Projects per year
Quantifying material mass and electron density from computed tomography (CT) reconstructions can be highly valuable in certain medical practices, such as radiation therapy planning. However, uniquely parameterising the x-ray attenuation in terms of mass or electron density is an ill-posed problem when a single polyenergetic source is used with a spectrally indiscriminate detector. Existing approaches to single source polyenergetic modelling often impose consistency with a physical model, such as water-bone or photoelectric-Compton decompositions, which will either require detailed prior segmentation or restrictive energy dependencies, and may require further calibration to the quantity of interest. In this work, we introduce a data centric approach to fitting the attenuation with piecewise-linear functions directly to mass or electron density, and present a segmentation-free statistical reconstruction algorithm for exploiting it, with the same order of complexity as other iterative methods. We show how this allows both higher accuracy in attenuation modelling, and demonstrate its superior quantitative imaging, with numerical chest and metal implant data, and validate it with real cone-beam CT measurements.
|Number of pages||24|
|Journal||Physics in Medicine & Biology|
|Early online date||1 Nov 2017|
|Publication status||Published - 21 Nov 2017|
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- 2 Finished
Next Generation Compressive and Computational Sensing and Signal Processing
1/10/16 → 30/09/21
Exploiting low dimensional models in sensing, computation and signal processing
1/09/16 → 31/08/22
- School of Engineering - Jeffrey Collins Chair of Signal Processing
Person: Academic: Research Active