Spectral CT Reconstruction With Image Sparsity and Spectral Mean

Abstract

Photon-counting detectors can acquire x-ray intensity data in different energy bins. The signal to noise ratio of resultant raw data in each energy bin is generally low due to the narrow bin width and quantum noise. To address this problem, here we propose an image reconstruction approach for spectral CT to simultaneously reconstructs x-ray attenuation coefficients in all the energy bins. Because the measured spectral data are highly correlated among the x-ray energy bins, the intra-image sparsity and inter-image similarity are important prior acknowledge for image reconstruction. Inspired by this observation, the total variation (TV) and spectral mean (SM) measures are combined to improve the quality of reconstructed images. For this purpose, a linear mapping function is used to minimalize image differences between energy bins. The split Bregman technique is applied to perform image reconstruction. Our numerical and experimental results show that the proposed algorithms outperform competing iterative algorithms in this context.

Stacked competitive networks for noise reduction in low-dose CT

“…Although BM3D suppressed most noise, the artifacts near the vertebral column are evident. SSCN, KAIST-Net and SCN eliminated most of the artifacts and noise, but the results in Fig 7(f) and 7(g) suffered from mild blurring and this drawback was also mentioned in our previous work [ 14 ]. Several regions with detectable structure differences are marked by the red arrows.…”

“…Iterative reconstruction (IR) [ 3 ] models the reconstruction problem as an objective function with prior constraints. Different priors have been proposed for dealing with the CT issues of low dose, limited angle and few views, such as nonlocal means (NLM), total variation (TV) and its variants, and sparse representation [ 4 ][ 5 ][ 6 ][ 7 ][ 8 ][ 9 ][ 10 ][ 11 ][ 12 ][ 13 ][ 14 ]. Despite promising results obtained by this type of method, their wide application is still circumscribed on account of the difficulty of accessing well-formatted raw data from commercial CT scanners and heavy computational burden.…”

Exposure Invariance in Spectral Reconstruction from RGB Images

“…Hyperspectral imaging devices are developed to capture high resolution radiance spectra at every pixel in an image, namely the hyperspectral images. These images often record additional scene information that are 'invisible' to human eyes and consumer RGB cameras (where the spectral information is recorded with only 3 intensity values per pixel), which has been found useful in numerous computer vision applications including remote sensing [40], [11], [19], [38], [10], anomaly detection [23] and medical imaging [44], [45], as well as computer graphics applications such as scene relighting [25] and digital art archiving [43].…”

Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

“…Considering the similarity between the image gradient of different energy bins, the image gradient L0-norm was incorporated into the TDL (L0TDL) framework for sparse-view spectral CT reconstruction [26]. The spectral prior image constrained compressed sensing algorithm (spectral PICCS) [27], TV-TV and total variation spectral mean (TV-SM) methods [28] can also be considered as prior-image-knowledge based methods, where a high quality image is treated as prior to constrain the final solution [29]. Very recently, an average-image-incorporated BM3D technology was developed to enhance the correlations among energy bin images [30].…”