Literature DB >> 30498876

Improvement of image quality at CT and MRI using deep learning.

Toru Higaki1, Yuko Nakamura2, Fuminari Tatsugami2, Takeshi Nakaura3, Kazuo Awai2.   

Abstract

Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for improving the image quality as "noise and artifact reduction", "super resolution" and "image acquisition and reconstruction". For each category, we present and outline the features of some studies.

Keywords:  Computed tomography; Deep learning; Image quality improvement; Magnetic resonance imaging

Mesh：

Year:  2018        PMID： 30498876     DOI： 10.1007/s11604-018-0796-2

Source DB:  PubMed          Journal:  Jpn J Radiol        ISSN： 1867-1071            Impact factor:   2.374


  27 in total

Review 1.  Machine Learning for Medical Imaging.

Authors:  Bradley J Erickson; Panagiotis Korfiatis; Zeynettin Akkus; Timothy L Kline
Journal:  Radiographics       Date:  2017-02-17       Impact factor: 5.333

2.  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

3.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.

Authors:  Kai Zhang; Wangmeng Zuo; Yunjin Chen; Deyu Meng; Lei Zhang
Journal:  IEEE Trans Image Process       Date:  2017-02-01       Impact factor: 10.856

4.  Deep Convolutional Neural Network for Inverse Problems in Imaging.

Authors:  Michael T McCann; Emmanuel Froustey; Michael Unser
Journal:  IEEE Trans Image Process       Date:  2017-06-15       Impact factor: 10.856

5.  Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI.

Authors:  Ki Hwan Kim; Sung-Hong Park
Journal:  Magn Reson Imaging       Date:  2016-11-27       Impact factor: 2.546

6.  q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans.

Authors:  Vladimir Golkov; Alexey Dosovitskiy; Jonathan I Sperl; Marion I Menzel; Michael Czisch; Philipp Samann; Thomas Brox; Daniel Cremers
Journal:  IEEE Trans Med Imaging       Date:  2016-04-06       Impact factor: 10.048

7.  Image Super-Resolution Using Deep Convolutional Networks.

Authors:  Chao Dong; Chen Change Loy; Kaiming He; Xiaoou Tang
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2016-02       Impact factor: 6.226

8.  A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

Authors:  Eunhee Kang; Junhong Min; Jong Chul Ye
Journal:  Med Phys       Date:  2017-10       Impact factor: 4.071

9.  Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network.

Authors:  Dufan Wu; Kyungsang Kim; Georges El Fakhri; Quanzheng Li
Journal:  IEEE Trans Med Imaging       Date:  2017-09-15       Impact factor: 10.048

10.  A neural network-based method for spectral distortion correction in photon counting x-ray CT.

Authors:  Mengheng Touch; Darin P Clark; William Barber; Cristian T Badea
Journal:  Phys Med Biol       Date:  2016-07-29       Impact factor: 3.609
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  24 in total

1.  Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge.

Authors:  Elizabeth M McKenzie; Anand Santhanam; Dan Ruan; Daniel O'Connor; Minsong Cao; Ke Sheng
Journal:  Med Phys       Date:  2020-01-02       Impact factor: 4.071

2.  Celebrating the beginning of international journal collaboration.

Authors:  Shinji Naganawa; Yukunori Korogi
Journal:  Jpn J Radiol       Date:  2020-01       Impact factor: 2.374

Review 3.  A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.

Authors:  Rintaro Ito; Shingo Iwano; Shinji Naganawa
Journal:  Diagn Interv Radiol       Date:  2020-09       Impact factor: 2.630

Review 4.  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

Review 5.  Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.

Authors:  Yasasvi Tadavarthi; Valeria Makeeva; William Wagstaff; Henry Zhan; Anna Podlasek; Neil Bhatia; Marta Heilbrun; Elizabeth Krupinski; Nabile Safdar; Imon Banerjee; Judy Gichoya; Hari Trivedi
Journal:  Radiol Artif Intell       Date:  2022-02-02

6.  Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study.

Authors:  Yoshiharu Ohno; Naruomi Akino; Yasuko Fujisawa; Hirona Kimata; Yuya Ito; Kenji Fujii; Yumi Kataoka; Yoshihiro Ida; Yuka Oshima; Nayu Hamabuchi; Chika Shigemura; Ayumi Watanabe; Yuki Obama; Satomu Hanamatsu; Takahiro Ueda; Hirotaka Ikeda; Kazuhiro Murayama; Hiroshi Toyama
Journal:  Eur Radiol       Date:  2022-07-16       Impact factor: 7.034

7.  Artificial Intelligence in Imaging: The Radiologist's Role.

Authors:  Daniel L Rubin
Journal:  J Am Coll Radiol       Date:  2019-09       Impact factor: 5.532

Review 8.  Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

Authors:  Dana J Lin; Patricia M Johnson; Florian Knoll; Yvonne W Lui
Journal:  J Magn Reson Imaging       Date:  2020-02-12       Impact factor: 4.813

9.  Novel Intraoperative Navigation Using Ultra-High-Resolution CT in Robot-Assisted Partial Nephrectomy.

Authors:  Kiyoshi Takahara; Yoshiharu Ohno; Kosuke Fukaya; Ryo Matsukiyo; Takuhisa Nukaya; Masashi Takenaka; Kenji Zennami; Manabu Ichino; Naohiko Fukami; Hitomi Sasaki; Mamoru Kusaka; Hiroshi Toyama; Makoto Sumitomo; Ryoichi Shiroki
Journal:  Cancers (Basel)       Date:  2022-04-18       Impact factor: 6.639

Review 10.  Computed Tomography Techniques, Protocols, Advancements, and Future Directions in Liver Diseases.

Authors:  Naveen M Kulkarni; Alice Fung; Avinash R Kambadakone; Benjamin M Yeh
Journal:  Magn Reson Imaging Clin N Am       Date:  2021-08       Impact factor: 1.376
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