Literature DB >> 31789682

Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality.

Yuko Nakamura1, Toru Higaki, Fuminari Tatsugami, Yukiko Honda, Keigo Narita, Motonori Akagi, Kazuo Awai.   

Abstract

Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduction. We review the ability of DL to reduce the image noise, present the advantages and disadvantages of computed tomography image reconstruction, and examine the potential value of new DL-based computed tomography image reconstruction.

Mesh：

Year:  2020        PMID： 31789682     DOI： 10.1097/RCT.0000000000000928

Source DB:  PubMed          Journal:  J Comput Assist Tomogr        ISSN： 0363-8715            Impact factor:   1.826


  8 in total

Review 1.  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

2.  Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT.

Authors:  Akio Tamura; Eisuke Mukaida; Yoshitaka Ota; Iku Nakamura; Kazumasa Arakita; Kunihiro Yoshioka
Journal:  Quant Imaging Med Surg       Date:  2022-05

3.  Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi.

Authors:  Xiaoxiao Zhang; Gumuyang Zhang; Lili Xu; Xin Bai; Jiahui Zhang; Min Xu; Jing Yan; Daming Zhang; Zhengyu Jin; Hao Sun
Journal:  Insights Imaging       Date:  2022-10-08

4.  Strain Analysis in Patients at High-Risk for COPD Using Four-Dimensional Dynamic-Ventilation CT.

Authors:  Yanyan Xu; Tian Liang; Yanhui Ma; Sheng Xie; Hongliang Sun; Lei Wang; Yinghao Xu
Journal:  Int J Chron Obstruct Pulmon Dis       Date:  2022-05-07

5.  Deep learning versus iterative image reconstruction algorithm for head CT in trauma.

Authors:  Zlatan Alagic; Jacqueline Diaz Cardenas; Kolbeinn Halldorsson; Vitali Grozman; Stig Wallgren; Chikako Suzuki; Johan Helmenkamp; Seppo K Koskinen
Journal:  Emerg Radiol       Date:  2022-01-05

6.  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
Journal:  Diagnostics (Basel)       Date:  2022-05-21

7.  Deep Learning Image Processing Enables 40% Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care : A Prospective Multicenter Multireader Study.

Authors:  S Bash; B Johnson; W Gibbs; T Zhang; A Shankaranarayanan; L N Tanenbaum
Journal:  Clin Neuroradiol       Date:  2021-11-30       Impact factor: 3.649

8.  Difference in Local Lung Movement During Tidal Breathing Between COPD Patients and Asthma Patients Assessed by Four-dimensional Dynamic-ventilation CT Scan.

Authors:  Eisuke Mochizuki; Yoshiihiro Kawai; Keisuke Morikawa; Yutaro Ito; Namio Kagoo; Tsutomu Kubota; Koshiro Ichijyo; Masahiro Uehara; Masanori Harada; Shun Matsuura; Masaru Tsukui; Naoki Koshimizu
Journal:  Int J Chron Obstruct Pulmon Dis       Date:  2020-11-20
  8 in total

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