Access the full text.
Sign up today, get DeepDyve free for 14 days.
K. Simonyan, Andrew Zisserman (2014)
Very Deep Convolutional Networks for Large-Scale Image RecognitionCoRR, abs/1409.1556
O. Ronneberger, P. Fischer, T. Brox (2015)
U-Net: Convolutional Networks for Biomedical Image SegmentationArXiv, abs/1505.04597
C. Feng, Zhiqiang Chen, Kejun Kang, Yuxiang Xing (2019)
Synthesize monochromatic images in spectral CT by dual-domain deep learning15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
C. Frellesen, Freia Fessler, A. Hardie, Julian Wichmann, Julian Wichmann, C. Cecco, C. Cecco, U. Schoepf, J. Kerl, B. Schulz, R. Hammerstingl, T. Vogl, R. Bauer (2015)
Dual-energy CT of the pancreas: improved carcinoma-to-pancreas contrast with a noise-optimized monoenergetic reconstruction algorithm.European journal of radiology, 84 11
A. Srinivasan, E. Hoeffner, M. Ibrahim, G. Shah, F. Lamarca, S. Mukherji (2013)
Utility of dual-energy CT virtual keV monochromatic series for the assessment of spinal transpedicular hardware-bone interface.AJR. American journal of roentgenology, 201 4
T. D'angelo, G. Cicero, S. Mazziotti, G. Ascenti, M. Albrecht, Simon Martin, A. Othman, T. Vogl, J. Wichmann (2019)
Dual energy computed tomography virtual monoenergetic imaging: technique and clinical applications.The British journal of radiology, 92 1098
(2013)
image quality, enhancement, diagnosis and radiation dose,” Eur
Yoshitake Yamada, M. Jinzaki, Y. Tanami, T. Abe, S. Kuribayashi (2012)
Virtual Monochromatic Spectral Imaging for the Evaluation of Hypovascular Hepatic Metastases: The Optimal Monochromatic Level With Fast Kilovoltage Switching Dual-Energy Computed TomographyInvestigative Radiology, 47
H. Gong, S. Tao, K. Rajendran, Wei Zhou, C. McCollough, S. Leng (2020)
Deep-learning-based direct inversion for material decomposition.Medical physics
G. Hanson, G. Michalak, Robert Childs, Brian McCollough, Anil Kurup, David Hough, Judson Frye, J. Fidler, Sudhakar Venkatesh, S. Leng, Lifeng Yu, A. Halaweish, W. Harmsen, C. McCollough, J. Fletcher (2018)
Low kV versus dual-energy virtual monoenergetic CT imaging for proven liver lesions: what are the advantages and trade-offs in conspicuity and image quality? A pilot studyAbdominal Radiology, 43
S. Tao, K. Rajendran, Wei Zhou, J. Fletcher, C. McCollough, S. Leng (2019)
Improving iodine contrast to noise ratio using virtual monoenergetic imaging and prior-knowledge-aware iterative denoising (mono-PKAID)Physics in Medicine & Biology, 64
Lifeng Yu, S. Leng, C. McCollough (2012)
Dual-energy CT-based monochromatic imaging.AJR. American journal of roentgenology, 199 5 Suppl
C. McCollough, S. Leng, Lifeng Yu, J. Fletcher (2015)
Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications.Radiology, 276 3
S. Lam, R. Gupta, M. Levental, E. Yu, H. Curtin, R. Forghani (2015)
Optimal Virtual Monochromatic Images for Evaluation of Normal Tissues and Head and Neck Cancer Using Dual-Energy CTAmerican Journal of Neuroradiology, 36
Jia Deng (2009)
A large-scale hierarchical image database
(2018)
what are the advantages and trade-offs in conspicuity and image quality? A pilot study,” Abdom
J. Ye, Yoseob Han, E. Cha (2017)
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse ProblemsSIAM J. Imaging Sci., 11
Augustus Odena, Vincent Dumoulin, C. Olah (2016)
Deconvolution and Checkerboard Artifacts, 1
(2017)
quantitative accuracy in derived image sets,” Phys
Guang-Hong Chen, Jie Tang, S. Leng (2008)
Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets.Medical physics, 35 2
Wei Zhao, Tianling Lyu, Yang Chen, L. Xing (2020)
A deep learning approach for virtual monochromatic spectral CT imaging with a standard single energy CT scannerarXiv: Medical Physics
(2019)
Image-domain synthesis of spectral CT virtual monoenergetic images using stacked deep convolutional neural networks,
Olivier Hénaff, Eero Simoncelli (2015)
Geodesics of learned representationsCoRR, abs/1511.06394
R. Yuan, W. Shuman, J. Earls, C. Hague, H. Mumtaz, Andrew Scott-Moncrieff, J. Ellis, J. Mayo, J. Leipsic (2012)
Reduced iodine load at CT pulmonary angiography with dual-energy monochromatic imaging: comparison with standard CT pulmonary angiography--a prospective randomized trial.Radiology, 262 1
Matthew Zeiler, R. Fergus (2013)
Visualizing and Understanding Convolutional NetworksArXiv, abs/1311.2901
Da Zhang, Xinhua Li, Bob Liu (2011)
Objective characterization of GE discovery CT750 HD scanner: gemstone spectral imaging mode.Medical physics, 38 3
(2019)
technique and clinical applications,” Br
Andrew Maas (2013)
Rectifier Nonlinearities Improve Neural Network Acoustic Models
(2012)
a systematically optimized protocol,” Invest
Abien Agarap (2018)
Deep Learning using Rectified Linear Units (ReLU)ArXiv, abs/1803.08375
(2011)
gemstone spectral imaging mode,” Med
Z. Yu, S. Leng, S. Jorgensen, Zhoubo Li, R. Gutjahr, Baiyu Chen, A. Halaweish, S. Kappler, Lifeng Yu, E. Ritman, C. McCollough (2016)
Evaluation of conventional imaging performance in a research whole-body CT system with a photon-counting detector arrayPhysics in Medicine & Biology, 61
J. Stehli, T. Fuchs, A. Singer, S. Bull, O. Clerc, M. Possner, O. Gaemperli, R. Buechel, P. Kaufmann (2015)
First experience with single-source, dual-energy CCTA for monochromatic stent imaging.European heart journal cardiovascular Imaging, 16 5
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander Alemi (2016)
Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningArXiv, abs/1602.07261
D. Leithner, J. Wichmann, T. Vogl, J. Trommer, Simon Martin, J. Scholtz, B. Bodelle, C. Cecco, Taylor Duguay, John Nance, U. Schoepf, M. Albrecht (2017)
Virtual Monoenergetic Imaging and Iodine Perfusion Maps Improve Diagnostic Accuracy of Dual-Energy Computed Tomography Pulmonary Angiography With Suboptimal Contrast AttenuationInvestigative Radiology, 52
S. Leng, Lifeng Yu, J. Fletcher, C. McCollough (2015)
Maximizing Iodine Contrast-to-Noise Ratios in Abdominal CT Imaging through Use of Energy Domain Noise Reduction and Virtual Monoenergetic Dual-Energy CT.Radiology, 276 2
(2012)
the optimal monochromatic level with fast kilovoltage switching dual-energy computed tomography,” Invest
Diederik Kingma, Jimmy Ba (2014)
Adam: A Method for Stochastic OptimizationCoRR, abs/1412.6980
G. Michalak, J. Grimes, J. Fletcher, A. Halaweish, Lifeng Yu, S. Leng, C. McCollough (2016)
Technical Note: Improved CT number stability across patient size using dual-energy CT virtual monoenergetic imaging.Medical physics, 43 1
S. Pomerantz, S. Kamalian, Da Zhang, Rajiv Gupta, O. Rapalino, D. Sahani, M. Lev (2013)
Virtual monochromatic reconstruction of dual-energy unenhanced head CT at 65-75 keV maximizes image quality compared with conventional polychromatic CT.Radiology, 266 1
S. Leng, Lifeng Yu, Jia Wang, J. Fletcher, C. Mistretta, C. McCollough (2011)
Noise reduction in spectral CT: reducing dose and breaking the trade-off between image noise and energy bin selection.Medical physics, 38 9
Ludovic Trottier, P. Giguère, B. Chaib-draa (2016)
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)
S. Leng, Wei Zhou, Z. Yu, A. Halaweish, B. Krauss, B. Schmidt, Lifeng Yu, S. Kappler, C. McCollough (2017)
Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image setsPhysics in Medicine & Biology, 62
K. Grant, T. Flohr, B. Krauss, M. Sedlmair, Christoph Thomas, B. Schmidt (2014)
Assessment of an Advanced Image-Based Technique to Calculate Virtual Monoenergetic Computed Tomographic Images From a Dual-Energy Examination to Improve Contrast-To-Noise Ratio in Examinations Using Iodinated Contrast MediaInvestigative Radiology, 49
F. Meinel, B. Bischoff, Qiaowei Zhang, F. Bamberg, M. Reiser, T. Johnson (2012)
Metal Artifact Reduction by Dual-Energy Computed Tomography Using Energetic Extrapolation: A Systematically Optimized ProtocolInvestigative Radiology, 47
Leon Gatys, Alexander Ecker, M. Bethge (2015)
Texture Synthesis Using Convolutional Neural Networks
K. Kalisz, N. Rassouli, A. Dhanantwari, D. Jordan, P. Rajiah (2018)
Noise characteristics of virtual monoenergetic images from a novel detector-based spectral CT scanner.European journal of radiology, 98
Moritz Albrecht, Moritz Albrecht, T. Vogl, Simon Martin, Simon Martin, John Nance, Taylor Duguay, Julian Wichmann, Julian Wichmann, C. Cecco, C. Cecco, A. Varga-Szemes, M. Assen, C. Tesche, U. Schoepf (2019)
Review of Clinical Applications for Virtual Monoenergetic Dual-Energy CT.Radiology
F. Secchi, C. Cecco, J. Spearman, J. Silverman, U. Ebersberger, F. Sardanelli, U. Schoepf (2015)
Monoenergetic extrapolation of cardiac dual energy CT for artifact reductionActa Radiologica, 56
Jia Deng, Wei Dong, R. Socher, Li-Jia Li, K. Li, Li Fei-Fei (2009)
ImageNet: A large-scale hierarchical image database2009 IEEE Conference on Computer Vision and Pattern Recognition
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Z. Wojna (2015)
Rethinking the Inception Architecture for Computer Vision2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
P. Carrascosa, J. Leipsic, C. Capunay, A. Deviggiano, J. Vallejos, Alejandro Goldsmit, G. Rodriguez-Granillo (2015)
Monochromatic image reconstruction by dual energy imaging allows half iodine load computed tomography coronary angiography.European journal of radiology, 84 10
Daniella Pinho, Naveen Kulkarni, A. Krishnaraj, S. Kalva, D. Sahani (2013)
Initial experience with single-source dual-energy CT abdominal angiography and comparison with single-energy CT angiography: image quality, enhancement, diagnosis and radiation doseEuropean Radiology, 23
C. Feng, Kejun Kang, Yuxiang Xing (2018)
Fully connected neural network for virtual monochromatic imaging in spectral computed tomographyJournal of Medical Imaging, 6
Yanye Lu, M. Kowarschik, Xiaolin Huang, Yan Xia, Jang-Hwan Choi, Shuqing Chen, Shiyang Hu, Q. Ren, R. Fahrig, J. Hornegger, A. Maier (2018)
A learning‐based material decomposition pipeline for multi‐energy x‐ray imagingMedical Physics, 46
S. Leng, M. Bruesewitz, S. Tao, K. Rajendran, A. Halaweish, N. Campeau, J. Fletcher, C. McCollough (2019)
Photon-counting Detector CT: System Design and Clinical Applications of an Emerging Technology.Radiographics : a review publication of the Radiological Society of North America, Inc, 39 3
(2018)
Convolutional neural network based material decomposition with a photoncounting-detector computed tomography system,
Abstract.Purpose: We developed a deep learning method to reduce noise and beam-hardening artifact in virtual monoenergetic image (VMI) at low x-ray energy levels.Approach: An encoder–decoder type convolutional neural network was implemented with customized inception modules and in-house-designed training loss (denoted as Incept-net), to directly estimate VMI from multi-energy CT images. Images of an abdomen-sized water phantom with varying insert materials were acquired from a research photon-counting-detector CT. The Incept-net was trained with image patches (64 × 64 pixels) extracted from the phantom data, as well as synthesized, random-shaped numerical insert materials. The whole CT images (512 × 512 pixels) with the remaining real insert materials that were unseen in network training were used for testing. Seven contrast-enhanced abdominal CT exams were used for preliminary evaluation of Incept-net generalizability over anatomical background. Mean absolute percentage error (MAPE) was used to evaluate CT number accuracy.Results: Compared to commercial VMI software, Incept-net largely suppressed beam-hardening artifact and reduced noise (53%) in phantom study. Incept-net presented comparable CT number accuracy at higher-density (P-value [0.0625, 0.999]) and improved it at lower-density inserts (P-value = 0.0313) with overall MAPE: Incept-net [2.9%, 4.6%]; commercial-VMI [6.7%, 10.9%]. In patient images, Incept-net suppressed beam-hardening artifact and reduced noise (up to 50%, P-value = 0.0156).Conclusion: In this preliminary study, Incept-net presented the potential to improve low-energy VMI quality.
Journal of Medical Imaging – SPIE
Published: Sep 1, 2021
Keywords: virtual monoenergetic image; dual-energy CT; deep learning; convolutional neural network; photon counting detector; noise reduction; artifact reduction
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.