UCLA

Purpose

To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.

Methods and materials

Twenty-five head-and-neck patients received magnetic resonance (MR) and computed tomography (CT) (CT_aligned ) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR-CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty-four of 25 patients also had a separate CT without immobilization (CT_non-aligned ) and were used for testing. CT_non-aligned 's were deformed to the synthetic CT, and compared to CT_non-aligned registered to MR. The same registrations were performed from MR to CT_non-aligned and from synthetic CT to CT_non-aligned . All registrations used B-splines for modeling the deformation, and mutual information for the objective. Results were evaluated using the 95% Hausdorff distance among spinal cord contours, landmark error, inverse consistency, and Jacobian determinant of the estimated deformation fields.

Results

When large initial rigid misalignment is present, registering CT to MRI-derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MR→CT_non-aligned to 6.0 ± 2.1 mm in CT_synth →CT_non-aligned deformable registrations. In the CT to MR direction, the landmark error decreased from 10.0 ± 4.3 mm in CT_non-aligned →MR deformable registrations to 6.6 ± 2.0 mm in CT_non-aligned →CT_synth deformable registrations. The Jacobian determinant had an average value of 0.98. The proposed method also demonstrated improved inverse consistency over the direct method.

Conclusions

We showed that using a deep learning-derived synthetic CT in lieu of an MR for MR→CT and CT→MR deformable registration offers superior results to direct multimodal registration.