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Journal Articles IEEE Transactions on Radiation and Plasma Medical Sciences Year : 2022

Projection deconvolution for proton CT using the spatially variant path uncertainty

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Abstract

Proton computed tomography (pCT) suffers from a lower spatial resolution compared to X-ray CT due to the stochastic non-linear proton paths. The most likely path (MLP) formalism provides an estimate for the proton path as well as the uncertainty around this estimate. Using the MLP estimate for the image reconstruction instead of straight integration lines has been shown to improve the spatial resolution of pCT images. In this work, the aim is to further increase the spatial resolution by also including the path uncertainty in the reconstruction algorithm. We proposed a projection-based deconvolution method, applied within the framework of a direct reconstruction algorithm based on distance-driven binning. We used an MLP formalism accounting for tracker resolution in addition to multiple Coulomb scattering. We investigated deconvolution artifacts and proposed a method to mitigate them via spatial regularisation. Our method was tested on Monte Carlo simulated data, using a water cylinder with aluminium inserts and two slices of an anthropomorphic phantom. Our results showed an improvement of spatial resolution in all cases (up to 29% or 60% for the cylindrical phantom, depending on whether deconvolution artifacts were corrected for or not). Overshoot artifacts were observed in the case of the cylindrical phantom but were less prominent in the case of the anthropomorphic phantom. In conclusion, we have shown that including the path uncertainty in the reconstruction can notably improve the spatial resolution.
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Dates and versions

hal-03656408 , version 1 (02-05-2022)

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Feriel Khellaf, Nils Krah, Jean Michel Létang, Simon Rit. Projection deconvolution for proton CT using the spatially variant path uncertainty. IEEE Transactions on Radiation and Plasma Medical Sciences, 2022, 6 (8), pp.847-858. ⟨10.1109/TRPMS.2022.3167334⟩. ⟨hal-03656408⟩
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