scholarly journals Monochromatic Image Reconstruction via Machine Learning

Author(s):  
Wenxiang Cong ◽  
Yan Xi ◽  
Bruno De Man ◽  
Ge Wang

Author(s):  
Matthew J. Muckley ◽  
Bruno Riemenschneider ◽  
Alireza Radmanesh ◽  
Sunwoo Kim ◽  
Geunu Jeong ◽  
...  


Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3701 ◽  
Author(s):  
Jin Zheng ◽  
Jinku Li ◽  
Yi Li ◽  
Lihui Peng

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.





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


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hyunkwang Lee ◽  
Chao Huang ◽  
Sehyo Yune ◽  
Shahein H. Tajmir ◽  
Myeongchan Kim ◽  
...  

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.



2018 ◽  
Vol 37 (6) ◽  
pp. 1289-1296 ◽  
Author(s):  
Ge Wang ◽  
Jong Chu Ye ◽  
Klaus Mueller ◽  
Jeffrey A. Fessler




2020 ◽  
Vol 76 ◽  
pp. 294-306
Author(s):  
Tonghe Wang ◽  
Yang Lei ◽  
Yabo Fu ◽  
Walter J. Curran ◽  
Tian Liu ◽  
...  


2015 ◽  
Vol 73 ◽  
pp. 301-307 ◽  
Author(s):  
P.A. Cheremkhin ◽  
I.A. Shevkunov ◽  
N.V. Petrov


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