The ultrasound fusion imaging (UFI) system is a new promising imaging modality that combines live ultrasound investigations with preregistered CT, MRI, or PET images. In this study, we want to present our initial experience with the new method that combines the transcranial color-coded sonography (TCCS) in different insonation planes and the 3T-weighted MRI cerebral images. The study validates the diagnostic capabilities of the system to detect different normal cerebral structures in healthy volunteers. In the present paper, we also discuss the advantages of US fusion imaging technology and its clinical applications in Neurology.
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http://dx.doi.org/10.3897/folmed.64.e64271 | DOI Listing |
Sci Rep
December 2024
Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok, 25913, Republic of Korea.
Autism spectrum disorder (ASD) is a neurologic disorder considered to cause discrepancies in physical activities, social skills, and cognition. There is no specific medicine for treating this disorder; early intervention is critical to improving brain function. Additionally, the lack of a clinical test for detecting ASD makes diagnosis challenging.
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December 2024
Department of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
In today's technologically advanced landscape, precision in navigation and positioning holds paramount importance across various applications, from robotics to autonomous vehicles. A common predicament in location-based systems is the reliance on Global Positioning System (GPS) signals, which may exhibit diminished accuracy and reliability under certain conditions. Moreover, when integrated with the Inertial Navigation System (INS), the GPS/INS system could not provide a long-term solution for outage problems due to its accumulated errors.
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December 2024
School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China.
Multi-modal medical images are important in tumor lesion detection. However, the existing detection models only use single-modal to detect lesions, a multi-modal semantic correlation is not enough to consider and lacks ability to express the shape, size, and contrast degree features of lesions. A Cross Modal YOLOv5 model (CMYOLOv5) is proposed.
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December 2024
School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang 330006, China (C.X., L.D., W.C., M.H.); Jiangxi Provincial Key Laboratory of Disease Prevention and Public Health, Nanchang University, Nanchang 330006, China (C.X., L.D., W.C., M.H.). Electronic address:
Rationale And Objectives: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.
Materials And Methods: A total of 724 pathologically confirmed early invasive lung adenocarcinoma patients were retrospectively included from two centers. Clinical and CT semantic features of the patients were collected, and 3D radiomics features were extracted from nonenhanced CT images.
Urology
December 2024
Department of Urology, University of Michigan, Ann Arbor, MI.
Objectives: To determine how many cores should be collected per region of interest in magnetic resonance imaging (MRI)-guided fusion prostate biopsy. MRI-guided targeted prostate biopsy has led to improved detection of clinically significant prostate cancer (csPC); however, data is limited regarding the optimal number of biopsy cores that should be taken. An ideal number of cores maximizes clinically significant cancer detection while minimizing cost, discomfort, and procedure time.
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