.png){kind=icon}
返回
A Model-Based Iterative Learning Approach for Diffuse Optical Tomography
.png){kind=icon}
OA
AI
DOI：10.1109/TMI.2021.3136461.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a 'model-based' learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration.
Keyword:
US Department of Transportation
Mathematical models
Image reconstruction
Inverse problems
Absorption
Tomography
Scattering
Deep learning
convolutional neural networks
diffuse optical tomography
absolute imaging
期刊
IF:
9.8
论文数:
6.3K
被引数:
3.7W
机构
引用论文
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction基于方向小波的深度卷积神经网络用于低剂量x射线CT重建.png)
MEDICAL PHYSICS
OAAI
MEDICAL PHYSICSIF3.2
OAAI分享收藏Compensation for geometric mismodelling by anisotropies in optical tomographyOPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏Regeneration of Military Sites as a Factor in the Improvement of the Town's Logistics InfrastructureOAAI
OAAI分享收藏
查看更多内容