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001-es BibID:BIBFORM095804
Első szerző:Opposits Gábor (fizikus, szoftver fejlesztő)
Cím:Automated procedure assessing the accuracy of HRCT-PET registration applied in functional virtual bronchoscopy / Gábor Opposits, Marianna Nagy, Zoltán Barta, Csaba Aranyi, Dániel Szabó, Attila Makai, Imre Varga, László Galuska, Lajos Trón, László Balkay, Miklós Emri
Dátum:2021
ISSN:2191-219X
Megjegyzések:Background: Bronchoscopy serves as direct visualisation of the airway. Virtual bronchoscopy provides similar visual information using a non-invasive imaging procedure(s). Early and accurate image-guided diagnosis requires the possible highest performance, which might be approximated by combining anatomical and functional imaging. This communication describes an advanced functional virtual bronchoscopic (fVB) method based on the registration of PET images to high-resolution diagnostic CT images instead of low-dose CT images of lower resolution obtained from PET/CT scans. PET/CT and diagnostic CT data were collected from 22 oncological patients to develop a computer-aided high-precision fVB. Registration of segmented images was performed using elastix. Results: For virtual bronchoscopy, we used an in-house developed segmentation method. The quality of low- and high-dose CT image registrations was characterised by expert's scoring the spatial distance of manually paired corresponding points and by eight voxel intensity-based (dis)similarity parameters. The distribution of (dis)similarity parameter correlating best with anatomic scoring was bootstrapped, and 95% confidence intervals were calculated separately for acceptable and insufficient registrations. We showed that mutual information (MI) of the eight investigated (dis)similarity parameters displayed the closest correlation with the anatomy-based distance metrics used to characterise the quality of image registrations. The 95% confidence intervals of the bootstrapped MI distribution were [0.15, 0.22] and [0.28, 0.37] for insufficient and acceptable registrations, respectively. In case of any new patient, a calculated MI value of registered low- and high-dose CT image pair within the [0.28, 0.37] or the [0.15, 0.22] interval would suggest acceptance or rejection, respectively, serving as an aid for the radiologist. Conclusion: A computer-aided solution was proposed in order to reduce reliance on radiologist's contribution for the approval of acceptable image registrations.
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Computed tomography
Diagnostics
Image registration
Image segmentation
Image-guided bronchoscopy
Megjelenés:EJNMMI Research. - 11 : 1 (2021), p. 1-13. -
További szerzők:Nagy Marianna (1987-) (orvosdiagnosztikai képalkotó) Barta Zoltán (1974-) (nukleáris medicina szakorvos) Aranyi Sándor Csaba (1988-) (programtervező informatikus) Szabó Dániel (1986-) (gépészmérnök) Makai Attila (1987-) (tüdőgyógyász szakorvos) Varga Imre (1960-) (tüdőgyógyász) Galuska László (1946-) (belgyógyász, izotópdiagnoszta) Trón Lajos (1941-) (biofizikus) Balkay László (1963-) (biofizikus) Emri Miklós (1962-) (fizikus)
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001-es BibID:BIBFORM081500
Első szerző:Opposits Gábor (fizikus, szoftver fejlesztő)
Cím:Accuracy of Low Dose and Diagnostic CT Image Registration of Bronchial Tree for Virtual Bronchoscopy / G. Opposits, M. Nagy, Cs. Aranyi, M. Emri, L. Balkay
Dátum:2019
Megjegyzések:The aim of this study is to support medical experts to be able to make an order among large number of automatic registration. The experts could tackle with the most problematic cases due to the inaccuracy of automatic registration procedure in the vicinity of the bronchus to help virtual bronchoscopy (VB) systems. Functional images (e.g. PET) can be projected on the relevant part of the organ that is examined in VB systems. We collected cases where the difference between the time of low-dose (ldCT) and diagnostic CT (hdCT) was less than one year. Altogether 22 anonymous ldCT and hdCT studies were selected in this study. Based on the literature, a potential candidate for image registration was elastix. We applied a specific in-house developed application for image preprocessing, before the elastix registration. We tried to resolve the goodness of the entire registration process by visual judgment combining it with special numerical features. Numerical data include the mutual information, standardized mutual information, Kullback-Leibler entropy, cross correlation, L1norm, L2norm square. We found satisfying correlation between mutual information and visual judgment in the close vicintiy of airtree. We calculated confident intervals for MI of acceptable and rejected registrations about the mean values of it by bootstraping method: [0.27, 0.36], [0.13, 0.19].
Tárgyszavak:Orvostudományok Klinikai orvostudományok előadáskivonat
könyvrészlet
Megjelenés:2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). - p. 1-2. -
További szerzők:Nagy Marianna (1987-) (orvosdiagnosztikai képalkotó) Aranyi Sándor Csaba (1988-) (programtervező informatikus) Emri Miklós (1962-) (fizikus) Balkay László (1963-) (biofizikus)
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DOI
Intézményi repozitóriumban (DEA) tárolt változat
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