Computer simulation of low-dose CT with clinical lung image database: a preliminary study

Large samples of raw low-dose CT (LDCT) projections on lungs are needed for evaluating or designing novel and effective reconstruction algorithms suitable for lung LDCT imaging. However, there exists radiation risk when getting them from clinical CT scanning. To avoid the problem, a new strategy for producing large samples of lung LDCT projections with computer simulations is proposed in this paper. In the simulation, clinical images from the publicly available medical image database-the Lung Image Database Consortium(LIDC) and Image Database Resource Initiative (IDRI) database (LIDC/IDRI) are used as the projected object to form the noise-free sinogram. Then by adding a Poisson distributed quantum noise plus Gaussian distributed electronic noise to the projected transmission data calculated from the noise-free sinogram, different noise levels of LDCT projections are obtained. At last the LDCT projections are used for evaluating two reconstruction strategies. One is the conventional filtered back projection (FBP) algorithm and the other is FBP reconstruction from the filtered sinogram with penalized weighted least square criterion (PWLS-FBP). Images reconstructed with the LDCT simulations have shown that the PWLS-FBP algorithm performs better than the FBP algorithm in reducing streaking artifacts and preserving resolution. Preliminary results indicate that the feasibility of the proposed lung LDCT simulation strategy for helping to determine advanced reconstruction algorithms.

[1]  Massimo Bellomi,et al.  Low-dose CT: technique, reading methods and image interpretation , 2013, Cancer imaging : the official publication of the International Cancer Imaging Society.

[2]  M. Roizen.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[3]  Felix G. Meinel,et al.  Detection and size measurements of pulmonary nodules in ultra-low-dose CT with iterative reconstruction compared to low dose CT. , 2016, European journal of radiology.

[4]  Zhaoying Bian,et al.  A Simple Low-Dose X-Ray CT Simulation From High-Dose Scan , 2015, IEEE Transactions on Nuclear Science.

[5]  Hongli Lin,et al.  Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative. , 2015, Academic radiology.

[6]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[7]  Qiu Wang,et al.  A low dose simulation tool for CT systems with energy integrating detectors. , 2013, Medical physics.

[8]  Jianhua Ma,et al.  Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images , 2016, IEEE Transactions on Medical Imaging.

[9]  Zhengrong Liang,et al.  Variance analysis of x-ray CT sinograms in the presence of electronic noise background. , 2012, Medical physics.

[10]  Wendy J. Post,et al.  Volumetric measurement of pulmonary nodules at low-dose chest CT: effect of reconstruction setting on measurement variability , 2009, European Radiology.

[11]  A. Huber,et al.  Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging , 2016, European Radiology.