Design of a novel gamma camera with large field of view for 16N diagnosis in the primary loop of nuclear reactor

The assessment of the concentration and distribution of l6N, derived from 16O in the cooling water exposed to neutron irradiation, is essential for ensuring radiation safety during nuclear reactor operation. The imaging method allows for the visualization of the intensity distribution of these l6N by capturing gamma-rays emitted during their decay process. However, the existing gamma camera is exclusively compatible with gamma-rays below 2 MeV. In this paper, a novel gamma camera featuring a thick double-conical penumbra aperture, a pixelated Lu1.8Y0.2SiO5:Ce scintillator array, and a position-sensitive photomultiplier tube is proposed to address this limitation. This innovative design offers a large field of view (FOV) and is suitable for high energy extended gamma source imaging. The optimization of key parameters of the camera was conducted, and a FOV of 60° and an angular resolution of up to 4.57° were achieved. Imaging simulations, including a simplified model of the primary loop of the pressurized-water reactor by GEANT4 code and image reconstruction using the expectation maximum algorithm, demonstrated that the proposed gamma camera could obtain a satisfactory spatial resolution for diagnosing the distribution of 16N in the primary loop of a nuclear reactor.

Reconstructing COVID-19 incidences from positive RT-PCR tests by deconvolution

BACKGROUND

The emergency of new COVID-19 variants over the past three years posed a serious challenge to the public health. Cities in China implemented mass daily RT-PCR tests by pooling strategies. However, a random delay exists between an infection and its first positive RT-PCR test. It is valuable for disease control to know the delay pattern and daily infection incidences reconstructed from RT-PCR test observations.

METHODS

We formulated the convolution model between daily incidences and positive RT-PCR test counts as a linear inverse problem with positivity restrictions. Consequently, the Richard-Lucy deconvolution algorithm was used to reconstruct COVID-19 incidences from daily PCR tests. A real-time deconvolution was further developed based on the same mathematical principle. The method was applied to an Omicron epidemic data set of a bar outbreak in Beijing and another in Wuxi in June 2022. We estimated the delay function by maximizing likelihood via an E-M algorithm.

RESULTS

The delay function of the bar-outbreak in 2022 differs from that reported in 2020. Its mode was shortened to 4 days by one day. A 95% confidence interval of the mean delay is [4.43,5.55] as evaluated by bootstrap. In addition, the deconvolved infection incidences successfully detected two associated infection events after the bar was closed. The application of the real-time deconvolution to the Wuxi data identified all explosive incidence increases. The results revealed the progression of the two COVID-19 outbreaks and provided new insights for prevention and control strategies, especially for the role of mass daily RT-PCR testing.

CONCLUSIONS

The proposed deconvolution method is generally applicable to other infectious diseases if the delay model can be assumed to be approximately valid. To ensure a fair reconstruction of daily infection incidences, the delay function should be estimated in a similar context in terms of virus variant and test protocol. Both the delay estimate from the E-M algorithm and the incidences resulted from deconvolution are valuable for epidemic prevention and control. The real-time feedback is particularly useful during the epidemic's acute phase because it can help the local disease control authorities modify the control measures more promptly and precisely.

Accurate Detection of Arbitrary Photon Statistics

We report a measurement workflow free of systematic errors consisting of a reconfigurable photon-number-resolving detector, custom electronic circuitry, and faithful data-processing algorithm. We achieve an unprecedented accurate measurement of various photon-number distributions going beyond the number of detection channels with an average fidelity of 0.998, where the error is primarily caused by the sources themselves. Mean numbers of photons cover values up to 20 and faithful autocorrelation measurements range from g^{(2)}=6×10^{-3} to 2. We successfully detect chaotic, classical, nonclassical, non-Gaussian, and negative-Wigner-function light. Our results open new paths for optical technologies by providing full access to the photon-number information without the necessity of detector tomography.

Gamma-ray imaging with a time-modulated random coded aperture

In this article, we present a new industrial gamma-ray imaging system. This system takes advantage of a time-modulated random coded aperture (TMRCA). The gamma-ray detector coupled to the TMRCA can be position-sensitive or non-position-sensitive. The TMRCA imaging system could offer the ability to identify radioactive sources without losing spatial resolution. With a non-position-sensitive BGO detector, a prototype TMRCA imaging system was constructed. The prototype system was investigated with two gamma-ray sources (¹³⁷Cs, ⁶⁰Co) and a ²³⁸Pu-Be neutron source, which was placed in a paraffin moderator to produce an extended source. The experimental results suggest that the TMRCA imaging system offers the opportunity to achieve high spatial-energy resolution cost-effectively for high-energy gamma rays.

Distribution of squeezed states through an atmospheric channel

Continuous variable quantum states of light are used in quantum information protocols and quantum metrology and known to degrade with loss and added noise. We were able to show the distribution of bright polarization squeezed quantum states of light through an urban free-space channel of 1.6 km length. To measure the squeezed states in this extreme environment, we utilize polarization encoding and a postselection protocol that is taking into account classical side information stemming from the distribution of transmission values. The successful distribution of continuous variable squeezed states is accentuated by a quantum state tomography, allowing for determining the purity of the state.

A method for partial volume correction of PET-imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function

Tumor heterogeneities observed in positron emission tomography (PET) imaging are frequently compromised by partial volume effects which may affect treatment prognosis, assessment or future implementations such as biologically optimized treatment planning (dose painting). This paper presents a method for partial volume correction of PET-imaged heterogeneous tumors. A point source was scanned on a GE Discovery LS at positions of increasing radii from the scanner's center to obtain the spatially varying point spread function (PSF). PSF images were fit in three dimensions to Gaussian distributions using least squares optimization. Continuous expressions were devised for each Gaussian width as a function of radial distance, allowing for generation of the system PSF at any position in space. A spatially varying partial volume correction (SV-PVC) technique was developed using expectation maximization (EM) and a stopping criterion based on the method's correction matrix generated for each iteration. The SV-PVC was validated using a standard tumor phantom and a tumor heterogeneity phantom and was applied to a heterogeneous patient tumor. SV-PVC results were compared to results obtained from spatially invariant partial volume correction (SINV-PVC), which used directionally uniform three-dimensional kernels. SV-PVC of the standard tumor phantom increased the maximum observed sphere activity by 55 and 40% for 10 and 13 mm diameter spheres, respectively. Tumor heterogeneity phantom results demonstrated that as net changes in the EM correction matrix decreased below 35%, further iterations improved overall quantitative accuracy by less than 1%. SV-PVC of clinically observed tumors frequently exhibited changes of +/-30% in regions of heterogeneity. The SV-PVC method implemented spatially varying kernel widths and automatically determined the number of iterations for optimal restoration, parameters which are arbitrarily chosen in SINV-PVC. Comparing SV-PVC to SINV-PVC demonstrated that similar results could be reached using both methods, but large differences result for the arbitrary selection of SINV-PVC parameters. The presented SV-PVC method was performed without user intervention, requiring only a tumor mask as input. Research involving PET-imaged tumor heterogeneity should include correcting for partial volume effects to improve the quantitative accuracy of results.

Biased tomography schemes: an objective approach

We report on an intrinsic relationship between the maximum-likelihood quantum-state estimation and the representation of the signal. A quantum analogy of the transfer function determines the space where the reconstruction should be done without the need for any ad hoc truncations of the Hilbert space. An illustration of this method is provided by a simple yet practically important tomography of an optical signal registered by realistic binary detectors.

Experimental reconstruction of photon statistics without photon counting

Experimental reconstructions of photon number distributions of both continuous-wave and pulsed light beams are reported. Our scheme is based on on/off avalanche photo-detection assisted by maximum-likelihood estimation and does not involve photon counting. Reconstructions of the distribution for both semiclassical and quantum states of light are reported for single-mode as well as for multi-mode beams.