Improvement of image quality at CT and MRI using deep learning

Giger ML. Machine learning in medical imaging. J Am Coll Radiol JACR. 2018. https://doi.org/10.1016/j.jacr.2017.12.028 .

Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018. https://doi.org/10.1148/radiol.2018171820 .

Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017. https://doi.org/10.1148/rg.2017160130 .

Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018. https://doi.org/10.1007/s11604-018-0726-3 .

Noguchi T, Higa D, Asada T, Kawata Y, Machitori A, Shida Y, et al. Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences. Jpn J Radiol. 2018. https://doi.org/10.1007/s11604-018-0779-3 .

Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics. 2017. https://doi.org/10.1148/rg.2017170077 .

Kunimatsu A, Kunimatsu N, Yasaka K, Akai H, Kamiya K, Watadani T, et al. 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. https://doi.org/10.2463/mrms.mp.2017-0178 .

Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. Trans Image Proc. 2017. https://doi.org/10.1109/tip.2017.2662206 .

Cavigelli L, Hager P, Benini L. CAS-CNN: a deep convolutional neural network for image compression artifact suppression. Int Jt Conf Neural Netw (IJCNN). 2017;2017:752–9.

Svoboda P, Hradis M, Barina D, Zemcik P. Compression artifacts removal using convolutional neural networks. J WSCG. 2016;24(2):63–72.

Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016. https://doi.org/10.1109/TPAMI.2015.2439281 .

Freeman WT, Jones TR, Pasztor EC. Example-based super-resolution. IEEE Comput Graph Appl. 2002. https://doi.org/10.1109/38.988747 .

Iizuka S, Simo-Serra E, Ishikawa H. Let there be color! ACM Trans Graph. 2016. https://doi.org/10.1145/2897824.2925974 .

Ulyanov D, Vedaldi A, Lempitsky V. Deep image prior. 2017. arXiv:1711.10925v2 .

Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, et al. Low-dose CT via convolutional neural network. Biomed Opt Express. 2017. https://doi.org/10.1364/boe.8.000679 .

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. https://doi.org/10.1371/journal.pone.0190069 .

Kang E, Min J, Ye JC. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med Phys. 2017. https://doi.org/10.1002/mp.12344 .

Jiang D, Dou W, Vosters L, Xu X, Sun Y, Tan T. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network. Jpn J Radiol. 2018. https://doi.org/10.1007/s11604-018-0758-8 .

Zhang Y, Yu H. Convolutional neural network based metal artifact reduction in X-ray computed tomography. IEEE Trans Med Imaging. 2018. https://doi.org/10.1109/TMI.2018.2823083 .

Kim KH, Park SH. Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI. Magn Reson Imaging. 2017. https://doi.org/10.1016/j.mri.2016.11.020 .

Hauptmann A, Arridge S, Lucka F, Muthurangu V, Steeden JA. Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magn Reson Med. 2018. https://doi.org/10.1002/mrm.27480 .

Umehara K, Ota J, Ishida T. Application of super-resolution convolutional neural network for enhancing image resolution in chest CT. J Digit Imaging. 2017. https://doi.org/10.1007/s10278-017-0033-z .

Park J, Hwang D, Kim KY, Kang SK, Kim YK, Lee JS. Computed tomography super-resolution using deep convolutional neural network. Phys Med Biol. 2018. https://doi.org/10.1088/1361-6560/aacdd4 .

Liu C, Wu X, Yu X, Tang Y, Zhang J, Zhou J. Fusing multi-scale information in convolution network for MR image super-resolution reconstruction. Biomed Eng Online. 2018. https://doi.org/10.1186/s12938-018-0546-9 .

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. https://doi.org/10.1109/tmi.2017.2753138 .

Jin KH, McCann MT, Froustey E, Unser M. Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process. 2017. https://doi.org/10.1109/tip.2017.2713099 .

Kida S, Nakamoto T, Nakano M, Nawa K, Haga A, Kotoku J, et al. Cone beam computed tomography image quality improvement using a deep convolutional neural network. Cureus. 2018. https://doi.org/10.7759/cureus.2548 .

Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 2018. https://doi.org/10.1038/nature25988 .

Han Y, Yoo J, Kim HH, Shin HJ, Sung K, Ye JC. Deep learning with domain adaptation for accelerated projection-reconstruction MR. Magn Reson Med. 2018. https://doi.org/10.1002/mrm.27106 .

Golkov V, Dosovitskiy A, Sperl JI, Menzel MI, Czisch M, Samann P, et al. q-Space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans Med Imaging. 2016. https://doi.org/10.1109/tmi.2016.2551324 .

Higaki T, Nishimaru E, Nakamura Y, Tatsugami F, Zhou J, Yu Z, et al. Radiation dose reduction in CT using deep learning based reconstruction (DLR): a phantom study. In: Proceeding of the 24th European congress of radiology. 2018. https://doi.org/10.1594/ecr2018/c-1656 .

Nakamura Y, Higaki T, Tatsugami F, Zhou J, Yu Z, Akino N, et al. Deep learning based reconstruction at CT: initial clinical trial targeting hypovascular hepatic metastases. Radiol Artif Intell. 2018. (Accepted).

Tatsugami F, Higaki T, Nakamura Y, Yu Z, Zhou J, Lu Y, et al. Improvement of image quality at coronary CT angiography by using a deep learning based reconstruction. Eur Radiol. 2018. (Accepted).

Touch M, Clark DP, Barber W, Badea CT. A neural network-based method for spectral distortion correction in photon counting X-ray CT. Phys Med Biol. 2016. https://doi.org/10.1088/0031-9155/61/16/6132 .

Xiang L, Wang Q, Nie D, Zhang L, Jin X, Qiao Y, et al. Deep embedding convolutional neural network for synthesizing CT image from T1-weighted MR image. Med Image Anal. 2018. https://doi.org/10.1016/j.media.2018.03.011 .