Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy
| Autor: | László Balkay, Lajos Trón, Attila Makai, László Galuska, Gábor Opposits, Zoltán Barta, Miklós Emri, Dániel Szabó, Marianna Nagy, Csaba Aranyi, Imre Varga |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Image-guided bronchoscopy
R895-920 Image registration 030218 nuclear medicine & medical imaging 03 medical and health sciences Medical physics. Medical radiology. Nuclear medicine 0302 clinical medicine Similarity (network science) Bronchoscopy Medicine Radiology Nuclear Medicine and imaging Segmentation Computed tomography Diagnostics Cardiac imaging Original Research Image segmentation medicine.diagnostic_test business.industry Mutual information Confidence interval 030220 oncology & carcinogenesis business Nuclear medicine |
| Zdroj: | EJNMMI Research, Vol 11, Iss 1, Pp 1-13 (2021) EJNMMI Research |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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