A feasibility study of time of flight cone beam computed tomography imaging

BACKGROUND: The time of flight (TOF) cone beam computed tomography (CBCT) was recently shown to reduce the X-ray scattering effects by 95%and improve the image CNR by 110%for large volume objects. The advancements in X-ray sources like in compact Free Electron Lasers (FEL) and advancements in detector technology show potential for the TOF method to be feasible in CBCT when imaging large objects. OBJECTIVE: To investigate feasibility and efficacy of TOF CBCT in imaging smaller objects with different targets such as bones and tumors embedded inside the background. METHODS: The TOF method used in this work was verified using a 24cm phantom. Then, the GATE software was used to simulate the CBCT imaging of an 8 cm diameter cylindrical water phantom with two bone targets using a modeled 20 keV quasi-energetic FEL source and various TOF resolutions ranging from 1 to 1000 ps. An inhomogeneous breast phantom of similar size with tumor targets was also imaged using the same system setup. RESULTS: The same results were obtained in the 24cm phantom, which validated the applied CBCT simulation approach. For the case of 8cm cylindrical phantom and bone target, a TOF resolution of 10 ps improved the image contrast-to-noise ratio (CNR) by 57%and reduced the scatter-to-primary ratio (SPR) by 8.63. For the case of breast phantom and tumor target, image CNR was enhanced by 12%and SPR was reduced by 1.35 at 5 ps temporal resolution. CONCLUSIONS: This study indicates that a TOF resolution below 10 ps is required to observe notable enhancements in the image quality and scatter reduction for small objects around 8cm in diameter. The strong scattering targets such as bone can result in substantial improvements by using TOF CBCT.

A Deep Learning-Based Scatter Correction of Simulated X-ray Images

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.

A Simple Scatter Reduction Method in Cone-Beam Computed Tomography for Dental and Maxillofacial Applications Based on Monte Carlo Simulation

The quality of images obtained from cone-beam computed tomography (CBCT) is important in diagnosis and treatment planning for dental and maxillofacial applications. However, X-ray scattering inside a human head is one of the main factors that cause a drop in image quality, especially in the CBCT system with a wide-angle cone-beam X-ray source and a large area detector. In this study, the X-ray scattering distribution within a standard head phantom was estimated using the Monte Carlo method based on Geant4. Due to small variation of low-frequency scattering signals, the scattering signals from the head phantom can be represented as the simple predetermined scattering signals from a patient’s head and subtracted the projection data for scatter reduction. The results showed higher contrast and less cupping artifacts on the reconstructed images of the head phantom and real patients. Furthermore, the same simulated scattering signals can also be applied to process with higher-resolution projection data.