Background: The aim of this study was to evaluate the influences of iterative model reconstruction (IMR) and hybrid-iterative reconstruction (HIR) techniques on quantitative and qualitative image analysis of 256-slice coronary computed tomography angiography (CCTA). Methods: Sixty-one patients (30 men and 31 women with mean age of 60. 68 ± 9. 13 years) who had undergone CCTA by the 256-slice CT scanner were evaluated. The raw data were reconstructed using HIR and IMR algorithms. For objective assessment of image quality, parameters of noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were obtained for both reconstruction algorithms. For subjective assessment, two physician specialists evaluated image quality using a 5-point scale. Findings: The mean image noise on HIR and IMR images was 32. 74 ± 6. 04 and 25. 15 ± 3. 59, respectively. In IMR, the CNR method (28. 81 ± 5. 13) was significantly better than HIR method (23. 06 ± 5. 03) (P < 0. 001). However, the HIR method (4. 62 ± 0. 39) was higher than the IMR method (4. 48 ± 0. 45) in terms of qualitative or subjective criteria (P < 0. 001). Conclusion: The IMR method can improve quantitative criteria of image, reduce noise, and increase SNR and CNR of images better than HIR method. However, the images which reconstructed by HIR were superior to IMR in terms of qualitative criteria.

Background: The aim of this study was to evaluate the impact of using adaptive statistical iterative reconstruction (ASiR) method on image quality and reduction of organ doses, effective dose, and carcinogenesis risk from coronary computed tomography angiography (CCTA) in 64-slice CT scan. Methods: This cross-sectional study was performed on two groups of patients. The first group consisted of 185 patients (90 women and 95 men; mean age: 55. 65 ± 14. 77 years) who underwent CCTA with the conventional filter back projection (FBP) reconstruction. The second group consisted of 172 patients (80 women and 92 men; mean age: 58. 59 ± 11. 22 years) who were tested using ASiR reconstruction. Organ doses, effective dose, and carcinogenic risk were compared between the groups. Moreover, image quality criteria including noise, contrast-to-noise ratio, signal-to-noise ratio, CT numbers of the left coronary artery, and left ventricle were compared between the groups. Findings: There were at least 40% reduction in the effective dose (11. 94 ± 5. 64 vs. 20. 38 ± 6. 84 mSv; P < 0. 001) and risk of carcinogenesis (8. 97 ± 4. 71 vs. 16. 09 ± 8. 42 in 10, 000 people; P < 0. 001) between the FBP and AsiR methods. No significant differences were observed between image quality parameters in the two reconstruction methods (P > 0. 050). Conclusion: The application of ASiR method compared to conventional FBP method can reduce the dose of organs, the effective dose, and the carcinogenic risk of CCTA without compromising the image quality.

Background: The objective of this study was to investigate the influence of iterative reconstruction (IR) algorithm on radiation dose and image quality of computed tomography (CT) scans of patients with malignant pancreatic lesions by designing a new protocol. Methods: The pancreas CT was performed on 40 patients (23 males and 17 females) with a 160-slice CT scan machine. The pancreatic parenchymal phase was performed in two stages: one with a usual dose of radiation and the other one after using a reduced dose of radiation. The images obtained with usual dose were reconstructed with Filtered Back Projection (FBP) method (Protocol A),and the images obtained with the reduced dose were reconstructed with both FBP (Protocol B) and IR method (Protocol C). The quality of images and radiation dose were compared among the three protocols. Results: image noise was significantly lower with Protocol C (10. 80) than with Protocol A (14. 98) and Protocol B (20. 60) (P < 0. 001). Signal-to-noise ratio and contrast-to-noise ratio were significantly higher with Protocol C than with Protocol A and Protocol B (P < 0. 001). Protocol A and Protocol C were not significantly different in terms of image quality scores. Effective dose was reduced by approximately 48% in Protocol C compared with Protocol A (1. 20 ±,0. 53 mSv vs. 2. 33 ±,0. 86 mSv, P < 0. 001). Conclusion: Results of this study showed that applying the IR method compared to the FBP method can improve objective image quality, maintain subjective image quality, and reduce the radiation dose of the patients undergo pancreas CT.

Classical image deconvolution seeks an estimate of the true image when the blur kernel or the point spread function (PSF) of the blurring system is known a priori. However, blind image deconvolution addresses the much more complicated, but realistic problem where the PSF is unknown. Bayesian inference approach with appropriate priors on the image and the blur has been used successfully to solve this blind problem, in particular with a Gaussian prior and a joint maximum a posteriori (JMAP) estimation. However, this technique is unstable and suffers from significant ringing artifacts in various applications. To overcome these limitations, we propose a regularized version using $H^1$ regularization terms on both the sharp image and the blur kernel. We present also useful techniques for estimating the smoothing parameters.  We were able to derive an efficient algorithm that produces high quality deblurred results compared to some well-known methods in the literature.

Background: Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line– area integral model (AIM) that highlights the advantages of both LIM and AIMs. Methods: The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow‑ angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM). Results: The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2. 9 times faster. Differences in root mean square error and peak signal‑ to‑ noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0. 05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0. 05), though computations were 4. 9 times more intensive than the proposed LIM. Conclusion: We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM– AIM that achieves significantly improved images though with its computational requirements.