Deep-Learning Driven Noise Reduction for Reduced Flux Computed Tomography

01/18/2021
by   Khalid L. Alsamadony, et al.
10

Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose images to higher-dose, higher-quality images thereby minimizing the associated radiation hazard. Conversely, computed tomography (CT) measurements of geomaterials are not limited by the radiation dose. In contrast to the human body, however, geomaterials may be comprised of high-density constituents causing increased attenuation of the X-Rays. Consequently, higher dosage images are required to obtain an acceptable scan quality. The problem of prolonged acquisition times is particularly severe for micro-CT based scanning technologies. Depending on the sample size and exposure time settings, a single scan may require several hours to complete. This is of particular concern if phenomena with an exponential temperature dependency are to be elucidated. A process may happen too fast to be adequately captured by CT scanning. To address the aforementioned issues, we apply DCNNs to improve the quality of rock CT images and reduce exposure times by more than 60%, simultaneously. We highlight current results based on micro-CT derived datasets and apply transfer learning to improve DCNN results without increasing training time. The approach is applicable to any computed tomography technology. Furthermore, we contrast the performance of the DCNN trained by minimizing different loss functions such as mean squared error and structural similarity index.

READ FULL TEXT

page 9

page 11

page 12

page 15

page 16

research
06/26/2020

Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose CT Denoising

Low Dose CT Denoising research aims to reduce the risks of radiation exp...
research
05/02/2018

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

Computed tomography (CT) is a popular medical imaging modality in clinic...
research
02/08/2022

Metal Artifact Reduction with Intra-Oral Scan Data for 3D Low Dose Maxillofacial CBCT Modeling

Low-dose dental cone beam computed tomography (CBCT) has been increasing...
research
11/04/2020

Noise Reduction to Compute Tissue Mineral Density and Trabecular Bone Volume Fraction from Low Resolution QCT

We propose a 3D neural network with specific loss functions for quantita...
research
10/01/2019

The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods

Deep Learning approaches for solving Inverse Problems in imaging have be...
research
06/03/2021

Fast improvement of TEM image with low-dose electrons by deep learning

Low-electron-dose observation is indispensable for observing various sam...

Please sign up or login with your details

Forgot password? Click here to reset