Preliminary clinical application of an adaptive iterative statistical reconstruction algorithm inhead and neck computed tomography angiography with low tube voltage and a low concentration of contrast medium

Objective To evaluate the feasibility of using a low concentration of contrast medium （Visipaque 270 mgl/mL）, low tube voltage, and an advanced image reconstruction algorithm in head and neck computed tomography angiography （CTA）. Methods Forty patients （22 men and 18 women; average age 48.7 ± 14.25 years; average body mass index 23.9 ± 3.7 kg/m^2） undergoing CTA for suspected vascular diseases were randomly assigned into two groups. Group A （n = 20） was administered 370 mgl/mL contrast medium, and group B （n = 20） was administered 270 mgl/mL contrast medium. Both groups were administered at a rate of 4.8 mL/s and an injection volume of 0.8 mL/kg. Images of group A were obtained with 120 kVp and filtered back projection （FBP） reconstruction, whereas images of group B were obtained with 80 kVp and 80% adaptive iterative statistical reconstruction algorithm （ASiR）. The CT values and standard deviations of intracranial arteries and image noise on the corona radiata were measured to calculate the contrast-to-noise ratio （CNR） and signal-to-noise ratio （SNR）. The beam-hardening artifacts （BHAs） around the skull base were calculated. Two readers evaluated the image quality with volume rendered images using scores from 1 to 5. The values between the two groups were statistically compared. Results The mean CT value of the intracranial arteries in group B was significantly higher than that in group A （P 〈 0.001）. The CNR and SNR values in group B were also statistically higher than those in group A （P 〈 0.001）. Image noise and BHAs were not significantly different between the two groups. The image quality score of VR images of in group B was significantly higher than that in group A （P = 0.001）. However, the quality scores of axial enhancement images in group B became significantly smaller than those in group A （P〈 0.001）. The CT dose index volume and dose-length product were decreased by 63.8% and 64%, respectively, in group B （P 〈 0.001 for both）. Conclusion Visipaque combined with 80 kVp and 80% ASiR provided similar image quality in intracranial CTA with 64% radiation dose reduction compared with the use of lopamidol, 120 kVp, and FBP reconstruc-tion.

Radar Imaging Based on Iterative Algorithms

It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.

Extension of emission expectation maximization lookalike algorithms to Bayesian algorithms

We recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization(ML-EM)algorithm.In this study,we extend these algorithms to Bayesian algorithms.The family of emission-EM-lookalike algorithms utilizes a multiplicative update scheme.The extension of these algorithms to Bayesian algorithms is achieved by introducing a new simple factor,which contains the Bayesian information.One of the extended algorithms can be applied to emission tomography and another to transmission tomography.Computer simulations are performed and compared with the corresponding un-extended algorithms.The total-variation norm is employed as the Bayesian constraint in the computer simulations.The newly developed algorithms demonstrate a stable performance.A simple Bayesian algorithm can be derived for any noise variance function.The proposed algorithms have properties such as multiplicative updating,non-negativity,faster convergence rates for bright objects,and ease of implementation.Our algorithms are inspired by Green’s one-steplate algorithm.If written in additive-update form,Green’s algorithm has a step size determined by the future image value,which is an undesirable feature that our algorithms do not have.