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Region of interest (ROI) alignment in medical images plays a crucial role indiagnostics, procedure planning, treatment, and follow-up. Frequently, a modelis represented as triangulated mesh while the patient data is provided from CATscanners as pixel or voxel data. Previously, we presented a 2D method forcurve-to-pixel registration. This paper contributes (i) a generalmesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a3D surface-to-voxel application, and (iii) a comprehensive quantitativeevaluation in 2D using ground truth provided by the simultaneous truth andperformance level estimation (STAPLE) method. The registration is formulated asa minimization problem where the objective consists of a data term, whichinvolves the signed distance function of the ROI from the reference image, anda higher order elastic regularizer for the deformation. The evaluation is basedon quantitative light-induced fluoroscopy (QLF) and digital photography (DP) ofdecalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, eachshowing one corresponding tooth in both modalities. The ROI in each image ismanually marked by three experts (900 curves in total). In the QLF-DP setting,our approach significantly outperforms the mutual information-basedregistration algorithm implemented with the Insight Segmentation andRegistration Toolkit (ITK) and Elastix.

Mesh-to-raster based non-rigid registration of multi-modal images