Magnetic resonance imaging (MRI)��often uses��inductively coupled coils—small receiver coils placed near the area being imaged—to improve image quality. However, when these coils are positioned close to the scanner’s primary coil, they can interfere with each other, causing effects that have traditionally been viewed as reducing image quality. This study investigated why inductively coupled coils can still perform well despite this strong interaction and examined the role of modern MRI��preamplifiers��(electronic components that amplify weak signals from the coils). The researchers tested different coil configurations and preamplifier settings in laboratory experiments and validated their findings with MRI scans at 7 tesla, a high-field MRI system. They found that modern low-input-impedance preamplifiers largely prevented the signal losses typically associated with strong coil coupling, allowing the secondary coils to function effectively even when placed very close to the primary coil. Although the interaction between the coils altered the electrical properties of the primary coil, it had little effect on the overall��signal-to-noise ratio (SNR), a key measure of image quality. In contrast, reducing the effectiveness of the preamplifiers led to a 21%–23% decrease in SNR. These findings suggest that modern preamplifiers play a critical role in maintaining MRI performance and could simplify the design of inductively coupled coils for future imaging systems.

FIGURE 1

(A) Setup and results of measuring the impedance of a 10-cm-diameter circular 7 T RF coil on a bottle phantom. (B) Setup and results of the same coil (primary coil) when a smaller 5-cm-diameter inductively coupled coil was placed underneath the primary coil but above the phantom. The primary coil was not retuned or rematched after introducing the inductively coupled coil. (C) Simplified equivalent circuit model of the coupled inductively coupled and primary coils illustrating resonance splitting due to strong mutual coupling.

Swelling around��brain tumors��can make it difficult to identify nearby��white matter—the bundles of nerve fibers that carry signals between different parts of the brain—on standard��magnetic resonance imaging (MRI). This can complicate surgical planning by making it harder to determine the safest path for removing a tumor while preserving important brain connections. In this proof-of-concept study, the researchers evaluated whether advanced��diffusion MRI��techniques, which model how water moves through different microscopic tissue compartments, could better identify white matter in areas affected by swelling (edema). Using MRI data from five patients with��meningiomas��(typically benign brain tumors), they compared conventional��diffusion tensor imaging (DTI)��with two advanced methods:��Neurite Orientation Dispersion and Density Imaging (NODDI)��and the��Standard Model (SM). The advanced techniques preserved measures of white matter organization in swollen tissue much better than standard DTI and more successfully traced white matter pathways through these regions. These findings suggest that biophysical diffusion MRI models may improve the mapping of critical white matter tracts before brain surgery, helping surgeons better plan procedures in patients with tumors surrounded by edema.

Figure 1.��Representative (A) post-contrast T₁-weighted images, (B) T2-weighted FLAIR images, (C) FA maps, (D) single-shell FW-FA maps, (E) multi-shell FW-FA maps, (F) ODI maps, and (G) P₂��maps are shown. Post-contrast T₁-weighted images best reflect tumor location, and T₂-weighted FLAIR images best reflect tumor plus edema location. DTI’s FA (where WM is typically represented by a brighter signal intensity) fails to identify WM tracts through regions of edema (seen as hyperintense signal traced in T₂-FLAIR images), whereas NODDI’s ODI (where WM is represented by a darker signal intensity) retains WM structure irrespective of edema presence. Similarly, the SM’s P₂map (where WM is typically represented by a brighter signal intensity) succeeds. Representative image planes were chosen on a per-patient basis to best reflect the lesion.

Multiple sclerosis (MS)��affects both the brain and��spinal cord, but these areas are often studied separately using��magnetic resonance imaging (MRI). This study examined whether tissue damage develops in similar ways across both regions and whether an advanced MRI technique could provide more useful information than conventional imaging methods. The researchers used the same MRI session to image the brain and cervical (neck) spinal cord of 34 people with��relapsing-remitting MS��and 36 healthy volunteers. They compared traditional��diffusion tensor imaging (DTI)��with a newer technique called��standard model imaging with free water (SMIfw), which provides more detailed information about the brain’s and spinal cord’s microscopic structure. Both methods detected MS-related changes, but their performance differed depending on the region being studied. A measure derived from SMIfw, called��neurite density fraction, consistently identified MS-related tissue damage in both the brain and spinal cord, while commonly used DTI measures performed as well only in the brain. The findings also suggest that although damage to nerve fibers is a common feature of MS throughout the central nervous system, the inflammatory environment surrounding lesions differs between the brain and spinal cord. Overall, the results indicate that SMIfw could improve the assessment of MS across the entire central nervous system and may be valuable for future clinical trials and disease monitoring.

FIGURE 1

All diffusion models can be roughly summarized similar concepts, which can be captured either by “sticks” or cylinders, spheres with isometric diffusion, or diffusion tensors with directional diffusion. In this depiction, SMI represents both SMI and SMIfw models, with SMIfw including a��f_w��measure not otherwise included in the SMI model.

��This study examined whether a blood or fluid test for abnormal alpha-synuclein, a protein linked to Parkinson’s disease, was related to differences in brain scans in people with early Parkinson’s disease. The researchers used diffusion MRI, a type of brain imaging that can show how water moves through brain tissue and reveal subtle structural changes, and focused on a measure called free-water imaging, which can pick up signs of tissue damage or inflammation. They compared people who tested positive for alpha-synuclein seeding activity, meaning the biomarker was present, with those who tested negative. Among 462 participants, most had a positive test. People with a positive result were more likely to have loss of smell and a shorter time since their movement symptoms began. The brain scan analysis found one small difference in a pathway linked to movement, but overall the positive and negative groups were not very different on the broader scan measures. In other words, the biomarker confirmed Parkinson’s-related protein changes, but it did not strongly separate people by the degree of brain tissue change seen on these scans.

FIGURE 1

CONSORT flow diagram of participant selection. The numbers of individuals assessed for eligibility and included in each analysis group are shown. AIDP = Automated Imaging Differentiation for Parkinsonism; PPMI = Parkinson’s Progression Markers Initiative; SAA = seed amplification.

Magnetic resonance imaging, or MRI, is an important tool for finding and tracking spinal cord lesions in people with multiple sclerosis (MS), which are areas of damage caused by the disease. But automatic computer methods for detecting and outlining these lesions often work well only for one MRI type or one hospital’s scanning setup, which makes them less useful in real clinics where scan methods vary a lot. To address this, the researchers developed a more robust segmentation system, meaning a model that can automatically identify lesion boundaries, across many MRI contrasts and imaging sites. They trained and tested it on a large dataset of 4,428 annotated images from 1,849 people with MS across 23 imaging centers, using six different MRI contrast types and scans taken at 1.5, 3, and 7 tesla, which refers to the strength of the MRI scanner. Compared with existing methods that are designed for only one contrast type, the new model generalized better across different scan settings, according to neuroradiologist ratings. It also remained strong when tested across different spinal cord levels, image resolutions, threshold settings, and external datasets. Overall, the study shows that this approach can detect spinal cord MS lesions accurately and reliably across diverse MRI data, which is an important step toward making automated lesion analysis more useful in everyday clinical care.

Figure 1. Sankey diagram of annotated MRI scans across clinical sites. Line thickness is associated with the number of scans.

MRI scan distribution is clustered per acquisition type (3D, 2D sagittal, or 2D axial) and per MRI contrast, for each site.

Magnetic resonance imaging, or MRI, is often collected as a stack of 2D slices because that can make scans faster and improve image quality for clinical use. But when software tries to analyze these scans as if they were full 3D images, it can struggle, especially when the slices are thick or have gaps between them. To address this, the researchers developed ECLARE, a new method that improves the resolution of these slice-based MRI scans without needing outside training data. ECLARE first estimates the shape of each slice’s signal, then learns from the image itself how to turn lower-resolution parts into higher-resolution ones, while also correcting for blur and making sure the image is resampled in a way that respects the original field of view. The method was tested on brain MRI data, including healthy T1-weighted scans and T2-FLAIR scans from people with multiple sclerosis, and compared with several existing image-enhancement methods. Across scans with slice thicknesses up to 5 mm and gaps up to 1.5 mm, ECLARE produced more accurate and visually similar images than the alternatives, including in important brain regions such as the ventricles, caudate, and white matter. Overall, the study suggests that ECLARE can make thick-slice MRI images more useful for 3D analysis, which could improve downstream tools that rely on detailed brain structure.

Fig.��1

Flowchart of our proposed method. The anisotropic input volume is fed independently into each of the three steps. First, in panel a (Sec.��), we estimate the slice excitation profile with ESPRESO.��Second, in panel b (Sec.��), we extract HR in-plane 2D patches and use the PSF estimated from panel a to create paired training data. This training data are used to train the network��𝑓𝜃��with supervised learning. Third, in panel c (Sec.��), we extract LR through-plane 2D slices and superresolve them with the trained network��𝑓𝜃��from panel b. The superresolved slices are stacked and averaged, yielding the superresolved output volume.

Language comprehension is a complex mental process that depends on several brain networks working together over very short and longer time scales. One challenge in studying this process is that different brain imaging methods have different strengths: some show where activity happens better, while others show when it happens better. To get around this, the researchers combined functional MRI, which shows which brain areas are active, with EEG, which records the brain’s electrical activity, and used a mathematical tool called Continuous Wavelet Transform to examine changes in brain activity frequencies in the second after a word or sentence was presented. They compared natural language passages with scrambled words and found three brain network patterns that were more active during meaningful language processing. These included the main language network, a left-sided part of the default mode network, which is a set of brain regions often involved in internally directed thought, and another default mode subnetwork in both sides of the brain. Each network had its own “frequency fingerprint”: the language network was linked to longer-lasting theta activity along with bursts of beta and gamma activity, the first default mode network showed beta and gamma bursts, and the second default mode network was dominated by alpha activity. These patterns also related to language ability: differences in the language network’s frequency pattern were associated with how well people remembered what they read or heard, and reading comprehension depended partly on how strongly the language network and the alpha-dominant default mode network worked together. Overall, the study suggests that brain networks involved in language have distinct patterns of electrical activity that change over time and may help explain differences in language skill.

Fig. 1.��Stimuli presentation and fused fMRI-EEG frequency analysis. (A) Presentation of expository passages and non-sequential word baseline during both fMRI and EEG acquisition. (B) Fused fMRI-EEG analysis on subject-level inputs including passage (Pass) and word baseline (WB) to generate independent fused source components with subject weight loadings. (C) Continuous wavelet transform analysis on the EEG joint components to characterize frequency power over time throughout the post-stimulus window.

This study looked at how to make very high-resolution 7T diffusion MRI scans more accurate and reliable. Diffusion MRI is used to study the brain’s white matter pathways, but at such high field strength the images can be distorted, which can reduce anatomical accuracy and make results less reproducible. The researchers tested different ways of collecting and correcting the scans by using five healthy adults who each had two MRI sessions. They compared several methods, ranging from uncorrected scans to more advanced approaches that used multiple phase-encoding directions, which are different ways of collecting the image data to help correct distortion. They then checked how well each method lined up with standard anatomical MRI images and how consistent the measurements were when the scan was repeated. All of the correction methods improved image accuracy compared with uncorrected scans, but using a full set of reversed phase-encoding images worked better than the common approach of using only one reversed reference image. The best results came from using a four-direction phase-encoding scheme, which produced the most accurate images and the most reproducible measurements. This approach also allowed the researchers to reconstruct both large and very fine white matter pathways with high quality. Overall, the study shows that collecting diffusion MRI data in multiple directions is important for getting dependable results from high-resolution 7T brain scans.

Fig. 1.��Methodology. The highly oversampled acquisition (top) enabled creation of subset combinations of nine time-equivalent 10��min acquisitions (bottom). In the schematic, short blocks denote b��=��0 volumes and long blocks denote diffusion-weighted volumes (DWIs). Color encodes the phase-encoding axis (blue��=��AP/PA; red��=��LR/RL), and the shading direction within each block indicates phase-encoding polarity (e.g., AP vs PA; LR vs RL). A full 10��min acquisition includes 64 uniformly distributed diffusion weighted directions (with b��=��0 images interspersed every 16 volumes). This is repeated once for each of the four PE directions (AP, PA, RL, and LR). The nine corrected acquisitions depicted here fall into four categories: (1–4) single reverse PE b��=��0 (PA, AP, RL, LR); (5–6) full blip-up/blip-down with unique DWIs (AP-PA, RL-LR); (7–8) full blip-up/blip-down with repeated DWIs (AP-PAr, RL-LRr); (9) -way PE (AP-PA-RL-LR). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

]]> /valiant/2026/05/26/quantitative-imaging-of-iron-dysregulation-in-multiple-system-atrophy/ Tue, 26 May 2026 20:59:08 +0000 /valiant/?p=6780 Trujillo, Paula.; Hett, Kilian.; Cooper, Amy.; Brown, Amy E.; Donahue, Manus J.; McKnight, Colin D.; Bradbury, Margaret.; Wong, Cynthia.; Stamler, David.; Claassen, Daniel O. (2026).��.��NeuroImage, 334, 121965.��

Multiple system atrophy, or MSA, is a fast-moving brain disease that can be hard to diagnose early and monitor over time. This study looked at whether a special kind of MRI scan called quantitative susceptibility mapping, or QSM, can detect abnormal iron buildup in the brain, since iron imbalance may play a role in MSA and could be useful for tracking the disease. The researchers scanned 38 people with MSA, including 10 with early-stage disease who were followed again after 12 months, along with 43 people with Parkinson’s disease and 23 healthy adults of similar age. They measured iron-related changes in several brain regions, including the substantia nigra, globus pallidus, putamen, and dentate nucleus. Compared with both Parkinson’s disease and healthy controls, people with MSA had higher iron-related signal changes in the globus pallidus and substantia nigra, with smaller changes in the putamen. A more sensitive analysis that focused on higher values within each region detected these differences better than simple median measurements, suggesting it was better at picking up small, localized areas of iron buildup. Higher iron levels in the globus pallidus were also linked to worse clinical symptoms. In the 12-month follow-up, iron-related changes increased in the substantia nigra and globus pallidus, showing that these abnormalities can worsen over time. Overall, the findings suggest that QSM MRI may be a useful way to help diagnose MSA earlier, follow disease progression, and evaluate treatments aimed at reducing iron-related damage.

Fig. 1.��Representative QSM images from each diagnostic group.Quantitative susceptibility maps (QSM) for representative female participants matched for age across cohorts: a healthy control (HC, 67 years), Parkinson’s disease (PD, 62 years), PD from the bioMUSE study (69 years), multiple system atrophy (MSA) from bioMUSE (65 years), and MSA from the cross-sectional cohort (72 years). The PD (bioMUSE) participant was initially enrolled as MSA but later reclassified as PD based on longitudinal clinical evaluation. The MSA (bioMUSE) participant represents an early-stage case, whereas the MSA (cross-sectional) participant had more advanced disease. Each row displays a different subcortical region: the putamen (PT) and globus pallidus (GP) (top), the substantia nigra (SN) (middle), and the dentate nucleus (DN) (bottom). The leftmost column shows the QSM overlaid on the T1-weighted image to provide anatomical context. The second column shows the zoomed QSM for the HC participant with atlas-derived ROI outlines in red and anatomical labels. The remaining columns show the zoomed QSM for each diagnostic group. White arrows indicate the structures of interest in each row. Increases in magnetic susceptibility (brighter signal) are visible in the PT, GP, and SN in MSA participants compared with HC and PD. Susceptibility values are displayed in parts per million (ppm) using a grayscale colormap windowed from −0.1 to 0.2 ppm.

This study focuses on improving how passive resonators—devices used in MRI scanners to shape and strengthen radiofrequency (RF) fields—are designed and optimized. Normally, designing these structures requires��full-wave electromagnetic (EM) simulations, which model how RF fields behave in detail. While accurate, these simulations are extremely slow and computationally expensive, especially when many design variables (like different capacitor or inductor values) need to be tested.

To solve this problem, the researchers developed a faster method called a��co-simulation framework, which combines a single detailed EM simulation with simpler circuit-level calculations. In this approach, parts of the resonator are replaced with connection points (“ports”) during the initial simulation, allowing many different electrical configurations to be tested afterward without repeating the costly EM computation. They also integrated a��genetic algorithm��(a search method inspired by natural selection) to automatically explore thousands of design options and find the best configuration for enhancing RF fields in a specific target area.

The method was tested in several scenarios, from simple models to a realistic human head model, and produced results nearly identical to full EM simulations (with less than 1% error). Importantly, the optimization process took less than five minutes, compared to what would normally require extremely long computation times. Overall, this approach offers a much faster and scalable way to design passive MRI components, making it easier to improve image quality without the heavy computational cost of traditional methods.

Fig. 1.��Schematic diagram of the co-simulation principle. Incorporate the optimization stage, indicate the starting point of the method and reorganized the layout.

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