Exploration of differentiability in a proton computed tomography simulation framework.

Objective.Gradient-based optimization using algorithmic derivatives can be a useful technique to improve engineering designs with respect to a computer-implemented objective function. Likewise, uncertainty quantification through computer simulations can be carried out by means of derivatives of the computer simulation. However, the effectiveness of these techniques depends on how 'well-linearizable' the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications.Approach.This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques.Main results.The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. However, the observed high density and magnitude of jumps was likely to preclude most meaningful uses of the derivatives. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path, and could be reduced in magnitude by a 'fuzzy voxels' approach. The investigated jumps in the MC function arose from local changes in the control flow that affected the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem.Significance.Besides the technical challenges of merely applying AD to existing software projects, the MC and MBIR codes must be adapted to compute smoother functions. For the MBIR code, we presented one possible approach for this while for the MC code, this will be subject to further research. For the tracking subprocedure, further research on surrogate models is necessary.

Uncertainty-aware spot rejection rate as quality metric for proton therapy using a digital tracking calorimeter.

Objective.Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots.Approach.We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction.Main results.The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1 × 107protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm.Significance.Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area.

Agro-morphological and genetic diversity studies in Rice (Oryza sativa L.) germplasm using microsatellite markers.

BACKGROUND: Knowledge of the genetic diversity and population structure of germplasm collections is an important foundation for crop improvement. Rice production across a broad range of rice-growing environments results in a diverse array of local rice varieties. Many rice varieties have been lost as a result of biodiversity loss and are now grown in Pakistan. METHODS AND RESULTS: To protect the biodiversity of rice varieties, an experiment was carried out to check the genetic and morphological variations between 8 exotic and 7 local rice genotypes, using 5 different SSR markers, i.e., RM3, RM259, RM341, RM520, and RM11943. The analysis of morphological and quality traits of rice observed significant variation across genotypes. The results revealed that genotype Irri-Pak attained the highest plant height and primary branch plant-1, while genotype Mushkan produced a higher number of productive tillers and obtained a higher fertility factor (%). Similarly, the highest value for panicle length was observed for genotype Faker-e-Malakand, 1000-grains weight in genotype Calmochi, and maximum days to maturity was noticed in genotype Swati-2014. Moreover, the genotype Brio attained the highest value of stem diameter, while maximum seed length was noted in the genotype Sug Dasi. The highest number of primary branches plant-1 in genotype Ibge-I and secondary branches plant-1 in genotype Calmochi were noticed. A higher concentration of sodium and potassium was observed for the genotype Marte, while the genotype Muskan attained the maximum content of copper. Moreover, the highest concentration of iron in genotype Originario, zinc in genotype JP-5, and cadmium content were noticed in genotype Ibge. Similarly, the dendrogram analysis for quantitative parameters showed three clusters at 74.13% similarities. Whereas all the genotypes of European origin formed a separate cluster. A set of 5 simple sequence repeat primers, covering four chromosomes, amplified a total of 14 alleles and showed 100% polymorphism with an average PIC value ranging from 0.39 to 0.91. The UPGMA cluster analysis separated the 15 rice genotypes into 3 main groups based on 32.5% similarities and the highest genetic distance (45.1%) was observed between two genotypes (Fakher-e-malakand and Musa), having different geographical origins. There was no genetic distance between the genotypes Marte and Brio, irrespective of having the same origin. CONCLUSIONS: The maximum genetic distances were noted for genotype, Fakhre-e-Malakand and Musa having a different origin, while the minimum genetic distance was shown by genotypes, Marte and Onice, from the same origin.

Helium radiography with a digital tracking calorimeter-a Monte Carlo study for secondary track rejection.

Radiation therapy using protons and heavier ions is a fast-growing therapeutic option for cancer patients. A clinical system for particle imaging in particle therapy would enable online patient position verification, estimation of the dose deposition through range monitoring and a reduction of uncertainties in the calculation of the relative stopping power of the patient. Several prototype imaging modalities offer radiography and computed tomography using protons and heavy ions. A Digital Tracking Calorimeter (DTC), currently under development, has been proposed as one such detector. In the DTC 43 longitudinal layers of laterally stacked ALPIDE CMOS monolithic active pixel sensor chips are able to reconstruct a large number of simultaneously recorded proton tracks. In this study, we explored the capability of the DTC for helium imaging which offers favorable spatial resolution over proton imaging. Helium ions exhibit a larger cross section for inelastic nuclear interactions, increasing the number of produced secondaries in the imaged object and in the detector itself. To that end, a filtering process able to remove a large fraction of the secondaries was identified, and the track reconstruction process was adapted for helium ions. By filtering on the energy loss along the tracks, on the incoming angle and on the particle ranges, 97.5% of the secondaries were removed. After passing through 16 cm water, 50.0% of the primary helium ions survived; after the proposed filtering 42.4% of the primaries remained; finally after subsequent image reconstruction 31% of the primaries remained. Helium track reconstruction leads to more track matching errors compared to protons due to the increased available focus strength of the helium beam. In a head phantom radiograph, the Water Equivalent Path Length error envelope was 1.0 mm for helium and 1.1 mm for protons. This accuracy is expected to be sufficient for helium imaging for pre-treatment verification purposes.

Design optimization of a pixel-based range telescope for proton computed tomography.

PURPOSE: A pixel-based range telescope for tracking particles during proton imaging is described. The detector applies a 3D matrix of stacked Monolithic Active Pixel Sensors with fast readout speeds. This study evaluates different design alternatives of the range telescope on basis of the protons' range accuracy and the track reconstruction efficiency. METHOD: Detector designs with different thicknesses of the energy-absorbing plates between each sensor layer are simulated using the GATE/Geant4 Monte Carlo software. Proton tracks traversing the detector layers are individually reconstructed, and a Bragg curve fitting procedure is applied for the calculation of each proton's range. RESULTS: Simulations show that the setups with 4 mm and thinner absorber layers of aluminum have a low range uncertainty compared to the physical range straggling, systematic errors below 0.3 mm water equivalent thickness and a track reconstruction capability exceeding ten million protons per second. CONCLUSIONS: In order to restrict the total number of layers and to yield the required tracking and range resolution properties, a design recommendation is reached where the proposed range telescope applies 3.5 mm thick aluminum absorber slabs between each sensor layer.