Multiparametric ultrasound (MPUS), combining conventional techniques (greyscale and colour Doppler ultrasound), ultrasound strain elastography, and contrast-enhanced ultrasound (CEUS), has been successfully used in the assessment of adult scrotal pathology. Contrast-enhanced ultrasound can confidently establish testicular tissue vascularity even in the small-volume paediatric testis. Elastography provides further assessment of tissue stiffness, potentially adding useful diagnostic information. In children, ultrasonography is particularly advantageous, being safe, radiation-free and negating the need for sedation or general anaesthesia during the imaging evaluation. In this review article, we aim to familiarise readers with the MPUS scanning protocol used for paediatric scrotal examination and provide an overview of scrotal MPUS features, with particular focus to clinical indications where MPUS may be advantageous over conventional ultrasonography.
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http://dx.doi.org/10.1259/bjr.20200063 | DOI Listing |
NMR Biomed
March 2025
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
In clinical practice, particularly in neurology assessments, imaging multiparametric MR images with a single-sequence scan is often limited by either insufficient imaging contrast or the constraints of accelerated imaging techniques. A novel single scan 3D imaging method, incorporating Wave-CAIPI and MULTIPLEX technologies and named WAMP, has been developed for rapid and comprehensive parametric imaging in clinical diagnostic applications. Featuring a hybrid design that includes wave encoding, the CAIPIRINHA sampling pattern, dual time of repetition (TR), dual flip angle (FA), multiecho, and optional flow modulation, the WAMP method captures information on RF B1t fields, proton density (PD), T1, susceptibility, and blood flow.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Dusseldorf, Germany.
Aim of this study was to proof the concept of optimizing the contrast between prostate cancer (PC) and healthy tissue by DWI post-processing using a quadrature method. DWI post-processing was performed on 30 patients (median age 67 years, prostate specific antigen 8.0 ng/ml) with PC and clear MRI findings (PI-RADS 4 and 5).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical Electronical Engineering, Yaşar University, Bornova, İzmir, Turkey.
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
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