Evaluation of the impact of a scanner prototype on proton CT and helium CT image quality and dose efficiency with Monte Carlo simulation.

Objective.The use of ion computed tomography (CT) promises to yield improved relative stopping power (RSP) estimation as input to particle therapy treatment planning. Recently, proton CT (pCT) has been shown to yield RSP accuracy on par with state-of-the-art x-ray dual energy CT. There are however concerns that the lower spatial resolution of pCT compared to x-ray CT may limit its potential, which has spurred interest in the use of helium ion CT (HeCT). The goal of this study was to investigate image quality of pCT and HeCT in terms of noise, spatial resolution, RSP accuracy and imaging dose using a detailed Monte Carlo (MC) model of an existing ion CT prototype.Approach.Three phantoms were used in simulated pCT and HeCT scans allowing estimation of noise, spatial resolution and the scoring of dose. An additional phantom was used to evaluate RSP accuracy. The imaging dose required to achieve the same image noise in a water and a head phantom was estimated at both native spatial resolution, and in a scenario where the HeCT spatial resolution was reduced and matched to that of pCT using Hann windowing of the reconstruction filter. A variance reconstruction formalism was adapted to account for Hann windowing.Main results.We confirmed that the scanner prototype would produce higher spatial resolution for HeCT than pCT by a factor 1.8 (0.86 lp mm-1versus 0.48 lp mm-1at the center of a 20 cm water phantom). At native resolution, HeCT required a factor 2.9 more dose than pCT to achieve the same noise, while at matched resolution, HeCT required only 38% of the pCT dose. Finally, RSP mean absolute percent error (MAPE) was found to be 0.59% for pCT and 0.67% for HeCT.Significance.This work compared the imaging performance of pCT and HeCT when using an existing scanner prototype, with the spatial resolution advantage of HeCT coming at the cost of increased dose. When matching spatial resolution via Hann windowing, HeCT had a substantial dose advantage. Both modalities provided state-of-the-art RSP MAPE. HeCT might therefore help reduce the dose exposure of patients with comparable image noise to pCT, enhanced spatial resolution and acceptable RSP accuracy at the same time.

Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept.

A method is proposed to model by a generative adversarial network the distribution of particles exiting a patient during Monte Carlo simulation of emission tomography imaging devices. The resulting compact neural network is then able to generate particles exiting the patient, going towards the detectors, avoiding costly particle tracking within the patient. As a proof of concept, the method is evaluated for single photon emission computed tomography (SPECT) imaging and combined with another neural network modeling the detector response function (ARF-nn). A complete rotating SPECT acquisition can be simulated with reduced computation time compared to conventional Monte Carlo simulation. It also allows the user to perform simulations with several imaging systems or parameters, which is useful for imaging system design.

Scattering proton CT.

Proton computed tomography (CT) is an imaging modality investigated mainly in the context of proton therapy as a complement to x-ray CT. It uses protons with high enough energy to fully traverse the imaged object. Common prototype systems measure each proton's position and direction upstream and downstream of the object as well as the energy loss which can be converted into the water equivalent thickness. A reconstruction algorithm then produces a map of the relative stopping power in the object. As an alternative to energy-loss proton CT, it has been proposed to reconstruct a map of the object's scattering power based on the protons' angular dispersion which can be estimated from the measured directions. As in energy-loss proton CT, reconstruction should best be performed considering the non-linear shape of proton trajectories due to multiple Coulomb scattering (MCS), but no algorithm to achieve this is so far available in the literature. In this work, we propose a filtered backprojection algorithm with distance-driven binning to account for the protons' most likely path. Furthermore, we present a systematic study of scattering proton CT in terms of inherent noise and spatial resolution and study the artefacts which arise from the physics of MCS. Our analysis is partly based on analytical models and partly on Monte Carlo simulations. Our results show that the proposed algorithm performs well in reconstructing relative scattering power maps, i.e. scattering power relative to that of water. Spatial resolution is improved by almost a factor of three compared to straight line projection and is comparable to energy-loss proton CT. Image noise, on the other hand, is inherently much higher. For example, in a water cylinder of 20 cm diameter, representative of a human head, noise in the central image pixel is about 40 times higher in scattering proton CT than in energy-loss proton CT. Relative scattering power in dense regions such as bone inserts is systematically underestimated by a few percent, depending on beam energy and phantom geometry.

2D directional ramp filter.

Usual tomographic reconstruction methods start by filtering projections before backprojecting the data. In some cases, inverting the filtering and the backprojection steps can be useful to preserve spatial information. In this paper, an intermediate between a filter-backproject and a backproject-filter approach is proposed, based on the extension of the usual ramp filter to two dimensions. To this end, an expression for a band-limited 2D version of the ramp filter is derived. We have tested this filter on simulated x-ray CT projections of a Shepp-Logan phantom and on proton CT list-mode data. We accurately reconstructed the x-ray CT and the proton CT data, although the reconstruction can be slightly noisier than a standard filtered backprojection in some cases. A slight improvement of the spatial resolution of proton CT images reconstructed with this 2D filter has been observed.

Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations.

A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact generative adversarial network (GAN). The GAN is trained based on a phase space dataset to create a neural network, called Generator (G), allowing G to mimic the multidimensional data distribution of the phase space. At the end of the training process, G is stored with about 0.5 million weights, around 10 MB, instead of a few GB of the initial file. Particles are then generated with G to replace the phase space dataset. This concept is applied to beam models from linear accelerators (linacs) and from brachytherapy seed models. Simulations using particles from the reference phase space on one hand and those generated by the GAN on the other hand were compared. 3D distributions of deposited energy obtained from source distributions generated by the GAN were close to the reference ones, with less than 1% of voxel-by-voxel relative difference. Sharp parts such as the brachytherapy emission lines in the energy spectra were not perfectly modeled by the GAN. Detailed statistical properties and limitations of the GAN-generated particles still require further investigation, but the proposed exploratory approach is already promising and paves the way for a wide range of applications.

Polynomial modelling of proton trajectories in homogeneous media for fast most likely path estimation and trajectory simulation.

Protons undergo many small angle deflections when traversing a medium, such as a patient. This effect, known as multiple Coulomb scattering (MCS), leads to degraded image resolution in proton radiography and computed tomography (CT) and to lateral spreading of the dose distribution in proton therapy. To optimally account for MCS in proton imaging, the most likely path (MLP) of a proton is estimated based on its position and propagation angle measured in front of and behind the object. In this work, we propose a functional which quantifies the likelihood of a proton trajectory and study how it can be used to model proton trajectories in a homogeneous medium. We focus on two aspects: first, we present an analytical method to quickly generate proton trajectories in a homogeneous medium based on the likelihood functional and validate it through Monte Carlo simulations. It could be used for fast generation of proton CT images without a full Monte Carlo simulation, or potentially to complement the components in a treatment planning Monte Carlo which simulate MCS. Second, by maximising the likelihood functional, we derive an expression for the MLP which is equivalent to the conventional ones reported in the literature yet computationally more convenient. Moreover, we show that the MLP is strictly a polynomial function if the protons' energy loss in the medium is approximated as a polynomial and that the orders of both are linked. We validate our MLP through Monte Carlo simulations and compare proton CT images reconstructed with our expression and with the conventional one. We find that an MLP polynomial of orders larger than five do not lead to increased spatial resolution compared to lower order expressions.

Regularised patient-specific stopping power calibration for proton therapy planning based on proton radiographic images.

Proton transmission imaging has been proposed and investigated as imaging modality complementary to x-ray based techniques in proton beam therapy. In particular, it addresses the issue of range uncertainties due to the conversion of an x-ray patient computed tomography (CT) image expressed in Hounsfield Units (HU) to relative stopping power (RSP) needed as input to the treatment planning system. One approach to exploit a single proton radiographic projection is to perform a patient-specific calibration of the CT to RSP conversion curve by optimising the match between a measured and a numerically integrated proton radiography. In this work, we develop the mathematical tools needed to perform such an optimisation in an efficient and robust way. Our main focus lies on set-ups which combine pencil beam scanning with a range telescope detector, although most of our methods can be employed in combination with other set-ups as well. Proton radiographies are simulated in Monte Carlo using an idealised detector and applying the same data processing chain used with experimental data. This approach allows us to have a ground truth CT-RSP curve to compare the optimisation results with. Our results show that the parameters of the CT-RSP curve are strongly correlated when using a pencil beam based set-up, which leads to unrealistic variation in the optimised CT-RSP curves. To address this issue, we introduce a regularisation procedure which guarantees a plausible degree of smoothness in the optimised CT-RSP curves. We investigate three different methods to perform the numerical projection operation needed to generate a proton digitally reconstructed radiography. We find that the approximate and computationally faster method performs as well as the more accurate but more demanding method. We perform a Monte Carlo experiment based on a head and neck patient to evaluate the range accuracy achievable with the optimised CT-RSP curves and find an agreement with the ground truth expectation of better than [Formula: see text]. Our results further indicate that the region in the patient in which the proton radiography is acquired does not necessarily have to correspond to the treatment volume to achieve this accuracy. This is important as the imaged region could be freely chosen, e.g. in order to spare organs at risk.

Learning SPECT detector angular response function with neural network for accelerating Monte-Carlo simulations.

A method to speed up [Formula: see text] simulations of single photon emission computed tomography (SPECT) imaging is proposed. It uses an artificial neural network (ANN) to learn the angular response function (ARF) of a collimator-detector system. The ANN is trained once from a complete simulation including the complete detector head with collimator, crystal, and digitization process. In the simulation, particle tracking inside the SPECT head is replaced by a plane. Photons are stopped at the plane and the energy and direction are used as input to the ANN, which provides detection probabilities in each energy window. Compared to histogram-based ARF, the proposed method is less dependent on the statistics of the training data, provides similar simulation efficiency, and requires less training data. The implementation is available within the GATE platform.

Proton radiography with a commercial range telescope detector using dedicated post processing methods.

Proton transmission imaging uses protons with high enough energy to fully traverse the phantom/patient and to be captured in a suitable detector placed behind it. The measured residual energy or residual range provide a direct estimate of the water equivalent thickness (WET) of the image volume. Requirements for proton imaging to be exploitable in clinical practice include: sufficient WET accuracy and integrability into the treatment room and the clinical workflow, as well as an acceptably low dose to the patient and a sufficient spatial resolution. In this work, we report on experiments performed at the Institut Curie-Proton therapy center in Orsay (IC-CPO), France, using a commercial range telescope commonly employed for quality assurance measurements. The purpose was to keep the experimental set-up as simple as possible and to achieve nonetheless high WET accuracy radiographies by developing and applying dedicated post processing methods. We explain these methods in detail and discuss their performance. We assess the WET accuracy based on two different reference phantoms: a CIRS electron density phantom with tissue equivalent inserts and a homogeneous step phantom. We find an agreement between the measured and the reference WET values of 0.2-0.5 mm. The lowest investigated dose was 10 mGy per acquisition. It could be lowered by modifying the irradiation plan and lowering the beam current, though the latter would impose slight optimisations of the detector hardware. Our work suggests that proton radiographies with good WET accuracy can be obtained with a reasonable experimental effort that would facilitate integration into clinical routine.

A comprehensive theoretical comparison of proton imaging set-ups in terms of spatial resolution.

We present a comprehensive analytical comparison of four types of proton imaging set-ups and, to this end, develop a mathematical framework to calculate the width of the uncertainty envelope around the most likely proton path depending on set-up geometry, detector properties, and proton beam parameters. As a figure of merit for the spatial resolution achievable with each set-up, we use the frequency [Formula: see text] at which the modular transfer function of a density step decreases below 10%. We verify the analytical results with Monte Carlo simulations. We find that set-ups which track the angle and position of individual protons in front of and behind the phantom would yield an average spatial resolution of 0.3-0.35 lp mm-1 assuming realistic geometric parameters (i.e. 30-40 cm distance between detector and phantom, 15-20 cm phantom thickness). For set-ups combining pencil beam scanning with either a position sensitive detector, e.g. an x-ray flat panel, or with a position insensitive detector, e.g. a range telescope, we find an average spatial resolution of about 0.1 lp mm-1 for an 8 mm FWHM beam spot size. The pixel information improves the spatial resolution by less than 10%. In both set-up types, performance can be significantly improved by reducing the pencil beam size down to 2 mm FWHM. In this case, the achievable spatial resolution reaches about 0.25 lp mm-1. Our results show that imaging set-ups combining double scattering with a pixel detector can provide sufficient spatial resolution only under very stringent conditions and are not ideally suited for computed tomography applications. We further propose a region-of-interest method for set-ups with a pixel detector to filter out protons which have undergone nuclear reactions and discuss the impact of tracker detector uncertainties on the most likely path.

An advanced image processing method to improve the spatial resolution of ion radiographies.

We present an optimization method to improve the spatial resolution and the water equivalent thickness (WET) accuracy of ion radiographies. The method is designed for imaging systems measuring for each actively scanned beam spot the lateral position of the pencil beam and at the same time the Bragg curve (behind the target) in discrete steps without relying on tracker detectors to determine the ion trajectory before and after the irradiated volume. Specifically, the method was used for an imaging set-up consisting of a stack of 61 parallel-plate ionization chambers (PPIC) interleaved with absorber plates of polymethyl methacrylate (PMMA) working as a range telescope. The method uses not only the Bragg peak position, but approximates the entire measured Bragg curve as a superposition of differently shifted Bragg curves. Their relative weights allow to reconstruct the distribution of thickness around each scan spot of a heterogeneous phantom. The approach also allows merging the ion radiography with the geometric information of a co-registered x-ray radiography in order to increase its spatial resolution. The method was tested using Monte Carlo simulated and experimental proton radiographies of a PMMA step phantom and an anthropomorphic head phantom. For the step phantom, the effective spatial resolution was found to be 6 and 4 times higher than the nominal resolution for the simulated and experimental radiographies, respectively. For the head phantom, a gamma index was calculated to quantify the conformity of the simulated proton radiographies with a digitally reconstructed radiography (DRR) obtained from an x-ray CT and properly converted into WET. For a distance-to-agreement (DTA) of 2.5 mm and a relative WET difference (RWET) of 2.5%, the passing ratio was 100%/85% for the optimized/non-optimized case, respectively. When the optimized proton radiography was merged with the co-registered DRR, the passing ratio was 100% at DTA = 1.3 mm and RWET = 1.3%. A special interpolation method allows to strongly reduce the dose by using a coarser grid of the measured beam spot position with a 5 times larger grid distance. We show that despite a dose reduction of 25 times (leading to a dose of 0.016 mGy for the current imaging set-up), the image quality of the optimized radiographies remains fairly unaffected for both the simulated and experimental case.

Omega-3 polyunsaturated fatty acids preserve retinal function in type 2 diabetic mice.

OBJECTIVE: Diabetic retinopathy (DR) is associated with hyperglycemia-driven microvascular pathology and neuronal compromise in the retina. However, DR is also linked to dyslipidemia. As omega-3 (ω-3) polyunsaturated fatty acids (PUFAs) are protective in proliferative retinopathy, we investigated the capacity of ω-3PUFAs to preserve retinal function in a mouse model of type 2 diabetes mellitus (T2DM). DESIGN: Male leptin-receptor-deficient (db/db) mice were maintained for 22 weeks (4 weeks-26 weeks of life) on calorically and compositionally matched diets, except for 2% enrichment in either ω-3 or ω-6PUFAs. Visual function was assessed at 9, 14 and 26 weeks by electroretinography. Retinal capillary and neuronal integrity, as well as glucose challenge responses, were assessed on each diet. RESULTS: The ω-3PUFA diet significantly preserved retinal function in the mouse model of T2DM to levels similar to those observed in nondiabetic control mice on normal chow. Conversely, retinal function gradually deteriorated in db/db mice on a ω-6PUFA-rich diet. There was also an enhanced ability of ω-3PUFA-fed mice to respond to glucose challenge. The protection of visual function appeared to be independent of cytoprotective or anti-inflammatory effects of ω-3PUFAs. CONCLUSION: This study identifies beneficial effects of dietary ω-3PUFAs on visual function in T2DM. The data are consistent with dyslipidemia negatively impacting retinal function. As ω-3PUFA lipid dietary interventions are readily available, safe and inexpensive, increasing ω-3PUFA intake in diabetic patients may slow the progression of vision loss in T2DM.

Computer-aided quantification of retinal neovascularization.

Rodent models of retinal angiogenesis play a pivotal role in angiogenesis research. These models are a window to developmental angiogenesis, to pathological retinopathy, and are also in vivo tools for anti-angiogenic drug screening in cancer and ophthalmic research. The mouse model of oxygen-induced retinopathy (OIR) has emerged as one of the leading in vivo models for these purposes. Many of the animal studies that laid the foundation for the recent breakthrough of anti-angiogenic treatments into clinical practice were performed in the OIR model. However, readouts from the OIR model have been time-consuming and can vary depending on user experience. Here, we present a computer-aided quantification method that is characterized by (i) significantly improved efficiency, (ii) high correlation with the established hand-measurement protocols, and (iii) high intra- and inter-individual reproducibility of results. This method greatly facilitates quantification of retinal angiogenesis while at the same time increasing lab-to-lab reproducibility of one of the most widely used in vivo models in angiogenesis research.

Nimodipine treatment of an adolescent with ultradian cycling bipolar affective illness.

This is a single case report of an open trial of nimodipine, a dihydropyridine-type calcium antagonist, in the treatment of a 13-year-old boy with refractory, ultradian rapid cycling, bipolar disorder type I. Prior clinical trials with calcium channel blockers in adults with ultrarapid cycling affective disorder supported an empirical trial of nimodipine for treatment of ultradian rapid cycling in this adolescent. Severity of mania and depression were rated before and after nimodipine therapy. A marked decrease in rapid, repeated, and significant mood changes was clinically observed and measured by standardized scales after 9 days of nimodipine 180 mg daily. No adverse effects were noticed. Remission persisted with continued treatment at 36-month follow-up. Medication response was partially attributed to adjunctive therapy with levothyroxine. Implications of treatment benefit are discussed in the context of novel pharmacotherapies for refractory bipolar disorder. These findings are preliminary and do not provide sufficient basis to recommend nimodipine as the treatment of choice in adolescents with ultradian cycling bipolar disorder, but suggest that controlled studies may be indicated.