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Adjoint image warping using multivariate splines with application to four‐dimensional computed tomography

PURPOSE: Adjoint image warping is an important tool to solve image reconstruction problems that warp the unknown image in the forward model. This includes four‐dimensional computed tomography (4D‐CT) models in which images are compared against recorded projection images of various time frames using...

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Autores principales: Renders, Jens, Sijbers, Jan, De Beenhouwer, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9291926/
https://www.ncbi.nlm.nih.gov/pubmed/34407210
http://dx.doi.org/10.1002/mp.14765
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author Renders, Jens
Sijbers, Jan
De Beenhouwer, Jan
author_facet Renders, Jens
Sijbers, Jan
De Beenhouwer, Jan
author_sort Renders, Jens
collection PubMed
description PURPOSE: Adjoint image warping is an important tool to solve image reconstruction problems that warp the unknown image in the forward model. This includes four‐dimensional computed tomography (4D‐CT) models in which images are compared against recorded projection images of various time frames using image warping as a model of the motion. The inversion of these models requires the adjoint of image warping, which up to now has been substituted by approximations. We introduce an efficient implementation of the exact adjoints of multivariate spline based image warping, and compare it against previously used alternatives. METHODS: Using symbolic computer algebra, we computed a list of 64 polynomials that allow us to compute a matrix representation of trivariate cubic image warping. By combining an on‐the‐fly computation of this matrix with a parallelized implementation of columnwise matrix multiplication, we obtained an efficient, low memory implementation of the adjoint action of 3D cubic image warping. We used this operator in the solution of a previously proposed 4D‐CT reconstruction model in which the image of a single subscan was compared against projection data of multiple subscans by warping and then projecting the image. We compared the properties of our exact adjoint with those of approximate adjoints by warping along inverted motion. RESULTS: Our method requires halve the memory to store motion between subscans, compared to methods that need to compute and store an approximate inverse of the motion. It also avoids the computation time to invert the motion and the tunable parameter of the number of iterations used to perform this inversion. Yet, a similar and often better reconstruction quality was obtained in comparison with these more expensive methods, especially when the motion is large. When compared against a simpler method that is similar to ours in computational demands, our method achieves a higher reconstruction quality in general. CONCLUSIONS: Our implementation of the exact adjoint of cubic image warping improves efficiency and provides accurate reconstructions.
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spelling pubmed-92919262022-07-20 Adjoint image warping using multivariate splines with application to four‐dimensional computed tomography Renders, Jens Sijbers, Jan De Beenhouwer, Jan Med Phys SPECIAL ISSUE: 6TH INTERNATIONAL CONFERENCE ON IMAGE FORMATION IN X‐RAY COMPUTED TOMOGRAPHY PURPOSE: Adjoint image warping is an important tool to solve image reconstruction problems that warp the unknown image in the forward model. This includes four‐dimensional computed tomography (4D‐CT) models in which images are compared against recorded projection images of various time frames using image warping as a model of the motion. The inversion of these models requires the adjoint of image warping, which up to now has been substituted by approximations. We introduce an efficient implementation of the exact adjoints of multivariate spline based image warping, and compare it against previously used alternatives. METHODS: Using symbolic computer algebra, we computed a list of 64 polynomials that allow us to compute a matrix representation of trivariate cubic image warping. By combining an on‐the‐fly computation of this matrix with a parallelized implementation of columnwise matrix multiplication, we obtained an efficient, low memory implementation of the adjoint action of 3D cubic image warping. We used this operator in the solution of a previously proposed 4D‐CT reconstruction model in which the image of a single subscan was compared against projection data of multiple subscans by warping and then projecting the image. We compared the properties of our exact adjoint with those of approximate adjoints by warping along inverted motion. RESULTS: Our method requires halve the memory to store motion between subscans, compared to methods that need to compute and store an approximate inverse of the motion. It also avoids the computation time to invert the motion and the tunable parameter of the number of iterations used to perform this inversion. Yet, a similar and often better reconstruction quality was obtained in comparison with these more expensive methods, especially when the motion is large. When compared against a simpler method that is similar to ours in computational demands, our method achieves a higher reconstruction quality in general. CONCLUSIONS: Our implementation of the exact adjoint of cubic image warping improves efficiency and provides accurate reconstructions. John Wiley and Sons Inc. 2021-08-18 2021-10 /pmc/articles/PMC9291926/ /pubmed/34407210 http://dx.doi.org/10.1002/mp.14765 Text en © 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle SPECIAL ISSUE: 6TH INTERNATIONAL CONFERENCE ON IMAGE FORMATION IN X‐RAY COMPUTED TOMOGRAPHY
Renders, Jens
Sijbers, Jan
De Beenhouwer, Jan
Adjoint image warping using multivariate splines with application to four‐dimensional computed tomography
title Adjoint image warping using multivariate splines with application to four‐dimensional computed tomography
title_full Adjoint image warping using multivariate splines with application to four‐dimensional computed tomography
title_fullStr Adjoint image warping using multivariate splines with application to four‐dimensional computed tomography
title_full_unstemmed Adjoint image warping using multivariate splines with application to four‐dimensional computed tomography
title_short Adjoint image warping using multivariate splines with application to four‐dimensional computed tomography
title_sort adjoint image warping using multivariate splines with application to four‐dimensional computed tomography
topic SPECIAL ISSUE: 6TH INTERNATIONAL CONFERENCE ON IMAGE FORMATION IN X‐RAY COMPUTED TOMOGRAPHY
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9291926/
https://www.ncbi.nlm.nih.gov/pubmed/34407210
http://dx.doi.org/10.1002/mp.14765
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