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Proceedings Paper

Image-based synthetic CT: simulating arbitrary low dose single and dual energy protocols from dual energy images
Author(s): Adam S. Wang; Charles Feng; Norbert J. Pelc
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Paper Abstract

While the imaging protocol determines radiation dose and image quality, it is difficult to find the lowest dose protocol (kVp, mAs, filtration) that provides appropriate diagnostic quality images. Recently, we developed a method for retrospectively synthesizing CT scans of arbitrary protocols using a previously acquired dual energy scan that relies on projection space data. Here, we propose a new variant of synthetic CT that only requires reconstructed dual energy images to simulate realistic images from arbitrary low dose protocols. Axial scans of a phantom were acquired on a GE CT750 HD system at 80 kVp and separately at 140 kVp, enabling material decomposition. Additional scans at 100 and 120 kVp and at different exposures were made to compare with synthesized results. Raw data for any spectrum can be estimated by forward transmission through the material decomposition images, but these have degraded spatial resolution. To avoid blurring, the synthesized image is represented as a linear combination (i.e., mixed or blended image) of the 80/140 kVp images. Noise with the correct statistics is then added so that the total noise matches the expected noise of the simulated protocol (estimated from forward transmission). For the studied object, the resulting synthesized images are indistinguishable from the actual images. Synthetic CT enables users to visualize the impact of protocol changes on the contrast and noise of CT scans, which can be used to develop lower dose protocols by demonstrating dose/noise/protocol trade-offs. Our new image domain implementation significantly increases the accessibility of synthetic CT to potential users.

Paper Details

Date Published: 3 March 2012
PDF: 7 pages
Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83131G (3 March 2012); doi: 10.1117/12.912163
Show Author Affiliations
Adam S. Wang, Stanford Univ. (United States)
Charles Feng, Stanford Univ. (United States)
Norbert J. Pelc, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 8313:
Medical Imaging 2012: Physics of Medical Imaging
Norbert J. Pelc; Robert M. Nishikawa; Bruce R. Whiting, Editor(s)

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