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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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/global-cortical-arousal-effects-in-fmri-reveal-brain-markers-of-state-and-trait-anxiety/ Thu, 26 Mar 2026 19:30:32 +0000 /valiant/?p=6334 Kimberly Kundert-Obando; Terra Lee; Caroline G. Martin; Kamalpreet Kaur; Juan Gomez Lagandara; Yamin Li; Jeffrey M. Harding; Shiyu Wang; Richard Song; Ruoqi Yang; Rithwik Guntaka; Sarah E. Goodale; Roza G. Bayrak; Lucina Q. Uddin; Martin Walter; Jeremy Hogeveen; Catie Chang (2026).��.��Cerebral Cortex, 36(2), bhag008.��

This study explores how brain activity measured with functional MRI (fMRI) can help better understand and personalize the diagnosis and treatment of anxiety. Anxiety is not just a psychological experience—it also involves physical responses in the body, such as changes in heart rate and alertness (called��arousal). These bodily and brain-wide states can influence fMRI signals across the entire brain, often referred to as “global” signals. Traditionally, these global signals have been treated as noise or interference, but the researchers investigated whether they might actually contain meaningful information about anxiety.

To do this, the team analyzed fMRI data to identify patterns related to both��autonomic physiological activity��(automatic body functions like heart rate) and��cortical arousal��(how alert or activated the brain is). They then examined how these patterns relate to two types of anxiety:��state anxiety��(temporary, situation-based anxiety) and��trait anxiety��(a person’s general tendency to feel anxious). The results showed clear links between these global brain signals and both forms of anxiety, with certain brain regions showing stronger associations. These patterns overlapped with well-known brain networks, including the��default mode network, which is involved in self-reflection and internal thoughts.

Overall, the findings suggest that these global fMRI signals carry useful information about how anxiety is represented in the brain. This insight could help improve how anxiety is measured and understood, potentially leading to more personalized approaches to diagnosis and treatment.

Fig 1. Spatial association between global components and anxiety. a and b) Areas in which the FAI was significantly associated with state and trait

anxiety. c and d) Areas in which the GS was significantly associated with state and trait anxiety (GS was analyzed using a negative contrast). Maps show

the t-statistics thresholded at P <0.05 corrected.

This study looks at how measurements of the spinal cord—such as its��cross-sectional area��(the size of the cord when viewed in a slice)—can be used as important indicators (biomarkers) for diagnosing and tracking neurological diseases like multiple sclerosis or spinal cord compression. Modern artificial intelligence methods can automatically identify and outline (segment) the spinal cord in MRI scans, but it is unclear whether these measurements stay consistent as models are updated with new data over time. This consistency is especially important when building “normal” reference values from healthy individuals.

To address this, the researchers developed a spinal cord segmentation model trained on a large and diverse dataset collected from 75 sites and over 1,600 participants, covering different MRI types and various spinal cord conditions. They also created a “lifelong learning” system that continuously monitors changes in measurements (called��morphometric drift) whenever the model is updated. This system automatically runs through a workflow (via GitHub Actions, an automated coding tool) to track how measurements evolve over time.

The results showed that the new model performs very well, accurately identifying the spinal cord even in challenging cases such as severe compression or tissue damage, with a high Dice score (a measure of how closely the model’s segmentation matches the true anatomy) of 0.95. The monitoring system also proved useful for quickly detecting any changes in measurements between model versions. Importantly, the study found that updates to the model caused only minimal shifts in spinal cord measurements, meaning the results remain stable and reliable. This allowed the researchers to safely update an existing database of normal spinal cord measurements. Overall, this work provides a reliable and transparent way to maintain consistency in AI-based medical measurements as models evolve.

Fig 1

Overview of the dataset and image characteristics. Representative axial slices of nine contrasts and the total of images used for each contrast in brackets, the orientation (axial/sagittal) along with the median resolution of images. The respective doughnut chart illustrates the proportion of clinical status among the scanned participants, including healthy controls (HC), patients with radiologically isolated syndrome (RIS), patients with multiple sclerosis (MS), and their different phenotypes, including primary progressive (PPMS) and relapsing-remitting (RRMS), patients with amyotrophic lateral sclerosis (ALS), patients with neuromyelitis optica spectrum disorder (NMOSD), pre-decompression acute traumatic SCI (AcuteSCI), post-decompression traumatic spinal cord injury (SCI), degenerative cervical myelopathy (DCM), and syringomyelia (SYR; not shown). Labels indicate the phenotype associated with the patient, with their respective colors shared across contrast sets.

This study followed cognitively normal adults over time to determine which magnetic resonance imaging (MRI) biomarkers best predict future cognitive impairment. Researchers examined 154 different MRI-based measurements in 509 participants from the Baltimore Longitudinal Study of Aging who were age 50 or older and cognitively normal at the start of the study. Participants underwent repeated cognitive testing and 3 Tesla (3T) MRI scans, including T1- and T2-weighted imaging to assess brain structure and diffusion tensor imaging (DTI) to measure white matter microstructural integrity. The analyses accounted for factors such as age and other confounders and also examined differences by sex and amyloid beta (Aβ) status, a biological marker associated with Alzheimer’s disease.

Over an average follow-up of 4.6 years, individuals who later developed cognitive impairment showed greater declines in white matter integrity compared to those who remained cognitively stable. These changes were especially pronounced in major white matter tracts, including the corpus callosum, cingulum bundle, and inferior fronto-occipital fasciculus, which are pathways that connect different brain regions. To a lesser extent, thinning and atrophy in the temporal lobe were also linked to later impairment. The associations between brain changes and future cognitive decline were stronger in men and in individuals who were amyloid-positive.

Overall, the findings suggest that early changes in white matter microstructure, as measured by DTI, are particularly sensitive indicators of future mild cognitive impairment (MCI) and dementia. Certain MRI metrics may therefore be especially useful for identifying risk in people who are still cognitively normal.

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FIGURE 1

Study overview. Participants were selected from the BLSA neuroimaging substudy based on cognitively normal (CN) status and age 50 or older at baseline. The study data included longitudinal cognitive assessments, clinical diagnoses (Dx), 3T magnetic resonance imaging scans, and baseline plasma biomarkers related to Alzheimer’s disease and related dementias, specifically amyloid beta 42/40, collected between 2008 and 2019. The subsequently impaired (SI) group (also CN at baseline) included individuals who later developed mild cognitive impairment (MCI) or dementia or were “Impaired, not MCI/dementia.” Impairment onset dates ranged from 2012 to 2019 (≈1- to 9-year interval).

Stretchable radiofrequency (RF) coils could improve MRI scans by fitting closely to a patient’s body, enhancing both image quality and comfort. In this study, we explore a stretchable receive array based on the coaxial capacitor (COCA) coil for 7 T MRI, made mainly from ultra-flexible Litz wire stitched onto elastic fabric. The COCA design removes the need for traditional capacitors and keeps stable performance even when stretched, as long as the coil’s overlapped and overall areas stretch proportionally. Tests on bench setups and phantom models show that elliptical COCA coils maintain consistent behavior up to 1.3× stretching and provide better signal-to-noise ratio than fixed coils across different phantom sizes. This design shows strong promise for wearable coil arrays, offering better imaging quality and adaptability to different body shapes.

Fig 1:  (A) Circuit diagram and (B) photograph of a single 10-cm-diameter coaxial capacitor coil designed for 7��T MRI. The exceptional flexibility of the Litz wire, which forms the majority of the coil, is demonstrated by its ability to bend around a rod as thin as 1.6��mm (C).

Understanding how the pancreas normally changes in size and shape from infancy through old age is important for recognizing abnormal changes linked to type 2 diabetes and other pancreatic diseases. In this study, we measured pancreas morphology (size and shape) across the lifespan, from birth to age 90. Our goals were to identify reliable imaging methods for artificial intelligence (AI)-based pancreas measurement, establish normal aging patterns, and determine how type 2 diabetes may alter these patterns.

We analyzed abdominal computed tomography (CT) and magnetic resonance imaging (MRI) scans from 2,533 patients who did not have cancer, pancreatic disease, sepsis, or trauma. The scans were standardized to the same resolution, and the pancreas was automatically segmented using AI-based methods. We then extracted 13 morphological features of the pancreas.

First, we compared pancreas volume trends across contrast CT, non-contrast CT, and MRI in 1,858 control patients to determine which imaging method produced the most consistent lifespan patterns. CT was selected for the main analyses because MRI measurements differed when processed with our AI method in this clinical dataset. Next, we established normative aging patterns in pancreas morphology by age group and sex. Finally, we used statistical modeling (GAMLSS regression) to compare 675 patients with type 2 diabetes to 675 age- and sex-matched non-diabetic controls.

After adjusting for other factors, 10 of the 13 morphological features showed significantly different aging trends in people with type 2 diabetes compared to controls. Overall, the pancreas was smaller in individuals with type 2 diabetes, confirming previous findings. This study provides a large clinical reference of normal pancreas morphology across the lifespan and shows how type 2 diabetes is associated with measurable changes in pancreas size and shape.

FIGURE 1

The pancreas undergoes structural changes with age, including atrophy and fat infiltration. While population-level pancreas volume and fat content have been examined across the aging process (Saisho et��al.��), there remains a knowledge gap in understanding age-related changes in the pancreas across a broader set of morphological measurements. Moreover, type 2 diabetes may cause changes in pancreas morphology that differ from normal aging. The two scans on the left illustrate age-related appearance differences in non-diabetic patients but are not from the same patient. The rightmost scan shows the pancreas from an elderly patient with type 2 diabetes. Understanding pancreas variation in normal aging is critical for understanding differences in type 2 diabetes. Any potential differences in the aging trends of the pancreas in type 2 diabetes may not necessarily be linear or smooth.

Medical imaging, such as MRI and CT scans, has a notable environmental footprint due to energy use, equipment production, and disposable supplies. This study evaluated the environmental impact of MRI and CT services at a large academic medical center in the Southeastern United States over one year using life cycle assessment methods. Researchers collected data from direct observation, records, staff interviews, and energy metering, and assessed impacts with established environmental databases and software.

Results showed that MRI and CT services produced an estimated 221 and 108 tons of carbon dioxide equivalent annually—comparable to the emissions of 52 and 25 cars driven for a year, respectively. Energy use contributed most to emissions (58% for MRI, 33% for CT), followed by disposable supplies, equipment production, and linens. Switching to solar power could cut MRI emissions by 70% and CT emissions by 40%, though the relative contribution of supplies and equipment would then become more significant.

These findings highlight the importance of energy consumption in imaging services and suggest that renewable energy adoption, efficient scanner use, reusable supplies, and circular business practices—such as extending equipment life—can meaningfully reduce the environmental impact of medical imaging.

Fig. 1��Flow diagram of components included in the study. ∗This study could not account for the production of all additional capital equipment. See e-only����here and in the previous publication [] for more information

In heart imaging studies, “normal” values are usually defined using a simple statistical rule: the average value from a healthy group, plus or minus two standard deviations (the “2-SD method”). This approach is widely used in cardiac magnetic resonance (CMR) imaging, but it has not been well tested to see whether values outside these ranges actually predict future heart problems. The goal of this study was to evaluate whether commonly used CMR reference ranges are clinically meaningful—that is, whether they are linked to later cardiovascular (CV) events.

Researchers first established normal reference ranges for measurements of the left and right sides of the heart using CMR data from 1,518 healthy adults in the Multi-Ethnic Study of Atherosclerosis (MESA) who had no known cardiovascular disease or risk factors. These measurements included heart chamber volumes, heart muscle mass, wall thickness, and pumping function (ejection fraction), adjusted for body size. Cut-off values at 1 and 2 standard deviations from the mean were defined. The investigators then examined whether people in the full MESA cohort with CMR data (4,915 participants, including those with CV risk factors) who had values outside these ranges were more likely to experience cardiovascular events over 5 and 10 years of follow-up.

The results showed that several left-ventricular (LV) measurements—such as larger chamber volumes and reduced pumping function—above or below the 2-SD threshold were linked to a higher risk of major and overall cardiovascular events in both men and women. In men, increases in LV muscle mass and wall thickness even beyond the 1-SD threshold were associated with higher risk, while in women, thicker LV walls at 1 SD and higher LV muscle mass at 2 SD were linked to adverse outcomes. In contrast, several right-ventricular measurements and LV end-diastolic diameter were not associated with future cardiovascular events. Similar patterns were seen when outcomes were assessed over 10 years.

Overall, this study supports the clinical usefulness of standard CMR reference ranges for many left-ventricular measurements. Values outside the commonly used “normal” range, especially beyond the 2-SD threshold, were meaningfully associated with increased cardiovascular risk, whereas some other commonly measured parameters were not predictive of future events.

Fig. 1.��Study participant flow chart.��MESA��multi-ethnic study in atherosclerosis,��CMR��cardiovascular magnetic resonance,��LV��left ventricle,��RV��right ventricle

Diagnosing metabolic dysfunction-associated steatohepatitis (MASH) currently relies on a liver biopsy, an invasive procedure used to assess fat buildup (steatosis), inflammation, and damaged liver cells (ballooning). Although MRI techniques such as proton density fat fraction (PDFF) and MR elastography can non-invasively measure liver fat and scarring (fibrosis), there are still no reliable imaging methods to directly evaluate liver inflammation without a biopsy.

In this study, we developed a new diffusion MRI (dMRI)–based approach called MR cytometry to non-invasively map basic cellular features of the liver. This technique estimates MRI-derived cell size (excluding fat) and cell density, which reflect underlying tissue microstructure. We first validated the method using simulations guided by histology and by imaging fixed human liver samples outside the body. These tests showed that stromal regions of the liver have smaller apparent cell sizes and higher cell densities compared with normal liver tissue and fat-rich tissue.

We then tested the feasibility of MR cytometry in living people using a standard clinical 3-tesla MRI scanner, scanning both healthy volunteers and patients with MASH. The results demonstrate that MR cytometry can detect differences in liver microstructure associated with disease, suggesting that this approach may provide a promising non-invasive way to characterize liver inflammation and related cellular changes in MASH.

Fig. 1: Comparison of MRI-Derived cellular properties from Histology-Based Simulations Across Tissue Types and SNR Levels.

Fitted cell sizes (top row) and densities (bottom row) from histology-based simulated diffusion signals for normal liver tissues, steatosis, and stroma (a combination of inflammatory cells and fibrosis) at three different SNR levels (10, 20, and 50, from left to right). *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 as measured by one-way analysis of variance (ANOVA) with Bonferroni correction.