RDI-a regression detectability index for quality assurance in
| Autor: | C Elster, Mathias Anton, W J H Veldkamp, I Hernandez-Giron |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Iterative method Image quality media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION task-specific quality assessment Iterative reconstruction quality assurance 030218 nuclear medicine & medical imaging Task (project management) 03 medical and health sciences 0302 clinical medicine Radiology Nuclear Medicine and imaging Quality (business) media_common Radiological and Ultrasound Technology business.industry x-ray CT Pattern recognition Regression 030220 oncology & carcinogenesis model observer low contrast detectability Artificial intelligence business Quality assurance |
| Zdroj: | Physics in Medicine & Biology, 65(8). IOP PUBLISHING LTD |
| DOI: | 10.1088/1361-6560/ab7b2e |
| Popis: | Novel iterative image reconstruction methods can help reduce the required radiation dose in x-ray diagnostics such as computed tomography (CT), while maintaining sufficient image quality. Since some of the established image quality measures are not appropriate for reliably judging the quality of images derived by iterative methods, alternative approaches such as task-specific quality assessment would be highly desirable for acceptance or constancy testing. Task-based image quality methods are also closer to tasks performed by the radiologists, such as lesion detection. However, this approach is usually hampered by a huge workload, since hundreds of images are usually required for its application. It is demonstrated that the proposed approach works reliably on the basis of significantly fewer images, and that it correlates well with results obtained from human observers. |
| Databáze: | OpenAIRE |
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