RESUMO
Fourier-transform spectral imaging captures frequency-resolved images with high spectral resolution, broad spectral range, high photon flux, and low stray light. In this technique, spectral information is resolved by taking Fourier transformation of the interference signals of two copies of the incident light at different time delays. The time delay should be scanned at a high sampling rate beyond the Nyquist limit to avoid aliasing, at the price of low measurement efficiency and stringent requirements on motion control for time delay scan. Here we propose, what we believe to be, a new perspective on Fourier-transform spectral imaging based on a generalized central slice theorem analogous to computerized tomography, using an angularly dispersive optics decouples measurements of the spectral envelope and the central frequency. Thus, as the central frequency is directly determined by the angular dispersion, the smooth spectral-spatial intensity envelope is reconstructed from interferograms measured at a sub-Nyquist time delay sampling rate. This perspective enables high-efficiency hyperspectral imaging and even spatiotemporal optical field characterization of femtosecond laser pulses without a loss of spectral and spatial resolutions.
RESUMO
Ultrafast electron microbunch trains have broad applications in which the individual bunch length and the bunch-to-bunch interval are critical parameters that need to be precisely diagnosed. However, directly measuring these parameters remains challenging. This paper presents an all-optical method that simultaneously measures the individual bunch length and the bunch-to-bunch spacing through an orthogonal THz-driven streak camera. For a 3 MeV electron bunch train, the simulation indicates that the temporal resolution of individual bunch length and the bunch-to-bunch spacing is 2.5 fs and 1 fs, respectively. Through this method, we expect to open a new chapter in the temporal diagnostic of electron bunch trains.
RESUMO
PURPOSE/OBJECTIVE(S): Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distribution of positron emitters in addition to the presence of noise, machine learning approaches were proposed to establish their relationship. MATERIALS/METHODS: Simulations were carried out with a spot-scanning proton system using GATE-8.0 and Geant4-10.3 toolkit with a computed tomography (CT)-based patient phantom. The one-dimensional (1D) distributions of positron emitters and radiation dose were obtained. A feedforward neural network classification model comprising two hidden layers, was developed to estimate whether the range is within a preset threshold. A recurrent neural network (RNN) regression model comprising three layers and ten neurons in each hidden layer was developed to estimate dose distribution. The performance was quantitatively studied in terms of mean squared error (MSE) and mean absolute error (MAE) under different signal-to-noise ratio (SNR) values. RESULTS: The feasibility of proton range and dose verification using the proposed neural network framework was demonstrated. The feedforward NN model achieves high classification accuracy close to 100% for individual classes without bias. The RNN model is able to accurately predict the 1D dose distribution for different energies and irradiation positions. When the SNR of the input activity profiles is above 4, the framework is able to predict with an MAE of ~0.60 mm and an MSE of ~0.066. Moreover, the model demonstrates a good capability of generalization. CONCLUSIONS: The RNN model is found to be effective in identifying the relationship between the distributions of dose and positron emitters. The machine learning-based framework and RNN models may be a useful tool to allow for accurate online range and dose verification based on proton-induced positron emitters.
