Prompt gamma emission prediction using a long short-term memory network.

: To present a long short-term memory (LSTM)-based prompt gamma (PG) emission prediction method for proton therapy.: Computed tomography (CT) scans of 33 patients with a prostate tumor were included in the dataset. A set of 10histories proton pencil beam (PB)s was generated for Monte Carlo (MC) dose and PG simulation.

Proton dose calculation with LSTM networks in presence of a magnetic field.

To present a long short-term memory (LSTM) network-based dose calculation method for magnetic resonance (MR)-guided proton therapy.35 planning computed tomography (CT) images of prostate cancer patients were collected for Monte Carlo (MC) dose calculation under a perpendicular 1.5 T magnetic field.

Towards real-time EPID-based 3D in vivo dosimetry for IMRT with Deep Neural Networks: A feasibility study.

We investigate the potential of the Deep Dose Estimate (DDE) neural network to predict 3D dose distributions inside patients with Monte Carlo (MC) accuracy, based on transmitted EPID signals and patient CTs. The network was trained using as input patient CTs and first-order dose approximations (FOD). Accurate dose distributions (ADD) simulated with MC were given as training targets.

Applications of a patient-specific whole-body CT-mesh hybrid computational phantom in second cancer risk prediction.

CT-mesh hybrid phantoms (or 'hybrid(s)') made from integrated patient CT data and mesh-type reference computational phantoms (MRCPs) can be beneficial for patient-specific whole-body dose evaluation, but this benefit has yet to be evaluated for second cancer risk prediction. The purpose of this study is to compare the hybrid's ability to predict risk throughout the body with a patient-scaled MRCP against ground truth whole-body CTs (WBCTs).Head and neck active scanning proton treatment plans were created for and simulated on seven hybrids and the corresponding scaled MRCPs and WBCTs.

Comparison of reconstructed prompt gamma emissions using maximum likelihood estimation and origin ensemble algorithms for a Compton camera system tailored to proton range monitoring.

Compton-based prompt gamma (PG) imaging is being investigated by several groups as a potential solution for in vivo range monitoring in proton therapy. The performance of this technique depends on the detector system as well as the ability of the reconstruction method to obtain good spatial resolution to establish a quantitative correlation between the PG emission and the proton beam range in the patient. To evaluate the feasibility of PG imaging for range monitoring, we quantitatively evaluated the emission distributions reconstructed by a Maximum Likelihood Expectation Maximization (MLEM) and a Stochastic Origin Ensemble (SOE) algorithm.

Comparative accuracy and resolution assessment of two prototype proton computed tomography scanners.

Background: Improving the accuracy of relative stopping power (RSP) in proton therapy may allow reducing range margins. Proton computed tomography (pCT) has been shown to provide state-of-the-art RSP accuracy estimation, and various scanner prototypes have recently been built. The different approaches used in scanner design are expected to impact spatial resolution and RSP accuracy.

A patient-specific hybrid phantom for calculating radiation dose and equivalent dose to the whole body.

. As cancer survivorship increases, there is growing interest in minimizing the late effects of radiation therapy such as radiogenic second cancer, which may occur anywhere in the body. Assessing the risk of late effects requires knowledge of the dose distribution throughout the whole body, including regions far from the treatment field, beyond the typical anatomical extent of clinical computed tomography (CT) scans.

Combining inter-observer variability, range and setup uncertainty in a variance-based sensitivity analysis for proton therapy.

Margin concepts in proton therapy aim to ensure full dose coverage of the clinical target volume (CTV) in presence of setup and range uncertainty. Due to inter-observer variability (IOV), the CTV itself is uncertain. We present a framework to evaluate the combined impact of IOV, setup and range uncertainty in a variance-based sensitivity analysis (SA).

An empirical artifact correction for proton computed tomography.

Purpose: To reduce image artifacts of proton computed tomography (pCT) from a preclinical scanner, for imaging of the relative stopping power (RSP) needed for particle therapy treatment planning using a simple empirical artifact correction method.

Methods: We adapted and employed a correction method previously used for beam-hardening correction in x-ray CT which makes use of a single scan of a custom-built homogeneous phantom with known RSP. Exploiting the linearity of the filtered backprojection operation, a function was found which corrects water-equivalent path lengths (RSP line integrals) in experimental scans using a prototype pCT scanner.

Proof of concept image artifact reduction by energy-modulated proton computed tomography (EMpCT).

Purpose: To reduce imaging artifacts and improve image quality of a specific proton computed tomography (pCT) prototype scanner by combining pCT data acquired at two different incident proton energies to avoid protons stopping in sub-optimal detector sections.

Methods: Image artifacts of a prototype pCT scanner are linked to protons stopping close to internal structures of the scanner's multi-stage energy detector. We aimed at avoiding such protons by acquiring pCT data at two different incident energies and combining the data in post-processing from which artifact-reduced images of the relative stopping power (RSP) were calculated.

Variance-based sensitivity analysis for uncertainties in proton therapy: A framework to assess the effect of simultaneous uncertainties in range, positioning, and RBE model predictions on RBE-weighted dose distributions.

Purpose: Treatment plans in proton therapy are more sensitive to uncertainties than in conventional photon therapy. In addition to setup uncertainties, proton therapy is affected by uncertainties in proton range and relative biological effectiveness (RBE). While to date a constant RBE of 1.

Accounting for prompt gamma emission and detection for range verification in proton therapy treatment planning.

Prompt gamma (PG) imaging is widely investigated as one of the most promising methods for proton range verification in proton therapy. The performance of this technique is affected by several factors like tissue heterogeneity, number of protons in the considered pencil beam and the detection device. Our previous work proposed a new treatment planning concept which boosts the number of protons of a few PG monitoring-friendly pencil beams (PBs), selected on the basis of two proposed indicators quantifying the conformity between the dose and PG at the emission level, above the desired detectability threshold.

Radiation protection modelling for 2.5 Petawatt-laser production of ultrashort x-ray, proton and ion bunches: Monte Carlo model of the Munich CALA facility.

The 'Centre for Advanced Laser Applications' (CALA) is a new research institute for laser-based acceleration of electron beams for brilliant x-ray generation, laser-driven sub-nanosecond bunches of protons and heavy ions for biomedical applications like imaging and tumour therapy as well as for nuclear and high-field physics.The radiation sources emerging from experiments using the up to 2.5 petawatt laser pulses with 25 femtosecond duration will be mixed particle-species of high intensity, high energy and pulsed, thus posing new challenges compared to conventional radiation protection.

A new treatment planning approach accounting for prompt gamma range verification and interfractional anatomical changes.

Prompt gamma (PG) imaging is widely investigated for spot-by-spot in vivo range verification for proton therapy. Previous studies pointed out that the accuracy of prompt gamma imaging is affected by the statistics (number of protons delivered per pencil beam) of the proton beams and the conformity between prompt gamma and dose distribution (PG-dose correlation). Recently a novel approach to re-optimize conventional treatment plans by boosting a few pencil beams with good PG-dose correlation above the statistics limit for reliable PG detectability was proposed.

Towards a novel small animal proton irradiation platform: the SIRMIO project.

Precision small animal radiotherapy research is a young emerging field aiming to provide new experimental insights into tumor and normal tissue models in different microenvironments, to unravel complex mechanisms of radiation damage in target and non-target tissues and assess efficacy of novel therapeutic strategies. For photon therapy, modern small animal radiotherapy research platforms have been developed over the last years and are meanwhile commercially available. Conversely, for proton therapy, which holds potential for an even superior outcome than photon therapy, no commercial system exists yet.

Two-dimensional noise reconstruction in proton computed tomography using distance-driven filtered back-projection of simulated projections.

We present a formalism for two-dimensional (2D) noise reconstruction in proton computed tomography (pCT). This is necessary for the application of fluence modulated pCT (FMpCT) since it permits image noise prescription and the corresponding proton fuence optimization. We aimed at extending previously published formalisms to account for the impact of multiple Coulomb scattering (MCS) on projection noise, and the use of filtered back projection (FBP) reconstruction along curved paths with distance driven binning (DDB).

Gel dosimetry for three dimensional proton range measurements in anthropomorphic geometries.

Proton beams used for radiotherapy have potential for superior sparing of normal tissue, although range uncertainties are among the main limiting factors in the accuracy of dose delivery. The aim of this study was to benchmark an N-vinylpyrrolidone based polymer gel to perform three-dimensional measurement of geometric proton beam characteristics and especially to test its suitability as a range probe in combination with an anthropomorphic phantom. For single proton pencil beams as well as for 3×3cm mono-energy layers depth dose profiles, lateral dose distribution at different depths and proton range were evaluated in simple cubic gel phantoms at different energies from 75 to 115MeV and different dose levels.

Experimental fluence-modulated proton computed tomography by pencil beam scanning.

Purpose: This experimental study is aimed at demonstrating, using a simple cylindrical water phantom, the feasibility of fluence-modulated proton computed tomography (FMpCT) by pencil beam scanning (PBS) proton computed tomography (pCT).

Methods: The phase II pCT prototype of the Loma Linda U. and U.

Monte Carlo proton dose calculations using a radiotherapy specific dual-energy CT scanner for tissue segmentation and range assessment.

Proton beam ranges derived from dual-energy computed tomography (DECT) images from a dual-spiral radiotherapy (RT)-specific CT scanner were assessed using Monte Carlo (MC) dose calculations. Images from a dual-source and a twin-beam DECT scanner were also used to establish a comparison to the RT-specific scanner. Proton ranges extracted from conventional single-energy CT (SECT) were additionally performed to benchmark against literature values.

A Monte-Carlo study to assess the effect of 1.5 T magnetic fields on the overall robustness of pencil-beam scanning proton radiotherapy plans for prostate cancer.

Combining magnetic-resonance imaging (MRI) and proton therapy (PT) using pencil-beam scanning (PBS) may improve image-guided radiotherapy. We aimed at assessing the impact of a magnetic field on PBS-PT plan quality and robustness. Specifically, the robustness against anatomical changes and positioning errors in an MRI-guided scenario with a 30 cm radius 1.

Application of single- and dual-energy CT brain tissue segmentation to PET monitoring of proton therapy.

The purpose of this work was to evaluate the ability of single and dual energy computed tomography (SECT, DECT) to estimate tissue composition and density for usage in Monte Carlo (MC) simulations of irradiation induced β activity distributions. This was done to assess the impact on positron emission tomography (PET) range verification in proton therapy. A DECT-based brain tissue segmentation method was developed for white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF).