While energy-integration spectral CT with the capability of material decomposition has been providing added value to diagnostic CT imaging in the clinic, photon-counting spectral CT is gaining momentum in research and development, with the potential of overcoming more clinically relevant challenges. In practice, the photon-counting spectral CT provides the opportunity for principal component analysis to effectively extract information from the raw data. However, the principal component analysis in spectral CT may suffer from high noise induced by photon starvation, especially in energy bins at the high energy end. Via phantom and small animal studies, we investigate the feasibility of principal component analysis in photon-counting spectral CT and the benefit that can be offered by de-noising with the Content-Oriented Sparse Representation method.
Denoising has been a challenging research subject in medical imaging in general and in CT imaging in particular, because the suppression of noise conflicts with the preservation of texture and edges. The purpose of this paper is to develop and evaluate a content-oriented sparse representation (COSR) denoising method in CT to effectively address this challenge. A CT image is firstly segmented by thresholding into several content-areas with similar materials, such as the air, soft tissues and bones. After being ex-painted smoothly outside it boundary, each content-area is sparsely coded by an atom from the dictionary that learnt from the image patches extracted from the corresponding content-area. The regenerated content-areas are finally aggregated to form the denoised CT image. The efficiency of image denoising and the ability of preserving texture and edges are demonstrated with a cylinder water phantom generated by simulation. The denoising performance of the proposed method is further tested with images of a pediatric head phantom and an anonymous pediatric patient that scanned by a state-of-the-art CT scanner, which shows that the proposed COSR denoising method can effectively preserve texture and edges while reducing noise. It is believed that this method would find its utility in extensive clinical and pre-clinical applications, such as dedicated and low dose CT, image segmentation and registration, and computer aided diagnosis (CAD) etc.
The optimization-based image reconstruction (OBIR) has been proposed and investigated in recent years to reduce radiation dose in X-ray computed tomography (CT) through acquiring sparse projection views. However, the OBIR usually generates images with a quite different noise texture compared to the clinical widely used reconstruction method (i.e. filtered back-projection – FBP). This may make the radiologists/physicians less confident while they are making clinical decisions. Recognizing the fact that the X-ray photon noise statistics is relatively uniform across the detector cells, which is enabled by beam forming devices (e.g. bowtie filters), we propose and evaluate a novel and practical texture enhancement method in this work. In the texture enhanced optimization-based image reconstruction (TxEOBIR), we first reconstruct a texture image with the FBP algorithm from a full set of synthesized projection views of noise. Then, the TxE-OBIR image is generated by adding the texture image into the OBIR reconstruction. As qualitatively confirmed by visual inspection and quantitatively by noise power spectrum (NPS) evaluation, the proposed method can produce images with textures that are visually identical to those of the gold standard FBP images.