This paper is focused on quantitative perfusion analysis using MRI and ultrasound. In both MRI and ultrasound, most approaches allow estimation of rate constants (Ktrans, kep for MRI) and indices (AUC, TTP) that are only related to the physiological perfusion parameters of a tissue (e.g. blood flow, vessel permeability) but do not allow their absolute quantification. Recent methods for quantification of these physiological perfusion parameters are shortly reviewed. The main problem of these methods is estimation of the arterial input function (AIF). This paper summarizes and extends the current blind-deconvolution approaches to AIF estimation. The feasibility of these methods is shown on a small preclinical study using both MRI and ultrasound.
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http://dx.doi.org/10.1109/EMBC.2014.6944569 | DOI Listing |
Sensors (Basel)
December 2024
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Breast cancer is a significant cause of death from cancer in women globally, highlighting the need for improved diagnostic imaging to enhance patient outcomes. Accurate tumor identification is essential for diagnosis, treatment, and monitoring, emphasizing the importance of advanced imaging technologies that provide detailed views of tumor characteristics and disease. Recently, a new imaging modality named synthetic correlated diffusion imaging (CDI) has been showing promise for enhanced prostate cancer delineation when compared to existing MRI imaging modalities.
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December 2024
Faculty of Computer Science, Polish-Japanese Academy of Information Technology, 86 Koszykowa Street, 02-008 Warsaw, Poland.
Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans ( = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated.
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December 2024
Research Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London WC2R 2LS, UK.
MR elastography is a non-invasive imaging technique that provides quantitative maps of tissue biomechanical properties, i.e., elasticity and viscosity.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Beitou Dist., Taipei City 112, Taiwan.
The tumor microenvironment (TME) is a critical factor in cancer progression, driving tumor growth, immune evasion, therapeutic resistance, and metastasis. Understanding the dynamic interactions within the TME is essential for advancing cancer management. Molecular imaging provides a non-invasive, real-time, and longitudinal approach to studying the TME, with techniques such as positron emission tomography (PET), magnetic resonance imaging (MRI), and fluorescence imaging offering complementary strengths, including high sensitivity, spatial resolution, and intraoperative precision.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
IBMM, University of Montpellier, CNRS, ENSCM, 34095 Montpellier, France.
Background: Gadolinium-based contrast agents (GBCA) are widely used in magnetic resonance imaging (MRI) to enhance image contrast by interacting with water molecules, thus improving diagnostic capabilities. However, understanding the residual accumulation of GBCA in tissues after administration remains an area of active research. This highlights the need for advanced analytical techniques capable of investigating interactions between GBCAs and biopolymers, such as type I collagen, which are abundant in the body.
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