Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT

03/29/2022
by   Wenjun Xia, et al.
0

Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black box nature and major issues such as instabilities, which is a major barrier to apply deep learning methods in low-dose CT applications. An emerging trend is to integrate imaging physics and model into deep networks, enabling a hybridization of physics/model-based and data-driven elements. In this paper, we systematically review the physics/model-based data-driven methods for LDCT, summarize the loss functions and training strategies, evaluate the performance of different methods, and discuss relevant issues and future directions

READ FULL TEXT
research
04/02/2021

Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning

Cone Beam Computed Tomography(CBCT) is a now known method to conduct CT ...
research
10/25/2020

Self-Supervised Training For Low Dose CT Reconstruction

Ionizing radiation has been the biggest concern in CT imaging. To reduce...
research
09/23/2019

Model-Based and Data-Driven Strategies in Medical Image Computing

Model-based approaches for image reconstruction, analysis and interpreta...
research
04/21/2022

Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging

Cameras were originally designed using physics-based heuristics to captu...
research
10/24/2021

Light-Field Microscopy for optical imaging of neuronal activity: when model-based methods meet data-driven approaches

Understanding how networks of neurons process information is one of the ...
research
07/26/2022

Physics Embedded Machine Learning for Electromagnetic Data Imaging

Electromagnetic (EM) imaging is widely applied in sensing for security, ...
research
07/14/2021

Hybrid Model and Data Driven Algorithm for Online Learning of Any-to-Any Path Loss Maps

Learning any-to-any (A2A) path loss maps, where the objective is the rec...

Please sign up or login with your details

Forgot password? Click here to reset