Dual energy CT with one full scan and a second sparse-view scan using structure preserving iterative reconstruction (SPIR)

Abstract

Conventional dual-energy CT (DECT) reconstruction requires two full-size projection datasets with two different energy spectra. In this study, we propose an iterative algorithm to enable a new data acquisition scheme which requires one full scan and a second sparse-view scan for potential reduction in imaging dose and engineering cost of DECT. A bilateral filter is calculated as a similarity matrix from the first full-scan CT image to quantify the similarity between any two pixels, which is assumed unchanged on a second CT image since DECT scans are performed on the same object. The second CT image from reduced projections is reconstructed by an iterative algorithm which updates the image by minimizing the total variation of the difference between the image and its filtered image by the similarity matrix under data fidelity constraint. As the redundant structural information of the two CT images is contained in the similarity matrix for CT reconstruction, we refer to the algorithm as structure preserving iterative reconstruction (SPIR). The proposed method is evaluated on both digital and physical phantoms, and is compared with the filtered-backprojection (FBP) method, the conventional total-variation-regularization-based algorithm (TVR) and Prior-Image-Constrained-Compressed-Sensing (PICCS). SPIR with a second 10-view scan reduces the image noise STD by a factor of one order of magnitude with same spatial resolution as full-view FBP image. SPIR substantially improves over TVR on the reconstruction accuracy of a 10-view scan by decreasing the reconstruction error from 6.18% to 1.33%, and outperforms TVR at 50 and 20-view scans on spatial resolution with a higher frequency at the modulation transfer function value of 10% by an average factor of 4. Compared with the 20-view scan PICCS result, the SPIR image has 7 times lower noise STD with similar spatial resolution. The electron density map obtained from the SPIR-based DECT images with a second 10-view scan has an average error of less than 1%.

A review on medical imaging synthesis using deep learning and its clinical applications

“…x-ray exposure (mAs) [104][105][106][107] or the number of x-ray projections. [104][105][106][107] However, if reconstructing an image with a conventional filteredbackprojection (FBP) algorithm, image quality would be degraded with greater image noise and reduced signal-to-noise ratio for a lowexposure protocol, or with severe undersampling artifacts for a reduced projection protocol. These low-quality images would make routine tasks requiring CT images difficult for clinicians.…”

“…[113][114][115][116] This is usually addressed by a monoenergetic forward projection matrix, or by obtaining an indirect simulation/estimation of the energy spectrum. 106,107,112,117,118 T A B L E 6 Summary of studies on PET-based synthetic CT for PET attenuation correction.…”

“…Computed tomography dose can be lowered by either reducing x‐ray exposure (mAs) 104–107 or the number of x‐ray projections 104–107 . However, if reconstructing an image with a conventional filtered‐backprojection (FBP) algorithm, image quality would be degraded with greater image noise and reduced signal‐to‐noise ratio for a low‐exposure protocol, or with severe undersampling artifacts for a reduced projection protocol.…”

Optimal virtual monoenergetic image in “TwinBeam” dual‐energy CT for organs‐at‐risk delineation based on contrast‐noise‐ratio in head‐and‐neck radiotherapy

“…In these applications, DECT provides material specific information which is extracted by processing the projection datasets of two different energy spectra acquired by DECT. For example, bone and iodine contrast can be distinguished and mapped separately in DECT by utilizing their different energy dependences of attenuation, even if their Hounsfield Units (HUs) highly overlap each other …”

Low-dose material-specific radiography using monoenergetic photons

“…In this work, h is set as the noise standard deviation of the transmission image, and Ω i is set to a 31×31 patch, centered about pixel i . This form of non-local filtration has been used in image processing, and can be especially useful for noise suppression when prior information is known about the structures within an image [31–33].…”