| Autor: |
Khallaghi S, Sánchez CA, Rasoulian A, Sun Y, Imani F, Khojaste A, Goksel O, Romagnoli C, Abdi H, Chang S, Mousavi P, Fenster A, Ward A, Fels S, Abolmaesumi P |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2015 Nov; Vol. 34 (11), pp. 2404-14. Date of Electronic Publication: 2015 Jun 03. |
| DOI: |
10.1109/TMI.2015.2440253 |
| Abstrakt: |
In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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