One of the main challenges in ultra-high field whole body MRI relates to the uniformity and efficiency of the radiofrequency field. Although recent advances in the design of RF coils have demonstrated that dipole antennas have a current distribution ideally suited to 7T MRI, they are limited by low isolation and poor robustness to loading changes. Multi-layered and self-decoupled loop coils have demonstrated improved RF performance in these areas at lower field MRI but have not been adapted to dipole designs. In this work, we introduce a novel type of RF antenna consisting of integrated multi-modal antenna with coupled radiating structures (I-MARS), which use layered conductors and dielectric substrates to allow dipole and transmission line modes to co-exist on the same compact dipole-shaped structure. The proposed antenna was optimally designed for 7T MRI and compared with existing dipole antennas using numerical simulations, which showed that I-MARS had similar B over specific absorption rate efficiency and superior isolation and stability. Subsequently, a prototype pTx coil array was built and tested in vivo on healthy volunteers at 7T. The articulated, modular construction of the I-MARS coil array allowed it to be readily conformed across multiple body regions (hip, knee, shoulder, lumbar spine and prostate), without requiring modification of the tuning and matching of the antennas. Using RF shimming, uniform and efficient excitation was successfully achieved in the acquisition of high-resolution MR images.
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http://dx.doi.org/10.1109/TMI.2021.3103654 | DOI Listing |
Biosensors (Basel)
November 2024
Key Laboratory of Marine Materials and Related Technologies, Zhejiang Key Laboratory of Marine Materials and Protective Technologies, Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences, Ningbo 315201, China.
This review examines recent advances in surface-enhanced Raman spectroscopy (SERS) for urinary metabolite analysis, focusing on the development and application of noble metal nanohybrids. We explore the diverse range of hybrid materials, including carbon-based, metal-organic-framework (MOF), silicon-based, semiconductor, and polymer-based systems, which have significantly improved SERS performance for detecting key urinary biomarkers. The principles underlying SERS enhancement in these nanohybrids are discussed, elucidating both electromagnetic and chemical enhancement mechanisms.
View Article and Find Full Text PDFBiol Psychol
December 2024
Big Data Analytics and Web Intelligence Laboratory, Department of Computer Science & Engineering, Delhi Technological University, New Delhi, India. Electronic address:
Within the domain of neurodevelopmental disorders, autism spectrum disorder (ASD) emerges as a distinctive neurological condition characterized by multifaceted challenges. The delayed identification of ASD poses a considerable hurdle in effectively managing its impact and mitigating its severity. Addressing these complexities requires a nuanced understanding of data modalities and the underlying patterns.
View Article and Find Full Text PDFMed Phys
December 2024
Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Background: Medical imaging plays a pivotal role in the real-time monitoring of patients during the diagnostic and therapeutic processes. However, in clinical scenarios, the acquisition of multi-modal imaging protocols is often impeded by a number of factors, including time and economic costs, the cooperation willingness of patients, imaging quality, and even safety concerns.
Purpose: We proposed a learning-based medical image synthesis method to simplify the acquisition of multi-contrast MRI.
Three-Dimensional Polarized Light Imaging (3D-PLI) and Computational Scattered Light Imaging (ComSLI) map dense nerve fibers in brain sections with micrometer resolution using visible light. 3D-PLI reconstructs single fiber orientations, while ComSLI captures multiple directions per pixel, offering deep insights into brain tissue structure. Here, we introduce the Scattering Polarimeter, a high-speed correlative microscope to leverage the strengths of both methods.
View Article and Find Full Text PDFArXiv
December 2024
Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA.
Purpose: A reliable and comprehensive cancer prognosis model for clear cell renal cell carcinoma (ccRCC) could better assist in personalizing treatment. In this work, we developed a multi-modal ensemble model (MMEM) which integrates pretreatment clinical information, multi-omics data, and histopathology whole slide image (WSI) data to learn complementary information to predict overall survival (OS) and disease-free survival (DFS) for patients with ccRCC.
Methods And Materials: We collected 226 patients from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma dataset (TCGA-KIRC).
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