Emerging methods in radiology
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B. Theek1,2 · T. Nolte1 · D. Pantke1 · F. Schrank1 · F. Gremse1 · V. Schulz1,2,3 · F. Kiessling1,2,3 1
© Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2020
Institute for Experimental Molecular Imaging, Medical Faculty, RWTH Aachen International University, Aachen, Germany 2 Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany 3 Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen, Aachen, Germany
Emerging methods in radiology Several milestones can be highlighted in the development of imaging methods. It started with the detection of X-rays, enabling the first noninvasive view into the body, followed by the introduction of tomographic methods, providing more anatomical details, and the introduction of functional and molecular imaging, paving the way for a spatially resolved pathophysiological characterization. Imaging methods and probes continue to undergo advances, and new imaging methods are emerging, increasing the spectrum of accessible biomedical features. However, with the rise of deep learning, radiomics, and comprehensive data analysis, a new era of diagnostics starts, where image features from various sources can be integrated and interpreted in concert with other diagnostic data. This article highlights some of the most recent developments in imaging technology, considering the opportunities ushered in with advanced digital data analysis. However, because of the limited space in this article, we only focus on approaches that are already close to or in the process of clinical translation.
Advances in computed tomography Due to concerns regarding the radiation risk of computed tomography (CT), intense research has been conducted on the processing of low-dose CT data. Iterative reconstruction techniques achieve higher image quality than filtered backprojection, but reconstructed images from lowdose CT data still suffer from increased
noise and artifacts. Recent publications present deep-learning methods from computer vision to improve the quality of medical images. Convolutional neural networks show promising results for low-dose CT denoising in the form of residual autoencoders (. Fig. 1a–c; [12]) and generative adversarial networks. Furthermore, neural networks can be used to reduce metal artifacts [27] and to optimize iterative reconstruction algorithms [71]. Advances in CT hardware have been achieved with smaller detector element sizes, resulting in ultra-high-resolution CT scanners, which use matrix sizes of up to 2048 × 2048 pixels (. Fig. 1d–i; [26]).
Photon-counting computed tomography AnotherCT developmentgaining clinical interest is photon-counting CT (PCCT), which uses energy-resolving detectors and thus enables the acquisition of CT scans at multiple energies. Initial evaluations have shown its capability of highresolution imaging with reduced radiation dose [56], as well as the distinction between different tissue types and contrast agents [48, 53]. One-step inversion methods have been recently introduced for the joint material decomposition
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