scholarly journals Monochromatic Image Reconstruction via Machine Learning

2021 ◽  
Author(s):  
Wenxiang Cong ◽  
Yan Xi ◽  
Bruno De Man ◽  
Ge Wang
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Monochromatic Image

scholarly journals Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

2021 ◽  
pp. 1-1
Author(s):  
Matthew J. Muckley ◽  
Bruno Riemenschneider ◽  
Alireza Radmanesh ◽  
Sunwoo Kim ◽  
Geunu Jeong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Mr Image ◽  
Mr Image Reconstruction

scholarly journals A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3701 ◽  
Author(s):  
Jin Zheng ◽  
Jinku Li ◽  
Yi Li ◽  
Lihui Peng
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Benchmark Dataset ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Image Reconstruction Algorithms ◽  
Capacitance Tomography ◽  
The Relationship

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

scholarly journals Deep Tomographic Image Reconstruction: Yesterday, Today, and Tomorrow—Editorial for the 2nd Special Issue “Machine Learning for Image Reconstruction”

2021 ◽  
Vol 40 (11) ◽  
pp. 2956-2964
Author(s):  
Ge Wang ◽  
Mathews Jacob ◽  
Xuanqin Mou ◽  
Yongyi Shi ◽  
Yonina C. Eldar
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Tomographic Image ◽  
Special Issue ◽  
Tomographic Image Reconstruction

scholarly journals fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning

2020 ◽  
Vol 2 (1) ◽  
pp. e190007 ◽  
Author(s):  
Florian Knoll ◽  
Jure Zbontar ◽  
Anuroop Sriram ◽  
Matthew J. Muckley ◽  
Mary Bruno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Mr Image ◽  
Mr Image Reconstruction

scholarly journals Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hyunkwang Lee ◽  
Chao Huang ◽  
Sehyo Yune ◽  
Shahein H. Tajmir ◽  
Myeongchan Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Image Space ◽  
Sensor Data ◽  
Reconstruction Process ◽  
Partial Representation

Abstract Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis. However, most deep learning algorithms have been developed using reconstructed, human-interpretable medical images. While image reconstruction from raw sensor data is required for the creation of medical images, the reconstruction process only uses a partial representation of all the data acquired. Here, we report the development of a system to directly process raw computed tomography (CT) data in sinogram-space, bypassing the intermediary step of image reconstruction. Two classification tasks were evaluated for their feasibility of sinogram-space machine learning: body region identification and intracranial hemorrhage (ICH) detection. Our proposed SinoNet, a convolutional neural network optimized for interpreting sinograms, performed favorably compared to conventional reconstructed image-space-based systems for both tasks, regardless of scanning geometries in terms of projections or detectors. Further, SinoNet performed significantly better when using sparsely sampled sinograms than conventional networks operating in image-space. As a result, sinogram-space algorithms could be used in field settings for triage (presence of ICH), especially where low radiation dose is desired. These findings also demonstrate another strength of deep learning where it can analyze and interpret sinograms that are virtually impossible for human experts.

Image Reconstruction is a New Frontier of Machine Learning

2018 ◽  
Vol 37 (6) ◽  
pp. 1289-1296 ◽  
Author(s):  
Ge Wang ◽  
Jong Chu Ye ◽  
Klaus Mueller ◽  
Jeffrey A. Fessler
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
New Frontier

Machine learning for image reconstruction

2020 ◽  
pp. 25-64
Author(s):  
Kerstin Hammernik ◽  
Florian Knoll
Keyword(s):  
Machine Learning ◽  
Image Reconstruction

scholarly journals Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods

2020 ◽  
Vol 76 ◽  
pp. 294-306
Author(s):  
Tonghe Wang ◽  
Yang Lei ◽  
Yabo Fu ◽  
Walter J. Curran ◽  
Tian Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Attenuation Correction ◽  
Quantitative Pet ◽  
Reconstruction Methods

scholarly journals Multiple-wavelength Color Digital Holography for Monochromatic Image Reconstruction

2015 ◽  
Vol 73 ◽  
pp. 301-307 ◽  
Author(s):  
P.A. Cheremkhin ◽  
I.A. Shevkunov ◽  
N.V. Petrov
Keyword(s):  
Image Reconstruction ◽  
Digital Holography ◽  
Monochromatic Image ◽  
Multiple Wavelength
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