Polyane M Queiroz1, Matheus L Oliveira1, Jefferson L O Tanaka2, Milton G Soares2, Francisco Haiter-Neto1, Evelise Ono3. 1. 1 Department of Oral Diagnosis, Area of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Sao Paulo, Brazil. 2. 2 Department of Dental Radiology and Imaging, Brazilian Dental Association, Ponta Grossa, Parana, Brazil. 3. 3 Department of Oral Medicine, State University of Londrina, Londrina, Parana, Brazil.
Abstract
OBJECTIVES: To evaluate three methods of geometric image reconstruction for digital subtraction radiography (DSR). METHODS: Digital periapical radiographs were acquired of 24 teeth with the X-ray tube at 6 different geometric configurations of vertical (V) and horizontal (H) angles: V0°H0°, V0°H10°, V10°H0°, V10°H10°, V20°H0° and V20°H10°. All 144 images were registered in pairs (Group V0°H0° + 1 of the 6 groups) 3 times by using the Emago(®) (Oral Diagnostic Systems, Amsterdam, Netherlands) with manual selection and Regeemy with manual and automatic selections. After geometric reconstruction on the two software applications under different modes of selection, all images were subtracted and the standard deviation of grey values was obtained as a measure of image noise. All measurements were repeated after 15 days to evaluate the method error. Values of image noise were statistically analyzed by one-way ANOVA for differences between methods and between projection angles, followed by Tukey's test at a level of significance of 5%. RESULTS: Significant differences were found between most of the projection angles for the three reconstruction methods. Image subtraction after manual selection-based reconstruction on Regeemy presented the lowest values of image noise, except on group V0°H0°. The groups V10°H0° and V20°H0° were not significantly different between the manual selection-based reconstruction in Regeemy and automatic selection-based reconstruction in Regeemy methods. CONCLUSIONS: The Regeemy software on manual mode revealed better quality of geometric image reconstruction for DSR than the Regeemy on automatic mode and the Emago on manual mode, when the radiographic images were obtained at V and H angles used in the present investigation.
OBJECTIVES: To evaluate three methods of geometric image reconstruction for digital subtraction radiography (DSR). METHODS: Digital periapical radiographs were acquired of 24 teeth with the X-ray tube at 6 different geometric configurations of vertical (V) and horizontal (H) angles: V0°H0°, V0°H10°, V10°H0°, V10°H10°, V20°H0° and V20°H10°. All 144 images were registered in pairs (Group V0°H0° + 1 of the 6 groups) 3 times by using the Emago(®) (Oral Diagnostic Systems, Amsterdam, Netherlands) with manual selection and Regeemy with manual and automatic selections. After geometric reconstruction on the two software applications under different modes of selection, all images were subtracted and the standard deviation of grey values was obtained as a measure of image noise. All measurements were repeated after 15 days to evaluate the method error. Values of image noise were statistically analyzed by one-way ANOVA for differences between methods and between projection angles, followed by Tukey's test at a level of significance of 5%. RESULTS: Significant differences were found between most of the projection angles for the three reconstruction methods. Image subtraction after manual selection-based reconstruction on Regeemy presented the lowest values of image noise, except on group V0°H0°. The groups V10°H0° and V20°H0° were not significantly different between the manual selection-based reconstruction in Regeemy and automatic selection-based reconstruction in Regeemy methods. CONCLUSIONS: The Regeemy software on manual mode revealed better quality of geometric image reconstruction for DSR than the Regeemy on automatic mode and the Emago on manual mode, when the radiographic images were obtained at V and H angles used in the present investigation.
Keywords:
computer-assisted image processing; digital dental radiography; radiographic image enhancement