Literature DB >> 33392038

Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality.

Angélique Bernard1, Pierre-Olivier Comby1, Brivaël Lemogne1, Karim Haioun2, Frédéric Ricolfi1, Olivier Chevallier3, Romaric Loffroy3.   

Abstract

BACKGROUND: To assess the radiation dose and image quality of cardiac computed tomography angiography (CCTA) in an acute stroke imaging protocol using a deep learning reconstruction (DLR) method compared to a hybrid iterative reconstruction algorithm.
METHODS: Retrospective analysis of 296 consecutive patients admitted to the emergency department for stroke suspicion. All patients underwent a stroke CT imaging protocol including a non-enhanced brain CT, a brain perfusion CT imaging if necessary, a CT angiography (CTA) of the supra-aortic vessels, a CCTA and a post-contrast brain CT. The CCTA was performed with a prospectively ECG-gated volume acquisition. Among all CT scans performed, 143 were reconstructed with an iterative reconstruction algorithm (AIDR 3D, adaptive iterative dose reduction three dimensional) and 146 with a DLR algorithm (AiCE, advanced intelligent clear-IQ engine). Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality (IQ) scored from 1 to 4 were assessed. Dose-length product (DLP), volume CT dose index (CTDIvol) and effective dose (ED) were obtained.
RESULTS: The radiation dose was significantly lower with AiCE than with AIDR 3D (DLP =106.4±50.0 vs. 176.1±37.1 mGy·cm, CTDIvol =6.9±3.2 vs. 11.5±2.2 mGy, and ED =1.5±0.7 vs. 2.5±0.5 mSv) (P<0.001). The median SNR and CNR were higher [9.9 (IQR, 8.1-12.3); and 12.6 (IQR, 10.5-15.5), respectively], with AiCE than with AIDR 3D [6.5 (IQR, 5.2-8.5); and 8.4 (IQR, 6.7-11.0), respectively] (P<0.001). SNR and CNR were increased by 51% and 49%, respectively, with AiCE compared to AIDR 3D. The image quality was significantly better with AiCE (mean IQ score =3.4±0.7) than with AIDR 3D (mean IQ score =3±0.9) (P<0.001).
CONCLUSIONS: The use of a DLR algorithm for cardiac CTA in an acute stroke imaging protocol reduced the radiation dose by about 40% and improved the image quality by about 50% compared to an iterative reconstruction algorithm. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Entities:  

Keywords:  Computed tomography angiography (CTA); artificial intelligence; cardiac imaging; deep learning; image reconstruction

Year:  2021        PMID: 33392038      PMCID: PMC7719916          DOI: 10.21037/qims-20-626

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  22 in total

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Authors:  James P Earls; Elise L Berman; Bruce A Urban; Charlene A Curry; Judith L Lane; Robert S Jennings; Colin C McCulloch; Jiang Hsieh; John H Londt
Journal:  Radiology       Date:  2008-01-14       Impact factor: 11.105

2.  State of the Art: Iterative CT Reconstruction Techniques.

Authors:  Lucas L Geyer; U Joseph Schoepf; Felix G Meinel; John W Nance; Gorka Bastarrika; Jonathon A Leipsic; Narinder S Paul; Marco Rengo; Andrea Laghi; Carlo N De Cecco
Journal:  Radiology       Date:  2015-08       Impact factor: 11.105

3.  Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

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Journal:  Eur Radiol       Date:  2019-04-11       Impact factor: 5.315

4.  Low-dose CT via convolutional neural network.

Authors:  Hu Chen; Yi Zhang; Weihua Zhang; Peixi Liao; Ke Li; Jiliu Zhou; Ge Wang
Journal:  Biomed Opt Express       Date:  2017-01-09       Impact factor: 3.732

5.  The feasibility of Forward-projected model-based Iterative Reconstruction SoluTion (FIRST) for coronary 320-row computed tomography angiography: A pilot study.

Authors:  Eriko Maeda; Nobuo Tomizawa; Shigeaki Kanno; Koichiro Yasaka; Takatoshi Kubo; Kenji Ino; Rumiko Torigoe; Kuni Ohtomo
Journal:  J Cardiovasc Comput Tomogr       Date:  2016-11-09

6.  Coronary CT angiography with single-source and dual-source CT: comparison of image quality and radiation dose between prospective ECG-triggered and retrospective ECG-gated protocols.

Authors:  Akmal Sabarudin; Zhonghua Sun; Ahmad Khairuddin Md Yusof
Journal:  Int J Cardiol       Date:  2012-10-23       Impact factor: 4.164

7.  CT image quality improvement using Adaptive Iterative Dose Reduction with wide-volume acquisition on 320-detector CT.

Authors:  Alban Gervaise; Benoît Osemont; Sophie Lecocq; Alain Noel; Emilien Micard; Jacques Felblinger; Alain Blum
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8.  Radiation dose reduction in cardiovascular CT angiography with iterative reconstruction (AIDR 3D) in a swine model: a model of paediatric cardiac imaging.

Authors:  Pengfei Zhao; Yang Hou; Qin Liu; Yue Ma; Qiyong Guo
Journal:  Clin Radiol       Date:  2016-05-11       Impact factor: 2.350

Review 9.  When Machines Think: Radiology's Next Frontier.

Authors:  Keith J Dreyer; J Raymond Geis
Journal:  Radiology       Date:  2017-12       Impact factor: 11.105

10.  Simulated 50 % radiation dose reduction in coronary CT angiography using adaptive iterative dose reduction in three-dimensions (AIDR3D).

Authors:  Marcus Y Chen; Michael L Steigner; Steve W Leung; Kanako K Kumamaru; Kurt Schultz; Richard T Mather; Andrew E Arai; Frank J Rybicki
Journal:  Int J Cardiovasc Imaging       Date:  2013-02-13       Impact factor: 2.357

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  9 in total

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2.  Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study.

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3.  Performance evaluation of using shorter contrast injection and 70 kVp with deep learning image reconstruction for reduced contrast medium dose and radiation dose in coronary CT angiography for children: a pilot study.

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4.  Deep Learning-Based Reconstruction vs. Iterative Reconstruction for Quality of Low-Dose Head-and-Neck CT Angiography with Different Tube-Voltage Protocols in Emergency-Department Patients.

Authors:  Marc Lenfant; Pierre-Olivier Comby; Kevin Guillen; Felix Galissot; Karim Haioun; Anthony Thay; Olivier Chevallier; Frédéric Ricolfi; Romaric Loffroy
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5.  Improving the image quality of pediatric chest CT angiography with low radiation dose and contrast volume using deep learning image reconstruction.

Authors:  Jihang Sun; Haoyan Li; Jianying Li; Tong Yu; Michelle Li; Zuofu Zhou; Yun Peng
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Review 6.  Robotics and Artificial Intelligence in Endovascular Neurosurgery.

Authors:  Javier Bravo; Arvin R Wali; Brian R Hirshman; Tilvawala Gopesh; Jeffrey A Steinberg; Bernard Yan; J Scott Pannell; Alexander Norbash; James Friend; Alexander A Khalessi; David Santiago-Dieppa
Journal:  Cureus       Date:  2022-03-30

7.  Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions.

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Journal:  Sci Rep       Date:  2022-09-07       Impact factor: 4.996

8.  A miniature U-net for k-space-based parallel magnetic resonance imaging reconstruction with a mixed loss function.

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9.  Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

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Journal:  Med Phys       Date:  2022-06-24       Impact factor: 4.506

  9 in total

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