What scans we will read: imaging instrumentation trends in clinical oncology

Beyer, Thomas, Bidaut, Luc, Dickson, John , Kachelriess, Marc, Kiessling, Fabian, Leitgeb, Rainer, Ma, Jingfei, Sundar, Lalith Kumar Shiyam, Theek, Benjamin and Mawlawi, Osama (2020) What scans we will read: imaging instrumentation trends in clinical oncology. Cancer Imaging, 20 (1). ISSN 1470-7330

Full content URL: https://doi.org/10.1186/s40644-020-00312-3

What scans we will read: imaging instrumentation trends in clinical oncology
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s40644-020-00312-3.pdf - Whole Document
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Item Type:Article
Item Status:Live Archive


Oncological diseases account for a significant portion of the burden on public healthcare systems with associated
costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific
morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-
invasively, so as to provide referring oncologists with essential information to support therapy management
decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards
integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/
CT), advanced MRI, optical or ultrasound imaging.
This perspective paper highlights a number of key technological and methodological advances in imaging
instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as
the hardware-based combination of complementary anatomical and molecular imaging. These include novel
detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system
developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing
methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging
in oncology patient management we introduce imaging methods with well-defined clinical applications and
potential for clinical translation. For each modality, we report first on the status quo and point to perceived
technological and methodological advances in a subsequent status go section. Considering the breadth and
dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the
majority of them being imaging experts with a background in physics and engineering, believe imaging methods
will be in a few years from now.
Overall, methodological and technological medical imaging advances are geared towards increased image contrast,
the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall
examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is
complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To
this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis,
including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor
phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-
dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and
analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts
with a domain knowledge that will need to go beyond that of plain imaging.

Keywords:Oncology imaging, Instrumentation, CT, MRI, Optical, SPECT, US, Sonography, Hybrid imaging, Machine Learning
Subjects:B Subjects allied to Medicine > B820 Radiology
G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H990 Engineering not elsewhere classified
F Physical Sciences > F350 Medical Physics
J Technologies > J990 Technologies not elsewhere classified
B Subjects allied to Medicine > B990 Subjects Allied to Medicine not elsewhere classified
A Medicine and Dentistry > A900 Others in Medicine and Dentistry
B Subjects allied to Medicine > B800 Medical Technology
B Subjects allied to Medicine > B890 Medical Technology not elsewhere classified
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:40956
Deposited On:15 Jun 2020 10:33

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