant/2026/03/26/pet-imaging-in-alzheimer-disease-in-the-era-of-antiamyloid-therapy-in-the-united-states-clinical-utility-quantification-and-policy-landscape/ Thu, 26 Mar 2026 19:04:23 +0000 /valiant/?p=6321 Ty Skyles; Samantha M. Bouchal; Anna Giarratana; Jacob Wengler; Ian Hart; Erin Greig; Harmanjeet Singh; Steve S. Huang; Felipe Martinez; Ba Nguyen; Clifford H. Shin; Ming Yang; Ephraim Parent; W. Hudson Robb; Ana M. Franceschi; Brian Burkett; Derek Johnson; Mary Ellen Koran (2026).��.��Journal of Nuclear Medicine Technology, 54(1), 10–17.��

This review explains how advanced brain imaging techniques are improving the way Alzheimer’s disease (AD) is diagnosed and managed. A key tool is��PET imaging (positron emission tomography), which allows doctors to see specific biological changes in the brain while a person is still alive. Different types of PET scans highlight different aspects of the disease.��Amyloid PET��detects amyloid-β plaques—abnormal protein buildups that are a hallmark of Alzheimer’s—and is now especially important because some new treatments require confirmation that these plaques are present before therapy can begin.��Tau PET��images another protein, tau, which forms tangles inside brain cells and is closely linked to disease severity; this makes it useful for determining how advanced the disease is and for understanding unusual symptoms. Meanwhile,��18F-FDG PET��measures how the brain uses glucose (its main energy source), helping doctors distinguish Alzheimer’s from other types of dementia based on patterns of reduced brain activity.

The review highlights that these imaging methods are becoming more widely available and are increasingly used together with clinical evaluations and other biomarkers (such as those found in blood or cerebrospinal fluid). Improved quantitative techniques—methods that provide precise, repeatable measurements—also allow doctors to track disease progression and monitor how well treatments are working over time. Overall, molecular imaging is shifting Alzheimer’s diagnosis toward a more biology-based approach, enabling earlier and more accurate detection and supporting more personalized treatment strategies.

FIGURE 1.

¹⁸F-FDG PET scans of patients without (A) and with (B) AD. (A) Maximum-intensity-projection image showing absence of gross atrophy or pathology. (B) Maximum-intensity-projection image showing characteristic hypometabolism in posterior cingulate, precuneus, and temporoparietal cortices, with relative preservation of metabolism in sensorimotor cortex. This pattern often produces appearance of person wearing headphones, sometimes referred to as “earmuff” or “headphone” sign.

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���������������������� Diffusion-weighted MRI (dMRI) is a powerful brain imaging technique that helps scientists study how nerve fibers, or white matter, are organized and connected in the brain. This method allows researchers to map the brain’s “connectome”—a network-like model that shows how different regions communicate. However, the accuracy of these maps can be affected by the shape and size of the 3D pixels, called voxels, used in the scans. When voxels are not perfect cubes (a condition called anisotropy), they can distort measurements of brain structure, but the full extent of this effect hasn’t been well understood.

In this study, we explored how anisotropic voxels influence both the fine details of brain tissue (microstructural measures like fractional anisotropy and mean diffusivity) and larger white matter features (such as bundle volume, length, and surface area). We analyzed brain scans from 44 participants in the Human Connectome Project, comparing data collected at different voxel resolutions. Using statistical tests, we examined how changing voxel shape affected key measurements of white matter structure and connectivity.

Our findings showed that even small changes in voxel shape caused significant differences in at least one microstructural and one bundle-related measure at every tested resolution. This means that voxel anisotropy can meaningfully alter how we interpret brain microstructure and tractography results. We also found that while certain detailed tissue measures could not be accurately restored through simple image upsampling, the consistency of larger white matter bundle measurements improved when data were resampled to 1 mm isotropic voxels.

In short, this study highlights how subtle differences in imaging resolution can affect the accuracy and reliability of brain connectivity studies, emphasizing the need for careful voxel selection and correction methods in diffusion MRI research.

Fig. 1.��We illustrate the range of voxels used for this experiment with tensor and tractogram representation. We see a bias in the tensor model toward the superior-inferior direction in the anisotropic voxels when compared to the isotropic sampling. The tractogram’s representation of the corpus callosum dramatically changes based on spatial sampling; the highly anisotropic voxels influence the tracking behavior to generate superior-inferior streamlines when the corpus callosum’s anatomy includes right-left whtie matter.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive brain imaging technique that tracks the movement of water molecules in tissue over time. Because water movement is influenced by tiny cellular structures like membranes, axons, and myelin, dMRI provides a unique way to study the brain’s microstructure. One advanced dMRI method, called neurite orientation dispersion and density imaging (NODDI), models how brain cells and their connections are organized, giving detailed insights into tissue structure.

The early postnatal period is a time of rapid brain growth, including axonal growth, dendritic branching, and synapse formation. These processes change the brain’s microstructure in ways that NODDI can detect, making it a promising tool for studying early brain development. This review highlights recent studies using NODDI in infancy, showing how it can map typical developmental patterns, examine changes in preterm infants, and link microstructural properties to environmental factors and early behaviors.

While research is still limited—often with small sample sizes, narrow age ranges, and few longitudinal studies—initial findings suggest that NODDI can complement traditional diffusion measures and offer new insights into early neural development and brain plasticity. Continued use and refinement of NODDI in infants may help identify sensitive periods in brain development and improve understanding of emerging neurobehavioral traits.

Fig 1

NODDI model components and representative maps of NODDI parameters.��A��The brain microstructure is modeled as three compartments: free water (FW), intra-neurite, and extra-neurite spaces. The free water fraction (FWF), neurite density index (NDI), and orientation dispersion index (ODI) are derived from these compartments.��B��Representative axial slices from NODDI-derived maps for FWF, NDI, and ODI. Note: Figure adapted from Kraguljac et al. (), licensed under Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0), available at��

Vigilance, or how alert and awake we are, constantly changes and affects our thinking and behavior. This state can be disrupted in many brain disorders. Certain areas deep in the brain, called neuromodulatory nuclei in the brainstem and basal forebrain, help regulate alertness and drive widespread brain activity and communication. However, it is not well understood how the brain’s large-scale networks change when we shift between being alert and drowsy.

In this study, we used simultaneous EEG (which measures brain electrical activity) and advanced fMRI scans to explore how these arousal centers connect with other parts of the brain depending on vigilance. We found that when people are drowsy, most of these nuclei show stronger global connections, especially to regions like the thalamus, precuneus, and sensory and motor areas. When people are more alert, the nuclei connect most strongly to networks involved in attention, internal thought, and hearing. These patterns remained consistent even after controlling for blood flow effects.

To confirm our findings, we analyzed two large brain imaging datasets and showed that these connectivity patterns are reproducible across different types of fMRI scans. Overall, this study provides new insights into how brain regions that regulate arousal influence large-scale brain activity depending on our level of alertness.

Fig 1 – Reproducible static connectivity profiles of neuromodulatory arousal centers. (a) Static functional connectivity (FC) t-maps of the locus coresuleus (LC), cuneiform/subcuneiform nucleus (CSC), and nucleus basalis of Meynert (NBM) in the VU 3T-ME, HCP 3T, and HCP 7T datasets for the mCSF/WM preprocessing pipeline. The FC t-maps were thresholded at 40% of the top t-values in the gray matter and at p < 0.05 (voxel-wise false discovery rate [FDR]-corrected over the entire gray matter volume). AFNI was used for visualization of the t-maps (@chauffeur_afni function; upper functional range set to the 98^th��percentile). (b) Spatial overlap of the thresholded static FC t-maps of the subcortical arousal regions with 16 canonical brain network templates from the FINDLAB and Melbourne atlases (Shirer et al., 2012;��Tian et al., 2020). A positive value for the spatial overlap corresponds to mostly positive correlations within the brain network template while a negative value corresponds to mostly negative correlations. (c) Spatial reproducibility (Dice similarity coefficient) of the thresholded static FC t-maps between the three fMRI datasets.

]]> /valiant/2025/08/20/optimizing-the-visualization-of-the-locus-coeruleus-using-magnetization-transfer-contrast-3d-imaging/ Wed, 20 Aug 2025 19:35:32 +0000 /valiant/?p=4957 Lyu, Haiying, He, Naying, Wu, Bo, Trujillo, Paula, Yan, Fuhua, Lu, Yong, & Haacke, Ewart Mark. (2025). “.” NeuroImage, 318, 121372.

The locus coeruleus (LC) is a very small but important part of the brain that produces norepinephrine, a chemical that helps regulate attention, arousal, and stress responses. Problems with the LC are linked to diseases like Alzheimer’s and Parkinson’s, as well as some psychiatric disorders. However, because the LC is tiny and located deep in the brainstem, it is difficult to capture clear images of it using standard brain scans. This study worked to improve MRI methods to make fast, high-quality images of the LC possible in less than five minutes.

Researchers tested an optimized MRI scanning method, called a 3D gradient echo sequence with magnetization transfer contrast, on 11 healthy volunteers (6 younger adults and 5 older adults). They measured brain tissue properties, ran simulations to find the best scanning settings, and then collected MRI images. Two independent experts reviewed the images and measured how well the LC could be seen compared to surrounding tissue. The size and shape of the LC were also measured. Each participant was scanned multiple times over three days to check how consistent the results were.

In total, 98 scans were collected. The improved scanning method produced very clear, high-resolution images of the LC, showing strong contrast compared to surrounding tissue and consistent results between different raters and across repeated scans. The LC could be seen across several slices of the brain, with measurements showing it was about 2 mm wide on the left side and slightly smaller on the right.

In conclusion, this optimized MRI technique makes it possible to reliably capture detailed images of the LC in under five minutes. This provides a valuable new tool for studying brain health and disease in both clinical and research settings.

Fig. 1.��Locus coeruleus (LC) MRI measurements. A)��Illustration of the methods used to calculate the relative contrast ratio (rCR), contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of the LC. Circular regions of interest (ROIs) were manually placed over the bilateral LCs and the adjacent background area on image zoomed by a factor of 8 (zoom-in three times resulted in zoom factor of 2³). To estimate regional noise, a subtraction image was generated by subtracting two repeated MTC scans acquired on the same day. The large red dashed ROI from the LC region (upper right of A) was copied onto the subtraction image (lower right of A) for noise estimation.��B)��Estimation of LC diameter using a diagonal line drawn across the LC, e.g., from the bottom left to the upper right, at approximately 45° on the zoomed image. This line extends through the LC and into the background region on the opposite side. The full width at half maximum (FWHM) and full width at quarter maximum (FWQM, 25%) were calculated to quantify LC dimensions, as shown on the right side of panel B.