DeepAI AI Chat
Log In Sign Up

BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization

08/04/2019
by   Il Yong Chun, et al.
Shanghai Jiao Tong University
0

Obtaining accurate and reliable images from low-dose computed tomography (CT) is challenging. Regression convolutional neural network (CNN) models that are learned from training data are increasingly gaining attention in low-dose CT reconstruction. This paper modifies the architecture of an iterative regression CNN, BCD-Net, for fast, stable, and accurate low-dose CT reconstruction, and presents the convergence property of the modified BCD-Net. Numerical results with phantom data show that applying faster numerical solvers to model-based image reconstruction (MBIR) modules of BCD-Net leads to faster and more accurate BCD-Net; BCD-Net significantly improves the reconstruction accuracy, compared to the state-of-the-art MBIR method using learned transforms; BCD-Net achieves better image quality, compared to a state-of-the-art iterative NN architecture, ADMM-Net. Numerical results with clinical data show that BCD-Net generalizes significantly better than a state-of-the-art deep (non-iterative) regression NN, FBPConvNet, that lacks MBIR modules.

READ FULL TEXT

page 8

page 11

page 12

page 13

02/27/2020

Momentum-Net for Low-Dose CT Image Reconstruction

This paper applies the recent fast iterative neural network framework, M...
12/02/2020

An Improved Iterative Neural Network for High-Quality Image-Domain Material Decomposition in Dual-Energy CT

Dual-energy computed tomography (DECT) has been widely used in many appl...
04/27/2021

Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction

We propose a provably convergent method, called Efficient Learned Descen...
06/05/2019

Improved low-count quantitative PET reconstruction with a variational neural network

Image reconstruction in low-count PET is particularly challenging becaus...
02/15/2018

Convolutional Analysis Operator Learning: Acceleration, Convergence, Application, and Neural Networks

Convolutional operator learning is increasingly gaining attention in man...
07/26/2019

Momentum-Net: Fast and convergent iterative neural network for inverse problems

Iterative neural networks (INN) are rapidly gaining attention for solvin...
03/19/2021

Learning the Superpixel in a Non-iterative and Lifelong Manner

Superpixel is generated by automatically clustering pixels in an image i...