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Purpose: Studies at 3T have shown that T relaxometry enables characterization of brain tissues at the single-subject level by comparing individual physical properties to a normative atlas. In this work, an atlas of normative T values at 7T is introduced with 0.6 mm isotropic resolution and its clinical potential is explored in comparison to 3T.
Methods: T maps were acquired in two separate healthy cohorts scanned at 3T and 7T. Using transfer learning, a template-based brain segmentation algorithm was adapted to ultra-high field imaging data. After segmenting brain tissues, volumes were normalized into a common space, and an atlas of normative T values was established by modeling the T inter-subject variability. A method for single-subject comparisons restricted to white matter and subcortical structures was developed by computing Z-scores. The comparison was applied to eight patients scanned at both field strengths for proof of concept.
Results: The proposed method for morphometry delivered segmentation masks without statistically significant differences from those derived with the original pipeline at 3T and achieved accurate segmentation at 7T. The established normative atlas allowed characterizing tissue alterations in single-subject comparisons at 7T, and showed greater anatomical details compared with 3T results.
Conclusion: A high-resolution quantitative atlas with an adapted pipeline was introduced and validated. Several case studies on different clinical conditions showed the feasibility, potential and limitations of high-resolution single-subject comparisons based on quantitative MRI atlases. This method in conjunction with 7T higher resolution broadens the range of potential applications of quantitative MRI in clinical practice.
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http://dx.doi.org/10.1002/mrm.29540 | DOI Listing |
IEEE Trans Biomed Eng
September 2025
Objective: Imposing accurate outflow boundary conditions remains a significant challenge in 3D computational fluid dynamics simulations of patient-specific cerebral blood flow. Widely used Windkessel models often rely solely on geometric factors, such as outlet numbers and diameters, leading to inaccuracies caused by image quality limitations and simplified vessel representations. This preliminary study proposes a novel functional region-based approach to enhance the accuracy of cerebral blood flow simulations.
View Article and Find Full Text PDFCompr Psychiatry
October 2025
Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary. Electronic address:
While traditional electrophysiological measures such as ERPs have been successfully used to study individual differences, their signal-to-noise ratio often limits the precision of single-subject analyses. To address this, the present study had two primary aims: (1) to explore the utility of steady-state visual evoked potential (SSVEP) method for investigating addiction-specific cognitive processes and (2) to examine the relationship between the imbalance of self-reported wanting and liking, and the neural correlates of tobacco smoking-related attentional bias. A total of 39 participants, including smokers (N = 22) and non-smokers (N = 17), were exposed to a passive visual oddball paradigm comprising rapidly flickering stimuli under two conditions: neutral (e.
View Article and Find Full Text PDFImaging Neurosci (Camb)
July 2025
Scientific Institute IRCCS Eugenio Medea, Neuroimaging Unit, Bosisio Parini, Italy.
Functional magnetic resonance imaging (fMRI) studies in cognitive and clinical neuroscience rely on blood oxygenation level-dependent (BOLD) contrast, measured with single-shot gradient-echo-planar imaging. However, conventional schemes encompass the acquisition of single-echo fMRI, which samples a single echo at a single-echo time (TE), facing limitations in disentangling neural signals from artifacts. Multi-echo (ME) fMRI captures images at multiple echo times within a single repetition time (TR) period and enables the separation of BOLD and non-BOLD signal components.
View Article and Find Full Text PDFJ Transl Med
August 2025
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
Background: Automated seizure detection based on scalp electroencephalography (EEG) can significantly accelerate the epilepsy diagnosis process. However, most existing deep learning-based epilepsy detection methods are deficient in mining the local features and global time series dependence of EEG signals, limiting the performance enhancement of the models in seizure detection.
Methods: Our study proposes an epilepsy detection model, CMFViT, based on a Multi-Stream Feature Fusion (MSFF) strategy that fuses a Convolutional Neural Network (CNN) with a Vision Transformer (ViT).
Neuroimage Clin
July 2025
Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Freiburg, Germany; Medical Faculty of Freiburg University, Germany; Department of Diagnostic and Interventional Radiology, Medical Physics, Medical Center - University of Freiburg, Germany. Electronic addr
Purpose: Imaging developments optimizing stereotactic surgery for tremor have yielded white matter attenuating sequences (FGATIR/FLAWS) showing a targetable hypointensity in the subthalamic region (rubral wing, RW). RW has been reported to coincide with yet to be defined portions of the dentato-rubro-thalamic tract (DRT). Its discernibility on the single subject level might be hampered by low signal-to-noise ratio (SNR), potentially compromising with surgical outcomes.
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