scholarly journals Performance of clinically available deep learning image reconstruction in computed tomography: a phantom study

2020 ◽  
Vol 7 (06) ◽  
Author(s):  
Hiroki Kawashima ◽  
Katsuhiro Ichikawa ◽  
Tadanori Takata ◽  
Wataru Mitsui ◽  
Hiroshi Ueta ◽  
...  
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.


PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0226963
Author(s):  
Jiaxi Wang ◽  
Jun Liang ◽  
Jingye Cheng ◽  
Yumeng Guo ◽  
Li Zeng

2021 ◽  
Vol 22 (1) ◽  
pp. 131 ◽  
Author(s):  
Joo Hee Kim ◽  
Hyun Jung Yoon ◽  
Eunju Lee ◽  
Injoong Kim ◽  
Yoon Ki Cha ◽  
...  

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