Emerging methods in radiology

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 a decisive 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.

Full wave 3D inverse scattering transmission ultrasound tomography in the presence of high contrast

“…This technology allows machine learning (ML) 46 and radiomics to apply to ultrasound tomography images even in the presence of bone. Machine learning has historically been applied to other ultrasound modalities [47][48][49][50] and other images, but the presence of bone has lead to artifacts that make ML problematic. Our lack of such artifacts makes application of ML more fruitful.…”

Automatic Intrahepatic Biliary Segmentation Based Image Processing Techniques using Magnetic Resonance Images

“…For Magnetic Resonance Images (MRI) of the biliary tree, it is very important to remove any type of noise as a preprocessing step, since such noise would obstruct disease diagnose [22]. [23] mentioned that a new age of images-based diseases diagnose is starting, since it reviews the most recent evolving techniques in the field of medical images processing, especially those techniques related to the de-noising stages with deep learning approaches. Several deep learning techniques were introduced in a wide range of professional research to measure the dilation of the bile duct.…”