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Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach

Abstract: 

In medical practice, the X-ray Computed tomography-based scans expose a high radiation dose and lead to the risk of prostate or abdomen cancers. On the other hand, the low-dose CT scan can reduce radiation exposure to the patient. But the reduced radiation dose degrades image quality for human perception, and adversely affects the radiologist’s diagnosis and prognosis. In this paper, we introduce a GAN based auto-encoder network to de-noise the CT images. Our network first maps CT images to low dimensional manifolds and then restore the images from its corresponding manifold representations. Our reconstruction algorithm separately calculates perceptual similarity, learns the latent feature maps, and achieves more accurate and visually pleasing reconstructions. We also showed the effectiveness of our model on a number of patient abdomen CT images, and compare our results with existing deep learning and iterative reconstruction methods. Experimental results demonstrate that our model outperforms other state-of-the-art methods in terms of PSNR, SSIM, and statistical properties of the image regions.

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Paper Details

Authors:
Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta
Submitted On:
24 September 2019 - 10:23am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Shiba Kuanar
Paper Code:
1720
Document Year:
2019
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Low Dose Image Reconstruction

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[1] Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta, "Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4585. Accessed: Dec. 13, 2019.
@article{4585-19,
url = {http://sigport.org/4585},
author = {Shiba Kuanar; Vassilis Athitsos; Dwarikanath Mahapatra; K.R. Rao; Zahid Akhtar; Dipankar Dasgupta },
publisher = {IEEE SigPort},
title = {Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach},
year = {2019} }
TY - EJOUR
T1 - Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach
AU - Shiba Kuanar; Vassilis Athitsos; Dwarikanath Mahapatra; K.R. Rao; Zahid Akhtar; Dipankar Dasgupta
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4585
ER -
Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta. (2019). Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach. IEEE SigPort. http://sigport.org/4585
Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta, 2019. Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach. Available at: http://sigport.org/4585.
Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta. (2019). "Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach." Web.
1. Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, K.R. Rao, Zahid Akhtar, Dipankar Dasgupta. Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning based Approach [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4585