Magnetic Resonance Imaging (MRI) resolution continues to improve, making it important to understand the cellular basis for different MRI contrast mechanisms. Manganese-enhanced MRI (MEMRI) produces layer-specific contrast throughout the brain enabling in vivo visualization of cellular cytoarchitecture, particularly in the cerebellum. Due to the unique geometry of the cerebellum, especially near the midline, 2D MEMRI images can be acquired from a relatively thick slice by averaging through areas of uniform morphology and cytoarchitecture to produce very high-resolution visualization of sagittal planes. In such images, MEMRI hyperintensity is uniform in thickness throughout the anterior-posterior axis of sagittal sections and is centrally located in the cerebellar cortex. These signal features suggested that the Purkinje cell layer, which houses the cell bodies of the Purkinje cells and the Bergmann glia, is the source of hyperintensity. Despite this circumstantial evidence, the cellular source of MRI contrast has been difficult to define. In this study, we quantified the effects of selective ablation of Purkinje cells or Bergmann glia on cerebellar MEMRI signal to determine whether signal could be assigned to one cell type. We found that the Purkinje cells, not the Bergmann glia, are the primary of source of the enhancement in the Purkinje cell layer. This cell-ablation strategy should be useful for determining the cell specificity of other MRI contrast mechanisms.
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http://dx.doi.org/10.1016/j.neuroimage.2023.120198 | DOI Listing |
BMC Med Imaging
January 2025
Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Purpose: We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs).
Methods: 279 features were extracted from each ROI including 9 histogram features, 220 Gy-level co-occurrence matrix features, 20 Gy-level run-length matrix features, 5 auto-regressive model features, 20 wavelets transform features and 5 absolute gradient statistics features. The datasets were randomly divided into two groups, the training set (~ 70%) and the test set (~ 30%).
Nat Biomed Eng
January 2025
Developing Brain Computing Lab, Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
In magnetic resonance imaging of the brain, an imaging-preprocessing step removes the skull and other non-brain tissue from the images. But methods for such a skull-stripping process often struggle with large data heterogeneity across medical sites and with dynamic changes in tissue contrast across lifespans. Here we report a skull-stripping model for magnetic resonance images that generalizes across lifespans by leveraging personalized priors from brain atlases.
View Article and Find Full Text PDFNPJ Syst Biol Appl
January 2025
Center for Interdisciplinary Digital Sciences (CIDS), Department Information Services and High-Performance Computing (ZIH), Dresden University of Technology, 01062, Dresden, Germany.
Predicting the biological behavior and time to recurrence (TTR) of high-grade diffuse gliomas (HGG) after maximum safe neurosurgical resection and combined radiation and chemotherapy plays a pivotal role in planning clinical follow-up, selecting potentially necessary second-line treatment and improving the quality of life for patients diagnosed with a malignant brain tumor. The current standard-of-care (SoC) for HGG includes follow-up neuroradiological imaging to detect recurrence as early as possible and relies on several clinical, neuropathological, and radiological prognostic factors, which have limited accuracy in predicting TTR. In this study, using an in-silico analysis, we aim to improve predictive power for TTR by considering the role of (i) prognostically relevant information available through diagnostics used in the current SoC, (ii) advanced image-based information not currently part of the standard diagnostic workup, such as tumor-normal tissue interface (edge) features and quantitative data specific to biopsy positions within the tumor, and (iii) information on tumor-associated macrophages.
View Article and Find Full Text PDFEur Radiol
January 2025
Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Background: Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (MRE), is well-accepted as a surrogate marker of liver fibrosis.
Purpose: To develop and validate deep learning (DL) models for predicting MRE-derived liver stiffness using routine clinical non-contrast abdominal T1-weighted (T1w) and T2-weighted (T2w) data from multiple institutions/system manufacturers in pediatric and adult patients.
Eur Radiol
January 2025
Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.
Objectives: We hypothesized that semiquantitative visual scoring of lung MRI is suitable for GOLD-grade specific characterization of parenchymal and airway disease in COPD and that MRI scores correlate with quantitative CT (QCT) and pulmonary function test (PFT) parameters.
Methods: Five hundred ninety-eight subjects from the COSYCONET study (median age = 67 (60-72)) at risk for COPD or with GOLD1-4 underwent PFT, same-day paired inspiratory/expiratory CT, and structural and contrast-enhanced MRI. QCT assessed total lung volume (TLV), emphysema, and air trapping by parametric response mapping (PRM, PRM) and airway disease by wall percentage (WP).
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