A likelihood-based particle imaging filter using prior information

doi:10.1002/mp.16258

A likelihood-based particle imaging filter using prior information

2023-02-18

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Charles‐Antoine Collins‐Fekete

DOI：10.1002/mp.16258

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BackgroundParticle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality, especially for multinucleon ions that can fragment, such as helium. PurposeThis work proposes a particle imaging filter, referred to as the Prior Filter, based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise. MethodsThe Prior Filter uses Fermi-Eyges scattering and Tschalar straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3 sigma filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories. Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of-the-art method including an implementation of the 3 sigma filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied. ResultsThe proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5-6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3 sigma filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of-the-art filtering method. ConclusionsThe proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction.

Keywords:

bespoke filter

helium CT

ion imaging

noise reduction

proton CT

A likelihood-based particle imaging filter using prior information

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