State of the art: iterative CT reconstruction techniques

Owing to recent advances in computing power, iterative reconstruction (IR) algorithms have become a clinically viable option in computed tomographic (CT) imaging. Substantial evidence is accumulating about the advantages of IR algorithms over established analytical methods, such as filtered back projection. IR improves image quality through cyclic image processing. Although all available solutions share the common mechanism of artifact reduction and/or potential for radiation dose savings, chiefly due to image noise suppression, the magnitude of these effects depends on the specific IR algorithm. In the first section of this contribution, the technical bases of IR are briefly reviewed and the currently available algorithms released by the major CT manufacturers are described. In the second part, the current status of their clinical implementation is surveyed. Regardless of the applied IR algorithm, the available evidence attests to the substantial potential of IR algorithms for overcoming traditional limitations in CT imaging

Beam hardening artifact reduction using dual energy computed tomography: implications for myocardial perfusion studies

Background: Myocardial perfusion computed tomography (CTP) using conventional single energy (SE) imaging is influenced by the presence of beam hardening artifacts (BHA), occasionally resembling perfusion defects and commonly observed at the left ventricular posterobasal wall (PB). We therefore sought to explore the ability of dual energy (DE) CTP to attenuate the presence of BHA. Methods: Consecutive patients without history of coronary artery disease who were referred for computed tomography coronary angiography due to atypical chest pain and a normal stress-rest SPECT and had absence or mild coronary atherosclerosis constituted the study population. The study group was acquired using DE and the control group using SE imaging. Results: Demographical characteristics were similar between groups, as well as the heart rate and the effective radiation dose. Myocardial signal density (SD) levels were evaluated in 280 basal segments among the DE group (140 PB segments for each energy level from 40 keV to 100 keV; and 140 reference segments), and in 40 basal segments (at the same locations) among the SE group. Among the DE group, myocardial SD levels and myocardial SD ratio evaluated at the reference segment were higher at low energy levels, with significantly lower SD levels at increasing energy levels. Myocardial signal-to-noise ratio was not significantly influenced by the energy level applied, although 70 keV was identified as the energy level with the best overall signal-to-noise ratio. Significant differences were identified between the PB segment and the reference segment among the lower energy levels, whereas at ≥ 70 keV myocardial SD levels were similar. Compared to DE reconstructions at the best energy level (70 keV), SE acquisitions showed no significant differences overall regarding myocardial SD levels among the reference segments. Conclusions: Beam hardening artifacts that influence the assessment of myocardial perfusion can be attenuated using DE at 70 keV or higher.Fil: Rodriguez Granillo, Gaston Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Cardiológicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiológicas; Argentina. Diagnóstico Maipú; ArgentinaFil: Carrascosa, Patricia. Diagnóstico Maipú; ArgentinaFil: Cipriano, Silvia. Diagnóstico Maipú; ArgentinaFil: De Zan, Macarena. Diagnóstico Maipú; ArgentinaFil: Deviggiano, Alejandro. Diagnóstico Maipú; ArgentinaFil: Capunay, Carlos. Diagnóstico Maipú; ArgentinaFil: Cury, Ricardo C.. Miami Cardiac and Vascular Institute and Baptist Health; Estados Unido

Evaluation of metal artefact reduction using dual-step adaptive thresholding technique in computed tomography

Background: Metal objects present in CT images may give rise to streak artefact. In the presence of severe artefacts, image quality may be extensively degraded and important clinical findings and pathology in the vicinity of the metal objects may be obscured. The purpose of this study is to evaluate the effectiveness of the dual-step adaptive thresholding technique as a method of metal artefact reduction in CT studies. Methodology: A total of 14 CT studies which contained metal-induced artefacts resulted from various surgical implants were retrieved from the Picture Archive Communication System (PACS). The CT images were corrected using the DSAT algorithm in MATLAB workspace to generate the artefact-corrected images with acceptable quality. Both groups of original images and artefact-corrected images were evaluated quantitatively using noise and SNR and qualitatively using visual evaluation by 2 evaluators. Level of significance was determined (p < 0.05). Results: A significant reduction of the noise were noticed in the corrected CT images following DSAT technique for metal artefact correction with the mean noise of 14.576 ± 11.7 as compared to the original images with mean of 40.177 ± 23.785 (p < 0.0005). A significant improvement of SNR was also demonstrated following DSAT correction with the mean SNR of 3.877 ± 3.931 for the corrected images in comparison to 3.614 ± 2.839 for the original images (p = 0.017). Visual evaluation has demonstrated reduced appearance of metal artefacts with increased conspicuity of adjacent structures (p < 0.05). Conclusion: Metal artefact correction using dual-step adaptive thresholding technique has the ability to suppress metal-induced artefacts with significant improvement of image quality

Unsupervised Multi Class Segmentation of 3D Images with Intensity Inhomogeneities

Intensity inhomogeneities in images constitute a considerable challenge in image segmentation. In this paper we propose a novel biconvex variational model to tackle this task. We combine a total variation approach for multi class segmentation with a multiplicative model to handle the inhomogeneities. Our method assumes that the image intensity is the product of a smoothly varying part and a component which resembles important image structures such as edges. Therefore, we penalize in addition to the total variation of the label assignment matrix a quadratic difference term to cope with the smoothly varying factor. A critical point of our biconvex functional is computed by a modified proximal alternating linearized minimization method (PALM). We show that the assumptions for the convergence of the algorithm are fulfilled by our model. Various numerical examples demonstrate the very good performance of our method. Particular attention is paid to the segmentation of 3D FIB tomographical images which was indeed the motivation of our work