Applications of Artificial Intelligence in Medical Imaging—Clinical and Pre-Clinical Scope
A special issue of Tomography (ISSN 2379-139X).
Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 14286
Special Issue Editor
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) is an extremely fast-growing field that infiltrates everything from household medical devices to intensive care units. The comparison that the breakthrough of AI is akin to that of electricity may illustrate the significance of this field. In medical imaging, AI has gained ground in multiple disciplines—from improving raw data to guiding outlines of pathology.
Drivers in AI development are channelled by both new medical discoveries as well as aided support for the interpretation of the growing range of medical imaging, the latter having major time and cost implications for the healthcare system.
We invite submissions presenting AI applications in the medical imaging field. Our special focus is on AI application that aids preclinical and clinical imaging work to help the interpretation, either by reconstruction improvements, co-registration, organ-segmentation or image improvements by noise filtration or resolution. Furthermore, submissions using imaging data along with tabular data to improve the interpretation are welcome.
The main scope is research in a clinical or translational phase, but we will also encourage submissions on papers focused on better pre-clinical workflow. This Special Issue will invite imaging modalities in MRI, CT, nuclear, molecular, ultrasound, optical and spectroscopy, and multimodal imaging approaches are also within the scope. Lastly, submissions exploring the benefits for cost, efficiency or added guidance for the application of AI in medical imaging are sought with great interest.
Dr. Esben Søvsø Szocska Hansen
Guest Editor
Manuscript Submission Information
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Keywords
- medical imaging
- machine learning
- artificial intelligense
- image reconstruction
- quantative imaging
- MRI
- CT
- ultrasound
- metabolic imaging
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