Abstract
Imaging modalities have developed rapidly in recent decades. In addition to improved resolution as well as whole-body and faster image acquisition, the possibilities of functional and molecular examination of tissue pathophysiology have had adecisive influence on imaging diagnostics and provided ground-breaking knowledge. Many promising approaches are currently being pursued to increase the application area of devices and contrast media and to improve their sensitivity and quantitative informative value. These are complemented by new methods of data processing, multiparametric data analysis, and integrated diagnostics. The aim of this article is to provide an overview of technological innovations that will enrich clinical imaging in the future, and to highlight the resultant diagnostic options. These relate to the established imaging methods such as CT, MRI, ultrasound, PET, and SPECT but also to new methods such as magnetic particle imaging (MPI), optical imaging, and photoacoustics. In addition, approaches to radiomic image evaluation are explained and the chances and difficulties for their broad clinical introduction are discussed. The potential of imaging to describe pathophysiological relationships in ever increasing detail, both at whole-body and tissue level, can in future be used to better understand the mechanistic effect of drugs, to preselect patients to therapies, and to improve monitoring of therapy success. Consequently, the use of interdisciplinary integrated diagnostics will greatly change and enrich the profession of radiologists.
Highlights
Advances in computed tomography (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 (
While diagnostic Magnetic resonance imaging (MRI) relies to a major extent on contrast-weighted images, faster scanning techniques in conjunction with model-based reconstruction techniques, such as MR fingerprinting [37], have started to enable a more widespread access to multi-parametric and quantitative tissue parameter measurements
After performing a sophisticated reconstruction, high image quality is restored. c–f A deep-learning-based reconstruction method working with convolutional recurrent neural networks (CRNN) is applied to a cardiac MRI scan in axial view.While the undersampled image (d) is diagnostically unusable, the CRNN reconstruction (e) closely resembles the ground truth image (c), with a deviation of less than 3% as depicted in the colormap (f). (Reprinted with permission from [49])
Summary
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 PCI can improve the sensitivity of calcification classification in breast imaging, and clinical systems in the field of mammography are being developed [51]. While diagnostic MRI relies to a major extent on contrast-weighted images, faster scanning techniques in conjunction with model-based reconstruction techniques, such as MR fingerprinting [37], have started to enable a more widespread access to multi-parametric and quantitative tissue parameter measurements.
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