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.