ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Convolutional neural network-based robust denoising of low-dose computed tomography perfusion maps

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.

[img] PDF
IEEE_tra_rad_3-2_137-152_2019.pdf - Published Version
Restricted to Registered users only

Download (2MB) | Request a copy
Official URL: https://doi.org/10.1109/TRPMS.2018.2860788

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
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

Actions (login required)

View Item View Item