Warp the binary TV mask onto the resampled 0.5 mm T1c grid before
visual QA, derive SMD thresholds from the native CT spacing using
normal-distribution theory, and widen the zoom context to 24 voxels.
Keep metrics on the native grid while rendering 0.5 mm slices, and
rename warped outputs to *_tv05.nii.gz.
- Implemented `qa_label_interpolation.py` to evaluate the registration of T1c and CT images using various methods (rigid, affine, and SyN).
- The script generates warped TV images, visualizations, and summary metrics for each registration method.
- Outputs include PNG figures for visual comparison, a summary JSON file with metrics, and cached registration transforms.
- Added functionality for volume preservation checks and detailed logging of processing steps.
