A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder.
A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder.
Phys Med Biol. 2020 Apr 15;:
Authors: van der Heyden B, Uray M, Fonseca GP, Huber P, Us D, Messner I, Law A, Parii A, Reisz N, Rinaldi I, Vilches-Freixas G, Deutschmann H, Verhaegen F, Steininger P
Abstract
The primary cone-beam computed tomography (CBCT) imaging beam scatters inside the patient and produces a contaminating photon fluence that is registered by the detector. Scattered photons cause artifacts in the image reconstruction, and are partially responsible for the inferior image quality compared to diagnostic fan-beam CT. In this work, a deep convolutional autoencoder (DCAE) and projection-based scatter removal algorithm were constructed for the ImagingRingTM system on rails (IRr), which allows for non-isocentric acquisitions around virtual rotation centers with its independently rotatable source and detector arms. A Monte Carlo model was developed to simulate (i) a non-isocentric training dataset of 1200 projection pairs (primary + scatter) from 27 digital head-and-neck cancer patients around five different virtual rotation centers (DCAENONISO), and (ii) an isocentric dataset existing of 1200 projection pairs around the physical rotation center (DCAEISO). The scatter removal performance of both DCAE networks was investigated in two digital anthropomorphic phantom simulations and due to superi...
Source: Physics in Medicine and Biology - Category: Physics Authors: van der Heyden B, Uray M, Fonseca GP, Huber P, Us D, Messner I, Law A, Parii A, Reisz N, Rinaldi I, Vilches-Freixas G, Deutschmann H, Verhaegen F, Steininger P Tags: Phys Med Biol Source Type: research
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