Midsagittal images of the brain provide a wealth of anatomic information and may show abnormalities that are pathognomonic for particular diagnoses. Using an anatomy-based approach, the authors identify pertinent anatomic structures to serve as a checklist when evaluating these structures. Subregions evaluated include the corpus callosum, pituitary gland and sellar region, pineal gland and pineal region, brainstem, and cerebellum. The authors present 25 conditions with characteristic identifiable abnormalities at midsagittal imaging. Midsagittal views from multiple imaging modalities are shown, including computed tomography, ultrasonography, and magnetic resonance (MR) imaging. Standard MR imaging sequences are shown, as well as fetal MR and sagittal diffusion-weighted images. To demonstrate these conditions, fetal, neonatal, childhood, adolescent, and young adulthood images are reviewed. The differentiation of normal variants is guided by the understanding of anatomy and pathology. When a specific diagnosis is not possible, the authors present information to evaluate differential considerations and discuss when follow-up imaging may be indicated. The authors hope each case will clarify a pertinent differential diagnosis, appropriately guide patient management, and improve understanding of normal anatomy and identification of pathologic entities. It is in these hopes that the authors have presented a checklist of pertinent anatomy and pathologic entities that can build on existing search patterns. Improved confidence and accuracy in the evaluation of midsagittal images will benefit physicians and patients. RSNA, 2018.
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http://dx.doi.org/10.1148/rg.2018170019 | DOI Listing |
J Vis Exp
January 2025
Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Disease, Henry and Allison McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Harvard Medical School;
A method to quantitate the stabilization of Mitochondria-Associated endoplasmic reticulum Membranes (MAMs) in a 3-dimensional (3D) neural model of Alzheimer's disease (AD) is presented here. To begin, fresh human neuro progenitor ReN cells expressing β-amyloid precursor protein (APP) containing familial Alzheimer's disease (FAD) or naïve ReN cells are grown in thin (1:100) Matrigel-coated tissue culture plates. After the cells reach confluency, these are electroporated with expression plasmids encoding red fluorescence protein (RFP)-conjugated mitochondria-binding sequence of AKAP1(34-63) (Mito-RFP) that detects mitochondria or constitutive MAM stabilizers MAM 1X or MAM 9X that stabilize tight (6 nm ± 1 nm gap width) or loose (24 nm ± 3 nm gap width) MAMs, respectively.
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January 2025
Human Phenome Institute and Shanghai Pudong Hospital, Fudan University, Shanghai, China.
. The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multi-modality and multi-view cardiac MRI data from 330 healthy volunteers, and each one covers standardized and commonly used clinical protocols. ©RSNA, 2025.
View Article and Find Full Text PDFMed Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
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January 2025
Department of Cognitive Psychology, University of Hamburg, Hamburg, Germany.
When retrieved, seemingly stable memories can become sensitive to significant events, such as acute stress. The mechanisms underlying these memory dynamics remain poorly understood. Here, we show that noradrenergic stimulation after memory retrieval impairs subsequent remembering, depending on hippocampal and cortical signals emerging during retrieval.
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January 2025
Sensor Engineering Department, Faculty of Science and Engineering, Maastricht University, 6200 MD Maastricht, The Netherlands.
As the main inhibitory neurotransmission system, the GABAergic system poses an interesting yet underutilized target for molecular brain imaging. While PET imaging of postsynaptic GABAergic neurons has been accomplished using radiolabeled benzodiazepines targeting the GABA receptor, the development of presynaptic radioligands targeting GABA transporter 1 (GAT1) has been unsuccessful thus far. Therefore, we developed a novel GAT1-addressing radioligand and investigated its applicability as a PET tracer in rodents.
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