David Rivest-Hénault1, Nicholas Dowson2, Peter B Greer3, Jurgen Fripp4, Jason A Dowling5. 1. CSIRO, The Australian e-Health Research Centre, Herston, Queensland 4029, Australia. Electronic address: david.rivest.henault@gmail.com. 2. CSIRO, The Australian e-Health Research Centre, Herston, Queensland 4029, Australia. Electronic address: nicholas.dowson@csiro.au. 3. Calvary Mater Newcastle Hospital, Newcastle, New South Wales 2298, Australia; University of Newcastle, Newcastle, New South Wales 2308, Australia. 4. CSIRO, The Australian e-Health Research Centre, Herston, Queensland 4029, Australia. 5. CSIRO, The Australian e-Health Research Centre, Herston, Queensland 4029, Australia; University of Newcastle, Newcastle, New South Wales 2308, Australia. Electronic address: jason.dowling@csiro.au.
Abstract
BACKGROUND: CT-MR registration is a critical component of many radiation oncology protocols. In prostate external beam radiation therapy, it allows the propagation of MR-derived contours to reference CT images at the planning stage, and it enables dose mapping during dosimetry studies. The use of carefully registered CT-MR atlases allows the estimation of patient specific electron density maps from MRI scans, enabling MRI-alone radiation therapy planning and treatment adaptation. In all cases, the precision and accuracy achieved by registration influences the quality of the entire process. PROBLEM: Most current registration algorithms do not robustly generalize and lack inverse-consistency, increasing the risk of human error and acting as a source of bias in studies where information is propagated in a particular direction, e.g. CT to MR or vice versa. In MRI-based treatment planning where both CT and MR scans serve as spatial references, inverse-consistency is critical, if under-acknowledged. PURPOSE: A robust, inverse-consistent, rigid/affine registration algorithm that is well suited to CT-MR alignment in prostate radiation therapy is presented. METHOD: The presented method is based on a robust block-matching optimization process that utilises a half-way space definition to maintain inverse-consistency. Inverse-consistency substantially reduces the influence of the order of input images, simplifying analysis, and increasing robustness. An open source implementation is available online at http://aehrc.github.io/Mirorr/. RESULTS: Experimental results on a challenging 35 CT-MR pelvis dataset demonstrate that the proposed method is more accurate than other popular registration packages and is at least as accurate as the state of the art, while being more robust and having an order of magnitude higher inverse-consistency than competing approaches. CONCLUSION: The presented results demonstrate that the proposed registration algorithm is readily applicable to prostate radiation therapy planning.
BACKGROUND: CT-MR registration is a critical component of many radiation oncology protocols. In prostate external beam radiation therapy, it allows the propagation of MR-derived contours to reference CT images at the planning stage, and it enables dose mapping during dosimetry studies. The use of carefully registered CT-MR atlases allows the estimation of patient specific electron density maps from MRI scans, enabling MRI-alone radiation therapy planning and treatment adaptation. In all cases, the precision and accuracy achieved by registration influences the quality of the entire process. PROBLEM: Most current registration algorithms do not robustly generalize and lack inverse-consistency, increasing the risk of human error and acting as a source of bias in studies where information is propagated in a particular direction, e.g. CT to MR or vice versa. In MRI-based treatment planning where both CT and MR scans serve as spatial references, inverse-consistency is critical, if under-acknowledged. PURPOSE: A robust, inverse-consistent, rigid/affine registration algorithm that is well suited to CT-MR alignment in prostate radiation therapy is presented. METHOD: The presented method is based on a robust block-matching optimization process that utilises a half-way space definition to maintain inverse-consistency. Inverse-consistency substantially reduces the influence of the order of input images, simplifying analysis, and increasing robustness. An open source implementation is available online at http://aehrc.github.io/Mirorr/. RESULTS: Experimental results on a challenging 35 CT-MR pelvis dataset demonstrate that the proposed method is more accurate than other popular registration packages and is at least as accurate as the state of the art, while being more robust and having an order of magnitude higher inverse-consistency than competing approaches. CONCLUSION: The presented results demonstrate that the proposed registration algorithm is readily applicable to prostate radiation therapy planning.
Authors: Alex M Pagnozzi; Kaikai Shen; James D Doecke; Roslyn N Boyd; Andrew P Bradley; Stephen Rose; Nicholas Dowson Journal: Hum Brain Mapp Date: 2016-11 Impact factor: 5.038
Authors: Alex M Pagnozzi; Nicholas Dowson; Simona Fiori; James Doecke; Andrew P Bradley; Roslyn N Boyd; Stephen Rose Journal: Hum Brain Mapp Date: 2016-06-03 Impact factor: 5.038
Authors: Alex M Pagnozzi; Nicholas Dowson; James Doecke; Simona Fiori; Andrew P Bradley; Roslyn N Boyd; Stephen Rose Journal: Neuroimage Clin Date: 2016-05-29 Impact factor: 4.881