Automated, reference-free local error assessment of multimodal deformable image registration for radiotherapy in the head and neck.
Radiother Oncol
; 125(3): 478-484, 2017 12.
Article
in En
| MEDLINE
| ID: mdl-29100697
ABSTRACT
BACKGROUND:
Head and neck MR-CT deformable image registration (DIR) for radiotherapy planning is hindered by the lack of both ground-truth and per-patient accuracy assessment methods. This study assesses novel post-registration reference-free error assessment algorithms, based on local rigid re-registration of native and pseudomodality images.METHODS:
Head and neck MR obtained in and out of the treatment position underwent DIR to planning CT. Block-wise mutual information (b-MI) and pseudomodality mutual information (b-pmMI) algorithms were validated against applied rotations and translations. Inherent registration error detection was compared across 14 patient datasets.RESULTS:
Using radiotherapy position MR-CT DIR, quantitative comparison of applied rotations and translations revealed that errors between 1 and 4â¯mm were accurately determined by both algorithms. Using diagnostic position MR-CT DIR, translations of up to 5â¯mm were accurately detected within the gross tumour volume by both methods. In 14 patient datasets, b-MI and b-pmMI detected similar errors with improved stability in regions of low contrast or CT artefact and a 10-fold speedup for b-pmMI.CONCLUSIONS:
b-MI and b-pmMI algorithms have been validated as providing accurate reference-free quantitative assessment of DIR accuracy on a per-patient basis. b-pmMI is faster and more robust in the presence of modality-specific information.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tomography, X-Ray Computed
/
Multimodal Imaging
/
Head and Neck Neoplasms
Limits:
Humans
Language:
En
Journal:
Radiother Oncol
Year:
2017
Document type:
Article