McClelland, JR;
Modat, M;
Arridge, S;
Grimes, H;
D'Souza, D;
Thomas, D;
O'Connell, D;
... Hawkes, D; + view all
(2017)
A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images.
Physics in Medicine and Biology
, 62
(11)
, Article 4273. 10.1088/1361-6560/aa6070.
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Abstract
Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of 'partial' imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative approach, so that both the motion and a motion-free image can be estimated from the partial image data. The framework has been applied to real 4DCT, Cine CT, multi-slice CT, and multi-slice MR data, as well as simulated datasets from a computer phantom. This includes the use of a super-resolution reconstruction method for the multi-slice MR data. Good results were obtained for all datasets, including quantitative results for the 4DCT and phantom datasets where the ground truth motion was known or could be estimated.
| Type: | Article |
|---|---|
| Title: | A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1361-6560/aa6070 |
| Publisher version: | http://doi.org/10.1088/1361-6560/aa6070 |
| Language: | English |
| Additional information: | As the Version of Record of this article is going to be/has been published on a gold open access basis under a CC BY 3.0 licence, this Accepted Manuscript is available for reuse under a CC BY 3.0 licence immediately. Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permission may be required. All third party content is fully copyright protected, and is not published on a gold open access basis under a CC BY licence, unless that is specifically stated in the figure caption in the Version of Record. |
| Keywords: | CT, Image registration, MR, Motion compensated image reconstruction, Respiratory motion modelling, Respiratory surrogate signals, Super-resolution |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1542262 |
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