Photon-counting computed tomography thermometry via material decomposition and machine learning

Thermal ablation procedures,such as high intensity focused ultrasound and radiofrequency ablation,are often used to eliminate tumors by minimally invasively heating a focal region.For this task,real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings.Current computed tomography(CT)thermometry is based on energy-integrated CT,tissue-specific experimental data,and linear relationships between attenuation and temperature.In this paper,we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest.In our feasibility study,distilled water,50 mmol/L CaCl2,and 600 mmol/L CaCl2 are chosen as the base materials.Their attenuations are measured in four discrete energy bins at various temperatures.The neural network trained on the experimental data achieves a mean absolute error of 3.97°C and 1.80°C on 300 mmol/L CaCl2 and a milk-based protein shake respectively.These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dis-similar to our base materials.

Near-Range Large Field-of-View Three-Dimensional Photon-Counting Imaging with a Single-Pixel Si-Avalanche Photodiode

Large field-of-view（FoV） three-dimensional（3 D） photon-counting imaging is demonstrated with a single-pixel single-photon detector based on a Geiger-mode Si-avalanche photodiode. By removing the collecting lens（CL）before the detector, the FoV is expanded to ±10°. Thanks to the high detection efficiency, the signal-to-noise ratio of the imaging system is as high as 7.8 dB even without the CL when the average output laser pulse energy is about 0.45 pJ/pulse for imaging the targets at a distance of 5 m. A 3 D image overlaid with the reflectivity data is obtained according to the photon-counting time-of-flight measurement and the return photon intensity.

Application of Photon-Counting X-ray Computed Tomography to Aluminum-Casting Inspection

One of the issues in the aluminum-alloy die casting industry is the space occurring inside the casting, and the improvement of the verification technology is expected. The purpose of this research is to seal holes inside the aluminum metal by resin and verify them by photon-counting X-ray computed tomography (CT) using an energy-discrimination 64-channel cadmium-telluride (CdTe) line detector. Moreover, it is important to estimate the image of the effective atomic number and the electronic density by the energy mapping of the attenuation coefficient utilizing photon-counting X-ray CTto distinguish both the aluminum metal and the resin filler in the aluminum hole. As a result, the energy discrimination of the resin filler in the space of aluminum casting has been attained. We could observe the atomic number image utilizing dual-energyX-ray CTmethod with the 64-channel CdTe photon-counting detector.

A Photon-Counting Full-Waveform Lidar

We present the results of using a photon-eounting full-waveform lidar to obtain detailed target information with high accuracy.The parameters of the waveforms(i.e.,vertical structure,peak position,peak amplitude,peak width and backscatter cross section)are derived with a high resolution limit of 31 mm to establish the vertical structure and scattering properties of targets,which contribute to the recognition and classification of various scatterers.The photon-counting full-waveform lidar has higher resolution than linear-mode full-waveform lidar,and it can obtain more specific target information compared to photon-counting discrete-point lidar,which can provide a potential alternative technique for tomographic surveying and mapping.

A new denoising method for photon-counting LiDAR data with different surface types and observation conditions

Spaceborne photon-counting LiDAR is significantly affected by noise,and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions.Accordingly,a new denoising method is presented to extract signal photons adaptively.The method includes two steps.First,the local neighborhood radius is calculated according to photons’density,then thefirst-step denoising process is completed via photons’curvature feature based on KNN search and covariance matrix.Second,the local photonfiltering direction and threshold are obtained based on thefirst-step denoising results by RANSAC and elevation frequency histogram,and the local dense noise photons that thefirst-step cannot be identified are further eliminated.The following results are drawn:(1)experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94,and the accuracy is effectively improves with the number of denoising times;(2)experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies.Overall,the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons,and the denoising results can provide more reasonable and reliable data for sustainable urban development.

Material decomposition of spectral CT images via attention-based global convolutional generative adversarial network

Spectral computed tomography(CT)based on photon counting detectors can resolve the energy of every single photon interacting with the sensor layer and be used to analyze material attenuation information under different energy ranges,which can be helpful for material decomposition studies.However,there is a considerable amount of inherent quantum noise in narrow energy bins,resulting in a low signal-to-noise ratio,which can consequently affect the material decomposition performance in the image domain.Deep learning technology is currently widely used in medical image segmentation,denoising,and recognition.In order to improve the results of material decomposition,we propose an attention-based global convolutional generative adversarial network(AGC-GAN)to decompose different materials for spectral CT.Specifically,our network is a global convolutional neural network based on an attention mechanism that is combined with a generative adversarial network.The global convolutional network based on the attention mechanism is used as the generator,and a patchGAN discriminant network is used as the discriminator.Meanwhile,a clinical spectral CT image dataset is used to verify the feasibility of our proposed approach.Extensive experimental results demonstrate that AGC-GAN achieves a better material decomposition performance than vanilla U-Net,fully convolutional network,and fully convolutional denseNet.Remarkably,the mean intersection over union,structural similarity,mean precision,PAcc,and mean F1-score of our method reach up to 87.31%,94.83%,93.22%,97.39%,and 93.05%,respectively.

Time evolution of distribution functions in dissipative environments

By introducing the thermal entangled state representation, we investigate the time evolution of distribution functions in the dissipative channels by bridging the relation between the initial distribution function and the any time distribution function. We find that most of them are expressed as such integrations over the Laguerre Gaussian function. Furthermore, as applications, we derive the time evolution of photon-counting distribution by bridging the relation between the initial distribution function and the any time photon-counting distribution, and the time evolution of Rfunction characteristic of nonclassicality depth.