Renovascular CT: comparison between adaptive statistical iterative reconstruction and model-based iterative reconstruction

Noda, Yoshifumi; Goshima, Satoshi; Koyasu, Hiromi; Shigeyama, S.; Miyoshi, Toshiharu; Kawada, Hiroshi; Kawai, Nobuyuki

doi:10.1016/j.crad.2017.06.002

Cited by 14 publications

(5 citation statements)

References 28 publications

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“…In one pediatric study the reported sensitivity and specificity were higher at 88% and 81% respectively [20]. Recent studies have demonstrated that full iterative reconstruction techniques of CTA can reduce noise and increase contrast [24–26]. In particular, model-based iterative reconstruction has been reported to improve the accuracy of vessel diameter measurements [25].…”

Section: Discussionmentioning

confidence: 99%

Imaging studies in pediatric fibromuscular dysplasia (FMD): a single-center experience

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Imaging studies in pediatric fibromuscular dysplasia (FMD): a single-center experience

et al. 2018

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“…Various AI algorithms have also been applied and developed in the field of medical imaging. e iterative reconstruction algorithm under AI can achieve noise control through the description and expression of noise characteristics, so it has been widely used in the reconstruction of low-dose CT imaging, of which the ASiR algorithm is the most widely used [19][20][21][22]. erefore, the effect of CT image reconstruction using the ASiR algorithm under AI was evaluated through subjective evaluation and objective measurement firstly.…”

Section: Discussionmentioning

confidence: 99%

Chest Computed Tomography Images in Neonatal Bronchial Pneumonia under the Adaptive Statistical Iterative Reconstruction Algorithm

Sun

Tian

et al. 2021

Journal of Healthcare Engineering

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This study was to explore the application value of chest computed tomography (CT) images processed by artificial intelligence (AI) algorithms in the diagnosis of neonatal bronchial pneumonia (NBP). The AI adaptive statistical iterative reconstruction (ASiR) algorithm was adopted to reconstruct the chest CT image to compare and analyze the effect of the reconstruction of CT image under the ASiR algorithm under different preweight and postweight values based on the objective measurement and subjective evaluation. 85 neonates with pneumonia treated in hospital from September 1, 2015, to July 1, 2020, were selected as the research objects to analyze their CT imaging characteristics. Subsequently, the peripheral blood of healthy neonates during the same period was collected, and the levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were detected. The efficiency of CT examination, CRP, ESR, and combined examination in the diagnosis of NBP was analyzed. The results showed that the subjective quality score, lung window subjective score, and mediastinal window subjective score were the highest after CT image reconstruction when the preweight value of the ASiR algorithm was 50%. After treatment, 79 NBP cases (92.9%) showed ground-glass features in CT images. Compared with the healthy neonates, the levels of CRP and ESR in the peripheral blood of neonates with bronchial pneumonia were much lower ( P < 0.05 ). The accuracy rates of CT examination, CRP examination, ESR examination, CRP + ESR examination, and CRP + ESR + CT examination for the diagnosis of NBP were 80.7%, 75.3%, 75.1%, 80.3%, and 98.6%, respectively. CT technology based on AI algorithm showed high clinical application value in the feature analysis of NBP.

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“…To make INDIA comparable to an optimization process such as NLR, we should define a cost function that INDIA can minimize. Fortunately, the constraint of the limited support can be interpreted as an operation in which one looks for the magnitude |T|k that minimizes the value of the mean background noise [27], where the background is the entire area outside the a priori known object support. In comparison to other nonlinear algorithms, we conclude that INDIA performs an extensive search over N×M variables of the magnitude |T|k that minimize a limited cost function, whereas NLR and LRRA search over only two variables that can minimize an unlimited number of cost functions.…”

Section: Methodsmentioning

confidence: 99%

Incoherent nonlinear deconvolution using an iterative algorithm for recovering limited-support images from blurred digital photographs

Rosen,

Anand

2023

Preprint

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Recovering original images from blurred images is a challenging task. We propose a new deconvolution method termed incoherent nonlinear deconvolution using an iterative algorithm (INDIA). Two inputs are introduced into the algorithm: one is a random or engineered point spread function of the scattering system, and the other is a blurred or distorted image of some object outputted from this system. The two functions are Fourier transformed, and their phase distributions are processed independently of their magnitude. The algorithm yields the image of the original object with reduced blurring effects. The results of the new method are compared to two linear and two nonlinear algorithms under various types of blurs. The root mean square error and structural similarity between the original and recovered images are chosen as the comparison criteria between the five different algorithms. The simulation and experimental results confirm the superior performance of INDIA compared to the other tested deblurring methods.

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Renovascular CT: comparison between adaptive statistical iterative reconstruction and model-based iterative reconstruction

Cited by 14 publications

References 28 publications

Imaging studies in pediatric fibromuscular dysplasia (FMD): a single-center experience

Imaging studies in pediatric fibromuscular dysplasia (FMD): a single-center experience

Chest Computed Tomography Images in Neonatal Bronchial Pneumonia under the Adaptive Statistical Iterative Reconstruction Algorithm

Incoherent nonlinear deconvolution using an iterative algorithm for recovering limited-support images from blurred digital photographs

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