Improvement of Image Quality at CT and MRI Using Deep Learning

Overview

Journal Jpn J Radiol

Publisher Springer

Specialty Radiology

Date 2018 Dec 1

PMID 30498876

Citations 72

Authors

Toru Higaki

Yuko Nakamura

Fuminari Tatsugami

Takeshi Nakaura

Kazuo Awai

Affiliations

Soon will be listed here.

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.

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References

Dong C, Loy C, He K, Tang X . Image Super-Resolution Using Deep Convolutional Networks. IEEE Trans Pattern Anal Mach Intell. 2016; 38(2):295-307. DOI: 10.1109/TPAMI.2015.2439281. View

Golkov V, Dosovitskiy A, Sperl J, Menzel M, Czisch M, Samann P . q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans. IEEE Trans Med Imaging. 2016; 35(5):1344-1351. DOI: 10.1109/TMI.2016.2551324. View

Touch M, Clark D, Barber W, Badea C . A neural network-based method for spectral distortion correction in photon counting x-ray CT. Phys Med Biol. 2016; 61(16):6132-53. PMC: 5056429. DOI: 10.1088/0031-9155/61/16/6132. View

Kim K, Park S . Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI. Magn Reson Imaging. 2016; 37:139-146. DOI: 10.1016/j.mri.2016.11.020. View

Zhang K, Zuo W, Chen Y, Meng D, Zhang L . Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Trans Image Process. 2017; 26(7):3142-3155. DOI: 10.1109/TIP.2017.2662206. View

Erickson B, Korfiatis P, Akkus Z, Kline T . Machine Learning for Medical Imaging. Radiographics. 2017; 37(2):505-515. PMC: 5375621. DOI: 10.1148/rg.2017160130. View

Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J . Low-dose CT via convolutional neural network. Biomed Opt Express. 2017; 8(2):679-694. PMC: 5330597. DOI: 10.1364/BOE.8.000679. View

Jin K, McCann M, Froustey E, Unser M . Deep Convolutional Neural Network for Inverse Problems in Imaging. IEEE Trans Image Process. 2017; 26(9):4509-4522. DOI: 10.1109/TIP.2017.2713099. View

Wu D, Kim K, El Fakhri G, Li Q . Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network. IEEE Trans Med Imaging. 2017; 36(12):2479-2486. PMC: 5897914. DOI: 10.1109/TMI.2017.2753138. View

10.

Kang E, Min J, Ye J . A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med Phys. 2017; 44(10):e360-e375. DOI: 10.1002/mp.12344. View

11.

Umehara K, Ota J, Ishida T . Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT. J Digit Imaging. 2017; 31(4):441-450. PMC: 6113156. DOI: 10.1007/s10278-017-0033-z. View

12.

Chartrand G, Cheng P, Vorontsov E, Drozdzal M, Turcotte S, Pal C . Deep Learning: A Primer for Radiologists. Radiographics. 2017; 37(7):2113-2131. DOI: 10.1148/rg.2017170077. View

13.

Du W, Chen H, Wu Z, Sun H, Liao P, Zhang Y . Stacked competitive networks for noise reduction in low-dose CT. PLoS One. 2017; 12(12):e0190069. PMC: 5739486. DOI: 10.1371/journal.pone.0190069. View

14.

Giger M . Machine Learning in Medical Imaging. J Am Coll Radiol. 2018; 15(3 Pt B):512-520. DOI: 10.1016/j.jacr.2017.12.028. View

15.

Han Y, Yoo J, Kim H, Shin H, Sung K, Ye J . Deep learning with domain adaptation for accelerated projection-reconstruction MR. Magn Reson Med. 2018; 80(3):1189-1205. DOI: 10.1002/mrm.27106. View

16.

Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O . Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018; 36(4):257-272. DOI: 10.1007/s11604-018-0726-3. View

17.

Zhu B, Liu J, Cauley S, Rosen B, Rosen M . Image reconstruction by domain-transform manifold learning. Nature. 2018; 555(7697):487-492. DOI: 10.1038/nature25988. View

18.

Xiang L, Wang Q, Nie D, Zhang L, Jin X, Qiao Y . Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image. Med Image Anal. 2018; 47:31-44. PMC: 6410565. DOI: 10.1016/j.media.2018.03.011. View

19.

Kunimatsu A, Kunimatsu N, Yasaka K, Akai H, Kamiya K, Watadani T . Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma. Magn Reson Med Sci. 2018; 18(1):44-52. PMC: 6326763. DOI: 10.2463/mrms.mp.2017-0178. View

20.

Zhang Y, Yu H . Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography. IEEE Trans Med Imaging. 2018; 37(6):1370-1381. PMC: 5998663. DOI: 10.1109/TMI.2018.2823083. View