MEMoRIAL-M1.5 | Volume-of-interest imaging in C-arm CT
Projektleiter:
Projektbearbeiter:
M.Sc. Daniel Punzet
Finanzierung:
Forschergruppen:
Background
Volume-of-interest (VOI) imaging allows for significant patient dose reduction. However, reconstructed images suffer from severe image artifacts due to the limited data acquisition. Yet, in practice there is typically unused data of the patient available.
Objective
>> Utilization of the available prior knowledge to increase image quality of VOI imaging or reduce dose, respectively
Methods
>> Usage of consistency conditions to incorporate prior data properly while maintaining and not overwriting information from VOI imaging acquisitions
This is achieved by registration of prior and the retrieval of further information from the limited data available.
Results
Image reconstruction from truncated projections supported by prior volume data offers good image quality while reducing patient dose. Final investigations still need to show how well the method works on clinical devices.
Conclusions
Extrapolation methods using solely consistency conditions to improve image quality do not work sufficiently stable, however incorporating available prior data enables good image results.
Originality
Usage of previously unused information enables patient dose reduction while maintaining sufficient image quality.
Keywords
CBCT, volume-of-interest imaging, truncation, prior knowledge, registration
Volume-of-interest (VOI) imaging allows for significant patient dose reduction. However, reconstructed images suffer from severe image artifacts due to the limited data acquisition. Yet, in practice there is typically unused data of the patient available.
Objective
>> Utilization of the available prior knowledge to increase image quality of VOI imaging or reduce dose, respectively
Methods
>> Usage of consistency conditions to incorporate prior data properly while maintaining and not overwriting information from VOI imaging acquisitions
This is achieved by registration of prior and the retrieval of further information from the limited data available.
Results
Image reconstruction from truncated projections supported by prior volume data offers good image quality while reducing patient dose. Final investigations still need to show how well the method works on clinical devices.
Conclusions
Extrapolation methods using solely consistency conditions to improve image quality do not work sufficiently stable, however incorporating available prior data enables good image results.
Originality
Usage of previously unused information enables patient dose reduction while maintaining sufficient image quality.
Keywords
CBCT, volume-of-interest imaging, truncation, prior knowledge, registration
Anmerkungen
Wiss. Co-Betreuende / Scientific Co-Supervisors: Prof. Dr. Oliver Speck (OVGU:FNW/IfP)
Kooperationen im Projekt
- MEMoRIAL-M1.10 | Deep learning for interventional C-arm CT, Philipp Ernst
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)/Pattern Recognition Lab, Prof. Andreas Maier
- MEMoRIAL-M1.11 | C-arm imaging with few arbitrary projections, Fatima Saad
- MEMoRIAL-M1.3 | Use of prior knowledge for interventional C-arm CT, Domenico Iuso
- MEMoRIAL-M1.6 | Stent detection and enhancement, Negar Chabi
- OVGU/FIN-Artificial Intelligence Lab (AI-Lab), Prof. Sebastian Stober
- Forschungscampus MODAL, Zuse-Institut Berlin (ZIB), Prof. T. Conrad, Dr. S. Zachow
- Universitätsklinik für Neuroradiologie, UKMD Magdeburg, Dr. Daniel Behme
Publikationen
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Kontakt
Univ.-Prof. Dr. Georg Rose
Otto-von-Guericke-Universität Magdeburg
Fakultät für Elektrotechnik und Informationstechnik
Universitätsplatz 2
39016
Magdeburg
Tel.:+49 391 6718862
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