Purpose: Patients with deep brain stimulation (DBS) implants benefit highly from MRI, however, access to MRI is restricted for these patients because of safety hazards associated with RF heating of the implant. To date, all MRI studies on RF heating of medical implants have been performed in horizontal closed-bore systems. Vertical MRI scanners have a fundamentally different distribution of electric and magnetic fields and are now available at 1.2T, capable of high-resolution structural and functional MRI. This work presents the first simulation study of RF heating of DBS implants in high-field vertical scanners.
Methods: We performed finite element electromagnetic simulations to calculate specific absorption rate (SAR) at tips of DBS leads during MRI in a commercially available 1.2T vertical coil compared to a 1.5T horizontal scanner. Both isolated leads and fully implanted systems were included.
Results: We found 10- to 30-fold reduction in SAR implication at tips of isolated DBS leads, and up to 19-fold SAR reduction at tips of leads in fully implanted systems in vertical coils compared to horizontal birdcage coils.
Conclusions: If confirmed in larger patient cohorts and verified experimentally, this result can open the door to plethora of structural and functional MRI applications to guide, interpret, and advance DBS therapy.
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http://dx.doi.org/10.1002/mrm.28049 | DOI Listing |
Neurol Res Pract
January 2025
Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität Würzburg (JMU), Haus D7, Josef-Schneider-Straße 2, 97080, Würzburg, Germany.
Background: Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches.
Methods: The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study.
Nat Cancer
January 2025
Dept. of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility.
View Article and Find Full Text PDFProg Neuropsychopharmacol Biol Psychiatry
January 2025
Department of Physiology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary. Electronic address:
Comorbidities between gastrointestinal diseases and psychiatric disorders have been widely reported, with the gut-brain axis implicated as a potential biological basis. Thus, dysbiosis may play an important role in the etiology of schizophrenia, which is barely detected. Triple-hit Wisket model rats exhibit various schizophrenia-like behavioral phenotypes.
View Article and Find Full Text PDFJ Clin Neurosci
January 2025
Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, NSW, Australia; Computational NeuroSurgery (CNS) Lab, Macquarie University, NSW, Australia.
Purpose: This literature review aims to synthesise current research on the application of artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in magnetic resonance imaging (MRI).
Methods: A literature search was conducted using the databases Embase, Medline, Scopus, and Web of Science, and captured articles were assessed for inclusion in the review. Data extraction was performed for the summary of the AI model used, and key findings of each article, advantages and disadvantages were identified.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
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