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Open AccessJournal ArticleDOI

State of the Art: Iterative CT Reconstruction Techniques

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TLDR
In this contribution, the technical bases of IR are reviewed and the currently available algorithms released by the major CT manufacturers are described and the current status of their clinical implementation is surveyed.
Abstract
The current evidence on the clinical implementation of iterative reconstruction into CT protocols shows substantial promise for major improvements in image quality, chiefly noise reduction—with subsequent radiation dose reduction—and artifact suppression.

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Citations
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Journal ArticleDOI

Generative Adversarial Networks for Noise Reduction in Low-Dose CT

TL;DR: Noise reduction improved quantification of low-density calcified inserts in phantom CT images and allowed coronary calcium scoring in low-dose patient CT images with high noise levels.
Journal ArticleDOI

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters.

TL;DR: Many RFs were redundant and nonreproducible, and if all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs because of redundancy.
Journal ArticleDOI

A Perspective on Deep Imaging

TL;DR: The combination of tomographic imaging and deep learning, or machine learning in general, promises to empower not only image analysis but also image reconstruction as discussed by the authors, and the latter aspect is considered in this perspective article with an emphasis on medical imaging to develop a new generation of image reconstruction theories and techniques.
Journal ArticleDOI

3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network

TL;DR: A conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising, which has a better performance in that it suppresses image noise and preserves subtle structures.
Journal ArticleDOI

Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction.

TL;DR: In this article, a modularized neural network for low-dose CT (LDCT) was proposed and compared with commercial iterative reconstruction methods from three leading CT vendors, and the learned workflow allows radiologists-in-the-loop to optimize the denoising depth in a task-specific fashion.
References
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Journal ArticleDOI

Computerized transverse axial scanning (tomography): Part I. Description of system. 1973.

TL;DR: A technique in which X-ray transmission readings are taken through the head at a multitude of angles: from these data, absorption values of the material contained within the head are calculated on a computer and presented as a series of pictures of slices of the cranium.
Journal ArticleDOI

Algebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography

TL;DR: The method works for totally asymmetric objects, and requires little computer time or storage, and is also applicable to X-ray photography, and may greatly reduce the exposure compared to current methods of body-section radiography.
Journal ArticleDOI

Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm.

TL;DR: This implementation of the Algebraic Reconstruction Technique appears to have a computational advantage over the more traditional implementation of ART and potential applications include image reconstruction in conjunction with ray tracing for ultrasound and microwave tomography.
Related Papers (5)
Trending Questions (1)
What are the different image production procedures for CT-scans?

The paper does not provide information about the different image production procedures for CT-scans. The paper focuses on the clinical implementation and benefits of iterative reconstruction techniques in CT protocols.