Literature DB >> 31660806

State of the Art in Abdominal CT: The Limits of Iterative Reconstruction Algorithms.

Achille Mileto1, Luis S Guimaraes1, Cynthia H McCollough1, Joel G Fletcher1, Lifeng Yu1.   

Abstract

The development and widespread adoption of iterative reconstruction (IR) algorithms for CT have greatly facilitated the contemporary practice of radiation dose reduction during abdominal CT examinations. IR mitigates the increased image noise typically associated with reduced radiation dose levels, thereby maintaining subjective image quality and diagnostic confidence for a variety of clinical tasks. Mounting evidence, however, points to important limitations of this method involving radiologists' ability to perform low-contrast diagnostic tasks, such as the detection of liver metastases or pancreatic masses. Radiologists need to be aware that use of IR can result in a decline of spatial resolution for low-contrast structures and degradation of low-contrast detectability when radiation dose reductions exceed approximately 25%. This article will review the principles of IR algorithm technology, describe the various commercial implementations of IR in CT, and review published studies that have evaluated the ability of IR to preserve diagnostic performance for low-contrast diagnostic tasks. In addition, future developments in CT noise reduction techniques and methods to rigorously evaluate their diagnostic performance will be discussed. © RSNA, 2019.

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Year:  2019        PMID: 31660806     DOI: 10.1148/radiol.2019191422

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  26 in total

1.  A Web-Based Software Platform for Efficient and Quantitative CT Image Quality Assessment and Protocol Optimization.

Authors:  Mingdong Fan; Theodore Thayib; Liqiang Ren; Scott Hsieh; Cynthia McCollough; David Holmes; Lifeng Yu
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2021-02-15

Review 2.  Advanced CT techniques for assessing hepatocellular carcinoma.

Authors:  Yuko Nakamura; Toru Higaki; Yukiko Honda; Fuminari Tatsugami; Chihiro Tani; Wataru Fukumoto; Keigo Narita; Shota Kondo; Motonori Akagi; Kazuo Awai
Journal:  Radiol Med       Date:  2021-05-05       Impact factor: 3.469

3.  A phantom study comparing low-dose CT physical image quality from five different CT scanners.

Authors:  Yali Li; Yaojun Jiang; Huilong Liu; Xi Yu; Sihui Chen; Duoshan Ma; Jianbo Gao; Yan Wu
Journal:  Quant Imaging Med Surg       Date:  2022-01

4.  Quantitative measurements of emphysema in ultra-high resolution computed tomography using model-based iterative reconstruction in comparison to that using hybrid iterative reconstruction.

Authors:  Shun Muramatsu; Kazuhiro Sato; Tsuneo Yamashiro; Kunio Doi
Journal:  Phys Eng Sci Med       Date:  2022-01-13

5.  Evaluation of Apparent Noise on CT Images Using Moving Average Filters.

Authors:  Keisuke Fujii; Keiichi Nomura; Kuniharu Imai; Yoshihisa Muramatsu; So Tsushima; Hiroyuki Ota
Journal:  J Digit Imaging       Date:  2021-11-10       Impact factor: 4.056

6.  Efficient Evaluation of Low-contrast Detectability of Deep-CNN-based CT Reconstruction Using Channelized Hotelling Observer on the ACR Accreditation Phantom.

Authors:  Mingdong Fan; Zhongxing Zhou; Thomas Vrieze; Jia Wang; Cynthia McCollough; Lifeng Yu
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2022-04-04

7.  Observer Performance for Detection of Pulmonary Nodules at Chest CT over a Large Range of Radiation Dose Levels.

Authors:  Joel G Fletcher; David L Levin; Anne-Marie G Sykes; Rebecca M Lindell; Darin B White; Ronald S Kuzo; Vighnesh Suresh; Lifeng Yu; Shuai Leng; David R Holmes; Akitoshi Inoue; Matthew P Johnson; Rickey E Carter; Cynthia H McCollough
Journal:  Radiology       Date:  2020-09-29       Impact factor: 11.105

8.  Performance of clinically available deep learning image reconstruction in computed tomography: a phantom study.

Authors:  Hiroki Kawashima; Katsuhiro Ichikawa; Tadanori Takata; Wataru Mitsui; Hiroshi Ueta; Norihide Yoneda; Satoshi Kobayashi
Journal:  J Med Imaging (Bellingham)       Date:  2020-12-16

9.  High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses.

Authors:  Wanjiang Li; Kaiyue Diao; Yuting Wen; Tao Shuai; Yongchun You; Jin Zhao; Kai Liao; Chunyan Lu; Jianqun Yu; Yong He; Zhenlin Li
Journal:  Eur Radiol       Date:  2022-01-21       Impact factor: 5.315

10.  Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules.

Authors:  Kai Ye; Min Chen; Qiao Zhu; Yuliu Lu; Huishu Yuan
Journal:  Quant Imaging Med Surg       Date:  2021-06
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