Japanese journal of radiology
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Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for improving the image quality as "noise and artifact reduction", "super resolution" and "image acquisition and reconstruction". For each category, we present and outline the features of some studies.
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Deep learning has been applied to clinical applications in not only radiology, but also all other areas of medicine. This review provides a technical and clinical overview of deep learning in radiology. To gain a more practical understanding of deep learning, deep learning techniques are divided into five categories: classification, object detection, semantic segmentation, image processing, and natural language processing. ⋯ In the medical field, radiologists are specialists in such tasks. Using clinical applications based on deep learning would, therefore, be expected to contribute to substantial improvements in radiology. By gaining a better understanding of the features of deep learning, radiologists could be expected to lead medical development.
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This study aimed to explore the clinical and prognostic significance of 18F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) in epithelial ovarian cancer (EOC). ⋯ Preoperative 18F-FDG PET/CT had a predictive value on chemosensitivity and proliferation after primary debulking surgery in EOC patients noninvasively.
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To evaluate the clinical results of central venous access port (CV port) placement by translumbar inferior vena cava cannulation using angio-CT unit for cancer patients with superior vena cava syndrome. ⋯ CV port placement with translumbar inferior vena cava cannulation using an angio-CT unit for cancer patients with superior vena cava syndrome was safe and effective.
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Comparative Study
Diagnostic performance of 18F-FDG PET/CT and whole-body MRI before and early after treatment of multiple myeloma: a prospective comparative study.
To determine the diagnostic accuracy of WB-MRI and 18F-FDG PET/CT in detecting infiltration pattern, disease activity, and response to treatment in patients with multiple myeloma (MM). ⋯ WB-MRI is more sensitive than 18F-FDG PET/CT in the diagnosis of MM before treatment; however, 18F-FDG PET/CT is more specific than WB-MRI in detecting residual involvement in treated patients.