Michael G Nix1, Robin J D Prestwich2, Richard Speight3. 1. Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, UK. Electronic address: michael.nix@nhs.net. 2. Department of Clinical Oncology, Leeds Teaching Hospitals NHS Trust, UK. 3. Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, UK.
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 4 mm were accurately determined by both algorithms. Using diagnostic position MR-CT DIR, translations of up to 5 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.
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 4 mm were accurately determined by both algorithms. Using diagnostic position MR-CT DIR, translations of up to 5 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.
Authors: Molly M McCulloch; Brian M Anderson; Guillaume Cazoulat; Christine B Peterson; Abdallah S R Mohamed; Stefania Volpe; Hesham Elhalawani; Houda Bahig; Bastien Rigaud; Jason B King; Alexandra C Ford; Clifton D Fuller; Kristy K Brock Journal: Phys Med Biol Date: 2019-09-05 Impact factor: 3.609
Authors: Jeffrey Barber; Johnson Yuen; Michael Jameson; Laurel Schmidt; Jonathan Sykes; Alison Gray; Nicholas Hardcastle; Callie Choong; Joel Poder; Amy Walker; Adam Yeo; Ben Archibald-Heeren; Kristie Harrison; Annette Haworth; David Thwaites Journal: J Med Radiat Sci Date: 2020-08-02