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DDC: 540 × Subject: magnetic resonance imaging ×

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    Targeted T1 Magnetic Resonance Imaging Contrast Enhancement with Extraordinarily Small CoFe2O4 Nanoparticles
    (Washington, DC : American Chemical Society, 2019) Piché, Dominique; Tavernaro, Isabella; Fleddermann, Jana; Lozano, Juan G.; Varambhia, Aakash; Maguire, Mahon L.; Koch, Marcus; Ukai, Tomofumi; Hernández Rodríguez, Armando J.; Jones, Lewys; Dillon, Frank; Reyes Molina, Israel; Mitzutani, Mai; González Dalmau, Evelio R.; Maekawa, Toru; Nellist, Peter D.; Kraegeloh, Annette; Grobert, Nicole
    Extraordinarily small (2.4 nm) cobalt ferrite nanoparticles (ESCIoNs) were synthesized by a one-pot thermal decomposition approach to study their potential as magnetic resonance imaging (MRI) contrast agents. Fine size control was achieved using oleylamine alone, and annular dark-field scanning transmission electron microscopy revealed highly crystalline cubic spinel particles with atomic resolution. Ligand exchange with dimercaptosuccinic acid rendered the particles stable in physiological conditions with a hydrodynamic diameter of 12 nm. The particles displayed superparamagnetic properties and a low r2/r1 ratio suitable for a T1 contrast agent. The particles were functionalized with bile acid, which improved biocompatibility by significant reduction of reactive oxygen species generation and is a first step toward liver-targeted T1 MRI. Our study demonstrates the potential of ESCIoNs as T1 MRI contrast agents.
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    A Review on Data Fusion of Multidimensional Medical and Biomedical Data
    (Basel : MDPI, 2022) Azam, Kazi Sultana Farhana; Ryabchykov, Oleg; Bocklitz, Thomas
    Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.