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Background: Particle 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., Purpose: This 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., Methods: The Prior Filter uses Fermi-Eyges scattering and Tschalär 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 3σ 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 3σ filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied., Results: The 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 3σ 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., Conclusions: The 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., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: Patient motion during a positron emission tomography (PET) scan can lead to significant resolution loss. Two of the main motion correction techniques employed to ameliorate the loss of image quality due to respiratory motion in the torso are postreconstruction registration (PRR) and motion-compensated image reconstruction (MCIR). In this study, the authors investigated whether versions of these methods that utilize registration-based weighting of the constituent respiratory gated data offer any advantage over the standard versions that use equal weighting. The registration-based weights were designed to penalize gates that were poorly registered to the reference gate., Methods: SimSET was used to simulate data from the NCAT phantom with six lesions added in the lung and liver. Images were reconstructed using registration-weighted PRR and MCIR algorithms, where the registration weighting was based on the mutual information (MI) of each registered gate and the reference gate. More specifically, relative to equal weighting, for which the weight for each gate is the inverse of the number of gates, the weights were increased for MI greater than the average MI and reduced for gates with MI less than the average MI. A scale factor was used to increase the range of the weights, and PRR and MCIR images were produced for a range of scale factor values., Results: At the optimal values of the scale factor, registration-weighted PRR produced significantly higher contrast-to-noise ratio (CNR) for each lesion than PRR (p < 0.001), with average lesion CNR increasing significantly from (2.10 ± 0.05) to (2.70 ± 0.06) for 3 mm postsmoothing (p < 0.001) and from (2.03 ± 0.06) to (2.77 ± 0.05) for 6 mm postsmoothing (p < 0.001). Likewise, for MCIR registration weighting significantly increased the average CNR from (2.38 ± 0.04) to (2.62 ± 0.07) for 3 mm postsmoothing (p < 0.001) and from (2.56 ± 0.05) to (2.84 ± 0.08) for 6 mm postsmoothing (p < 0.001). These gains in lesion CNR were obtained despite corresponding reductions in signal-to-noise ratio, as expected from the use of unequal weighting of gated data with comparable variance., Conclusions: Registration weighting can significantly improve lesion CNR in motion corrected images, especially for PRR.

Purpose: One limitation of positron emission tomography (PET) imaging of the torso is patient motion. Motion-compensated image reconstruction (MCIR) is one method employed to reduce the deleterious effects of motion. Existing MCIR algorithms use a single sensitivity correction term, which provides inexact normalization for multigate data. Consequently, in this study, the authors derive and examine the performance of an MCIR algorithm with sensitivity correction per gate. In addition, they demonstrate an approximate tube-of-response (TOR) backprojector., Methods: Simulated data from the NCAT phantom with six lesions added were used to compare MCIR algorithms with and without the incorporation of sensitivity correction per gate and TOR backprojection to postreconstruction registration (PRR) and images reconstructed without motion correction. To make the simulations more realistic, intragate motion was included. Deformation fields were determined from NCAT anatomical images using a free-form deformation approach with bending energy regularization., Results: Sensitivity correction per gate and TOR backprojection improved mean lesion contrast-to-noise ratio by 6%-8%, with the maximum increase (21%-23%) found for the smallest lesion. These increases were obtained despite a small increase (3%) in noise as measured by standard deviation in a uniform lung region. Sensitivity correction per gate comes at no extra computational cost, whilst replacing line-of-response backprojection with TOR backprojection increased the overall computation time by ∼20%. In addition, MCIR was found to be superior to PRR, with one factor contributing to this difference being the differential impact of interpolation following deformation. MCIR was also shown to exhibit super-resolution., Conclusions: Replacing a single sensitivity correction term in MCIR with sensitivity correction per gate improves lesion detectability. For a small increase in computational expense, further improvements are achieved using an approximate TOR backprojector rather than line-of-response backprojection.