Iterative CT reconstruction algorithms coupled with edge-preserving filters are attracting a growing interest in the field of biomedical X-ray imaging. In many cases such algorithms demonstrate improved reconstruction quality compared with analytical reconstruction algorithms, for instance in the case of measurements with a reduced number of projections. Their performance is often evaluated on test phantoms using conventional figures of merit, as for example contrast-to-noise ratio or spatial resolution at sharp edges. However, this approach can lead to an optimistic evaluation of the reconstruction quality: compared to test phantoms, biomedical images typically present complex structures and strong inhomogeneities. The reconstruction algorithms can generate artifacts that can hide small details, produce fake structures or alter the shape of actual ones. Figures of merit based on similarity metrics are a valuable tool for an objective evaluation of the reconstruction quality, in particular in the case of biomedical images.In this work we present an application of such figures of merit to breast-CT reconstruction with a simultaneous algebraic reconstruction technique (SART) algorithm combined with a bilateral filter, at varying number of projections. The results show that, for a given reconstruction, the figures of merit are fairly stable for a wide range of variations of the bilateral filter parameters, thus showing the robustness of the reconstruction technique.

Quantitative evaluation of breast CT reconstruction by means of figures of merit based on similarity metrics / Oliva, P.; Golosio, B.; Arfelli, F.; Delogu, P.; Lillo, F. D.; Dreossi, D.; Fanti, V.; Fardin, L.; Fedon, C.; Mettivier, G.; Rigon, L.; Russo, P.; Sarno, A.; Tromba, G.; Longo, R.. - (2017), pp. 1-5. ((Intervento presentato al convegno 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 tenutosi a Hyatt Regency, usa nel 2017 [10.1109/NSSMIC.2017.8532786].

Quantitative evaluation of breast CT reconstruction by means of figures of merit based on similarity metrics

Oliva P.
Supervision
;
Golosio B.;
2017

Abstract

Iterative CT reconstruction algorithms coupled with edge-preserving filters are attracting a growing interest in the field of biomedical X-ray imaging. In many cases such algorithms demonstrate improved reconstruction quality compared with analytical reconstruction algorithms, for instance in the case of measurements with a reduced number of projections. Their performance is often evaluated on test phantoms using conventional figures of merit, as for example contrast-to-noise ratio or spatial resolution at sharp edges. However, this approach can lead to an optimistic evaluation of the reconstruction quality: compared to test phantoms, biomedical images typically present complex structures and strong inhomogeneities. The reconstruction algorithms can generate artifacts that can hide small details, produce fake structures or alter the shape of actual ones. Figures of merit based on similarity metrics are a valuable tool for an objective evaluation of the reconstruction quality, in particular in the case of biomedical images.In this work we present an application of such figures of merit to breast-CT reconstruction with a simultaneous algebraic reconstruction technique (SART) algorithm combined with a bilateral filter, at varying number of projections. The results show that, for a given reconstruction, the figures of merit are fairly stable for a wide range of variations of the bilateral filter parameters, thus showing the robustness of the reconstruction technique.
978-1-5386-2282-7
Quantitative evaluation of breast CT reconstruction by means of figures of merit based on similarity metrics / Oliva, P.; Golosio, B.; Arfelli, F.; Delogu, P.; Lillo, F. D.; Dreossi, D.; Fanti, V.; Fardin, L.; Fedon, C.; Mettivier, G.; Rigon, L.; Russo, P.; Sarno, A.; Tromba, G.; Longo, R.. - (2017), pp. 1-5. ((Intervento presentato al convegno 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 tenutosi a Hyatt Regency, usa nel 2017 [10.1109/NSSMIC.2017.8532786].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/221626
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