scholarly journals Model-based system matrix for iterative reconstruction in sub-diffuse angular-domain fluorescence optical projection tomography

2021 ◽  
Vol 12 (3) ◽  
pp. 1248
Author(s):  
Veronica C. Torres ◽  
Chengyue Li ◽  
Jovan G. Brankov ◽  
Kenneth M. Tichauer
2010 ◽  
Vol 18 (19) ◽  
pp. 19444 ◽  
Author(s):  
Eldon Ng ◽  
Fartash Vasefi ◽  
Bozena Kaminska ◽  
Glenn H. Chapman ◽  
Jeffrey J.L. Carson

2020 ◽  
Vol 60 (1) ◽  
pp. 135
Author(s):  
Veronica C. Torres ◽  
Chengyue Li ◽  
Wei Zhou ◽  
Jovan G. Brankov ◽  
Kenneth M. Tichauer

2009 ◽  
Author(s):  
Fartash Vasefi ◽  
Bozena Kaminska ◽  
Kevin Jordan ◽  
Glenn H. Chapman ◽  
Jeffrey J. L. Carson

2011 ◽  
Author(s):  
Eldon Ng ◽  
Fartash Vasefi ◽  
Michael Roumeliotis ◽  
Bozena Kaminska ◽  
Jeffrey J. L. Carson

2009 ◽  
Vol 48 (33) ◽  
pp. 6448 ◽  
Author(s):  
Fartash Vasefi ◽  
Eldon Ng ◽  
Bozena Kaminska ◽  
Glenn H. Chapman ◽  
Kevin Jordan ◽  
...  

2019 ◽  
Vol 10 (2) ◽  
pp. 747 ◽  
Author(s):  
L. Sinha ◽  
F. Massanes ◽  
V. C. Torres ◽  
C. Li ◽  
K. M. Tichauer ◽  
...  

Author(s):  
Luuk J. Oostveen ◽  
Frederick J. A. Meijer ◽  
Frank de Lange ◽  
Ewoud J. Smit ◽  
Sjoert A. Pegge ◽  
...  

Abstract Objectives To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). Methods Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. Results For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. Conclusions With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. Key Points • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.


Sign in / Sign up

Export Citation Format

Share Document