Accurate characterization of cirrhotic nodules and early diagnosis of hepatocellular carcinoma (HCC) are of vital importance. Currently, computed tomography (CT) and magnetic resonance (MR) imaging are standard modalities for the investigation of new nodules found at surveillance ultrasonography (US). This article describes the successful integration of contrast material-enhanced US into a multimodality approach for diagnosis of HCC and its benefits in this population. The application of contrast-enhanced US immediately following surveillance US allows for prompt dynamic contrast-enhanced evaluation, removing the need for further imaging of benign lesions. Contrast-enhanced US also provides dynamic real-time assessment of tumor vascularity so that contrast enhancement can be identified regardless of its timing or duration, allowing for detection of arterial hypervascularity and portal venous washout. The purely intravascular nature of US contrast agents is valuable as the rapid washout of nonhepatocyte malignancies is highly contributory to their differentiation from HCC. The authors believe contrast-enhanced US provides complementary information to CT and MR imaging in the characterization of nodules in high-risk patients. RSNA, 2017 Online supplemental material is available for this article.
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http://dx.doi.org/10.1148/radiol.2016151732 | DOI Listing |
Med Phys
December 2024
School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China.
Background: In clinical practices, doctors usually need to synthesize several single-modality medical images for diagnosis, which is a time-consuming and costly process. With this background, multimodal medical image fusion (MMIF) techniques have emerged to synthesize medical images of different modalities, providing a comprehensive and objective interpretation of the lesion.
Purpose: Although existing MMIF approaches have shown promising results, they often overlook the importance of multiscale feature diversity and attention interaction, which are essential for superior visual outcomes.
Eur Arch Psychiatry Clin Neurosci
December 2024
Department of Psychiatry, University of Muenster, Muenster, Germany.
Schizophrenia (SCZ), bipolar (BD) and major depression disorder (MDD) are severe psychiatric disorders that are challenging to treat, often leading to treatment resistance (TR). It is crucial to develop effective methods to identify and treat patients at risk of TR at an early stage in a personalized manner, considering their biological basis, their clinical and psychosocial characteristics. Effective translation of theoretical knowledge into clinical practice is essential for achieving this goal.
View Article and Find Full Text PDFNeuromodulation
December 2024
Functional and Pain Clinic, Sao Paulo, SP, Brazil; Pediatric Neurosurgery, Washington University in St. Louis, St Louis, MO, USA. Electronic address:
Introduction: Chronic pelvic pain (CPP) is a multifaceted condition that poses significant challenges in clinical management owing to its complex and varied pathophysiology, including neuropathic, somatic, visceral, and musculoskeletal components. Endometriosis is frequently associated with CPP, necessitating a comprehensive, multimodal treatment strategy. This approach typically includes physical and behavioral therapy, pharmacologic interventions, surgical management of endometriosis, and various pain-modulating procedures.
View Article and Find Full Text PDFTomography
December 2024
Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 824005, Taiwan.
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagnostic accuracy. This study aims to enhance breast cancer detection through a cross-modality fusion approach combining mammography and ultrasound imaging, using advanced convolutional neural network (CNN) architectures.
View Article and Find Full Text PDFJ Imaging
December 2024
National Electronic and Computer Technology Center, National Science and Technology Development Agency, Khlong Luang, Pathum Thani 12120, Thailand.
Accurate human action recognition is becoming increasingly important across various fields, including healthcare and self-driving cars. A simple approach to enhance model performance is incorporating additional data modalities, such as depth frames, point clouds, and skeleton information, while previous studies have predominantly used late fusion techniques to combine these modalities, our research introduces a multi-level fusion approach that combines information at early, intermediate, and late stages together. Furthermore, recognizing the challenges of collecting multiple data types in real-world applications, our approach seeks to exploit multimodal techniques while relying solely on RGB frames as the single data source.
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