In this work, we studied how the detector angle affects the signal-to-noise ratio (S/N) of X-ray fluorescence CT (XFCT), which is a major factor in reducing background noise and improving the detection sensitivity. We simulated a benchtop multi-pinhole XFCT system including a fan-beam x-ray source, a phantom (2.5 cm in diameter) consisting of one insert with gold nanoparticles (GNPs) , 2-sided multi-pinhole (3 pinholes) collimator aimed to acquire multiple projections simultaneously and 2-sided two-dimensional (2D) detector based on Geant4. The signal and noise were defined as the emitted Kα1=68.8keV X-ray fluorescence and Compton scatter fluorescence, respectively. The result was evaluated in three approaches at four different detector angles 60° (forward-scatter), 90° (side-scatter), 120° and 150° (back-scatter). In the simulation, the tube voltage was set to 90,100,110 and 120keV with fixed GNPs concentration (2%) and insert diameter (6mm).Then, the GNPs concentration was increased from 0.25%, 0.5%, 1% to 2% with fixed tube voltage (120keV) and insert diameter (6mm). Next, the insert diameter was set to 4, 6, 8, and 10mm with fixed tube voltage (120keV) and GNPs concentration (1%). The optimized detection angle was acquired by comparing the correlation between S/N and detector angles in terms of tube voltage, GNPs concentration and insert diameter. The experimental results demonstrated that, for most circumstances, the highest S/N could be obtained when detector angle was set to 120°.
X-Ray computed tomography (CT) is one of the most popular imaging modality in the medical image analysis for clinical application. Meanwhile, the potential risk of X-Ray radiation dose to patients has attracted the public attention. Over the past decades, extensive efforts have been made for developing low-dose CT. However, X-Ray radiation dose reduction may result in increased noise and artifacts, which can significantly compromise the image quality and deteriorate the diagnostic performance. Hence, restoring CT image from low-dose CT and improving the diagnostic performance is a challenging for the vast researchers and developers. In this paper, a method based on deep learning techniques is proposed for low-dose CT noise reduction. Our method integrates convolutional neural network (CNN) blocks, residual learning, exponential linear units (ELUs) into a deep learning framework. Especially, loss of structural similarity index (SSIM) is combines to the final objective function to improve the image quality. Differs from general deep learning based denoising method, our deep CNN blocks architecture learning noise directly from original low-dose CT images, then restores denoised CT image by subtracting the obtained noise image from the original low-dose CT. After training patch by patch, the proposed method attains promising performance compared to state of the art traditional methods (non-local means and Block-matching 3D) and representative deep learning methods (primary three layers convolutional neural networks and residual encoder-decoder convolutional neural network) in visual effects and quantitative measurements. Extensive experiments was implemented for how choosing the coefficients of overall loss function and the number of CNN blocks. The experimental results demonstrate that our noise reduction method is effective for low-dose CT and potential clinic application.
Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which is able to distinguish different material compositions. Nowadays, deep learning has generated widespread attention in CT imaging applications. In this paper, a method of material decomposition for spectral CT based on improved Fully Convolutional DenseNets (FC-DenseNets) was proposed. Spectral data were acquired by a photon-counting detector and reconstructed spectral CT images were used to construct a training dataset. Experimental results showed that the proposed method could effectively identify bone and different tissues in high noise levels. This work could establish guidelines for multi-material decomposition approaches with spectral CT.
The patient motion can damage the quality of computed tomography images, which are typically acquired in cone-beam geometry. The rigid patient motion is characterized by six geometric parameters and are more challenging to correct than in fan-beam geometry. We extend our previous rigid patient motion correction method based on the principle of locally linear embedding (LLE) from fan-beam to cone-beam geometry and accelerate the computational procedure with the graphics processing unit (GPU)-based all scale tomographic reconstruction Antwerp toolbox. The major merit of our method is that we need neither fiducial markers nor motion-tracking devices. The numerical and experimental studies show that the LLE-based patient motion correction is capable of calibrating the six parameters of the patient motion simultaneously, reducing patient motion artifacts significantly.
Bowtie filters are used to modulate an incoming x-ray beam as a function of the angle of the x-ray to balance the photon flux on a detector array. Because of their key roles in radiation dose reduction and multi-energy imaging, bowtie filters have attracted a major attention in modern X-ray computed tomography (CT). However, few researches are concerned on the effects of the structure and materials for the bowtie filter in the Cone Beam CT (CBCT). In this study, the influence of bowtie filters’ structure and materials on X-ray photons distribution are analyzed using Monte Carlo (MC) simulations by MCNP5 code. In the current model, the phantom was radiated by virtual X-ray source (its’ energy spectrum calculated by SpekCalc program) filtered using bowtie, then all photons were collected through array photoncounting detectors. In the process above, two bowtie filters’ parameters which include center thickness (B), edge thickness (controlled by A), changed respectively. Two kinds of situation are simulated: 1) A=0.036, B=1, 2, 3, 4, 5, 6mm and the material is aluminum; 2) A=0.016, 0.036, 0.056, 0.076, 0.096, B=2mm and the material is aluminum. All the X-ray photons' distribution are measured through MCNP. The results show that reduction in center thickness and edge thickness can reduce the number of background photons in CBCT. Our preliminary research shows that structure parameters of bowtie filter can influence X-ray photons, furthermore, radiation dose distribution, which provide some evidences in design of bowtie filter for reducing radiation dose in CBCT.