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Purpose: Although conventional multi-echo gradient-echo (GRE) sequences effectively quantify short and intermediate T* in brain tissue, and general interest in cerebrospinal fluid (CSF) is growing due to its association with the glymphatic system, quantifying T* in CSF remains underexplored. Accurate quantification of the slow-relaxing water pools requires imaging at long echo times, significantly increasing acquisition time. This study proposes a novel sequence capable of quantifying the entire range of T* without prolonged acquisition time, mapping T* in both CSF and brain tissue.
Methods: The proposed echo-shifted, multi-echo GRE (ES-mGRE) combines the conventional multi-echo GRE sequence with an echo-shifting technique. Additional gradients are introduced, producing echoes in the next sub-repetition time interval.
Results: ES-mGRE generates artifact-free images at both short and long echo times without extending acquisition time. Increasing the area of the additional gradients enhances diffusion sensitivity, allowing simultaneous quantification of T* and D in CSF. The mean T* of white matter and gray matter is 55.9 ms and 51.5 ms at 3 T, respectively. The mean T* in the ventricles is 234.5 ms. The simultaneously quantified mean D value of 3.07 μm/ms is closely aligned with the reference diffusivity.
Conclusion: We demonstrate that the proposed ES-mGRE sequence can effectively quantify the T* of both CSF and brain tissue while also providing simultaneous diffusion information.
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http://dx.doi.org/10.1002/mrm.30624 | DOI Listing |
Radiol Adv
September 2024
Department of Radiology, Northwestern University and Northwestern Medicine, Chicago, IL, 60611, United States.
Background: In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact resulting from voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to register the background DSA mask to subsequent postcontrast images rely on key point registration using iterative optimization, which has limited real-time application.
Purpose: Leveraging state-of-the-art, unsupervised deep learning, we aim to develop a fast, deformable registration model to substantially reduce DSA misregistration in craniocervical angiography without compromising spatial resolution or introducing new artifacts.
Front Neural Circuits
September 2025
Neuroscience Institute, National Research Council (CNR), Pisa, Italy.
Neural circuits sculpt their structure and modify the strength of their connections to effectively adapt to the external stimuli throughout life. In response to practice and experience, the brain learns to distinguish previously undetectable stimulus features recurring in the external environment. The unconscious acquisition of improved perceptual abilities falls into a form of implicit learning known as perceptual learning.
View Article and Find Full Text PDFMed Phys
September 2025
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Background: Four-dimensional magnetic resonance imaging (4D-MRI) holds great promise for precise abdominal radiotherapy guidance. However, current 4D-MRI methods are limited by an inherent trade-off between spatial and temporal resolutions, resulting in compromised image quality characterized by low spatial resolution and significant motion artifacts, hindering clinical implementation. Despite recent advancements, existing methods inadequately exploit redundant frame information and struggle to restore structural details from highly undersampled acquisitions.
View Article and Find Full Text PDFNMR Biomed
October 2025
Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Understanding gastric physiology in rodents is critical for advancing preclinical neurogastroenterology research. However, existing techniques are often invasive, terminal, or limited in resolution. This study aims to develop a non-invasive, standardized MRI protocol capable of capturing whole-stomach dynamics in anesthetized rats with high spatiotemporal resolution.
View Article and Find Full Text PDFJ Robot Surg
September 2025
Department of General Surgery, Giglio Hospital Foundation, Cefalu', Italy.
The adoption of robotic pancreatectomy has grown significantly in recent years, driven by its potential advantages in precision, minimally invasive access, and improved patient recovery. However, mastering these complex procedures requires overcoming a substantial learning curve, and the role of structured mentoring in facilitating this transition remains underexplored. This systematic review and meta-analysis aimed to comprehensively evaluate the number of cases required to achieve surgical proficiency, assess the impact of mentoring on skill acquisition, and analyze how outcomes evolve throughout the learning process.
View Article and Find Full Text PDF