Kadimesetty, VS and Gutta, S and Ganapathy, S and Yalavarthy, PK (2019) Convolutional neural network-based robust denoising of low-dose computed tomography perfusion maps. In: IEEE Transactions on Radiation and Plasma Medical Sciences, 3 (2). pp. 137-152.
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Abstract
The low-dose computed tomography (CT) perfusion data has low signal-to-noise ratio resulting in derived perfusion maps being noisy. These low-quality maps typically requires a denoising step to improve their utility in real-time. The existing methods, including state-of-the-art online sparse perfusion deconvolution (SPD), largely relies on the convolutional model that may not be applicable in all cases of brain perfusion. In this paper, a denoising convolutional neural network (DCNN) was proposed that relies only on computed perfusion maps for performing the denoising step. The network was trained with a large number of low-dose digital brain phantom perfusion maps to provide an approximation to the corresponding high-dose perfusion maps. The batch normalization coupled with residual learning makes the trained model invariant to the dynamic range of the input low-dose perfusion maps. The denoising of the raw-data using the convolutional neural network was also attempted here and shown to have limited applicability in the low-dose CT perfusion cases. The digital perfusion phantom as well as in-vivo results indicate that the proposed DCNN applied in the derived map domain provides superior improvement compared to the online SPD with an added advantage of being computationally efficient. © 2017 IEEE.
Item Type: | Journal Article |
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Publication: | IEEE Transactions on Radiation and Plasma Medical Sciences |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Additional Information: | The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc. |
Keywords: | Computerized tomography; Convolution; Phantoms; Signal to noise ratio, Cerebral blood flow; Computationally efficient; Convolutional model; De-noising; Low dose; Low signal-to-noise ratio; Robust denoising; State of the art, Convolutional neural networks |
Department/Centre: | Division of Electrical Sciences > Electrical Engineering Division of Interdisciplinary Sciences > Computational and Data Sciences |
Date Deposited: | 12 Dec 2022 08:59 |
Last Modified: | 12 Dec 2022 08:59 |
URI: | https://eprints.iisc.ac.in/id/eprint/78302 |
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