An Engineering View on Megatrends in Radiology: Digitization to Quantitative Tools of Medicine

Abstract

Within six months of the discovery of X-ray in 1895, the technology was used to scan the interior of the human body, paving the way for many innovations in the field of medicine, including an ultrasound device in 1950, a CT scanner in 1972, and MRI in 1980. More recent decades have witnessed developments such as digital imaging using a picture archiving and communication system, computer-aided detection/diagnosis, organ-specific workstations, and molecular, functional, and quantitative imaging. One of the latest technical breakthrough in the field of radiology has been imaging genomics and robotic interventions for biopsy and theragnosis. This review provides an engineering perspective on these developments and several other megatrends in radiology.

Has COVID-19 pushed digitalisation in SMEs? The role of entrepreneurial orientation

“…Traditionally, SMEs encounter many difficulties in reorganising their existing businesses (Gallego et al, 2013) due to their limited resources (e.g. financial and skilled human resources), which represent a barrier to digitalisation (Kim et al, 2013). Despite these resource limits, some SMEs overcome these barriers, thus pushing the business to its full technological potential.…”

“…, 2013) due to their limited resources (e.g. financial and skilled human resources), which represent a barrier to digitalisation (Kim et al. , 2013).…”

Digitalisierung in der Mund‑, Kiefer- und Gesichtschirurgie

“…Die radiologische Bildgebung ist einer der am umfassendsten digitalisierten Bereiche der Medizin und auch in der historischen Betrachtung Vorreiterin im Bereich der Adaptation von Innovation, Definition von Branchenstandards und elektronischen Archivierungssystemen [12]. Der Fachbereich der MKG-Chirurgie ist mit diesem Fach eng vernetzt und profitiert daher von diesen Entwicklungen.…”

Genomics of Brain Tumor Imaging

“…[1][2][3][4] It is the integration of big data, quantitative imaging features taken from large numbers of images (typically MRI and computed tomography [CT]) and "-omic" data, which represent gene, protein or metabolite expression, as well as gene copy number, DNA methylation, and other important molecular markers. In oncology, this approach is being used to combine cancer phenotypes that can be globally assessed by imaging, with relevant molecular data, in order to develop prognostic and predictive biomarkers (Fig.…”