Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis

11/19/2016
by   Yo Seob Han, et al.
0

Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient. However, due to the insufficient number of projection views, an analytic reconstruction approach results in severe streaking artifacts and CS-based iterative approach is computationally very expensive. To address this issue, here we propose a novel deep residual learning approach for sparse view CT reconstruction. Specifically, based on a novel persistent homology analysis showing that the manifold of streaking artifacts is topologically simpler than original ones, a deep residual learning architecture that estimates the streaking artifacts is developed. Once a streaking artifact image is estimated, an artifact-free image can be obtained by subtracting the streaking artifacts from the input image. Using extensive experiments with real patient data set, we confirm that the proposed residual learning provides significantly better image reconstruction performance with several orders of magnitude faster computational speed.

READ FULL TEXT

page 2

page 3

page 4

page 5

page 6

page 8

page 9

research
01/04/2018

Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner

For homeland and transportation security applications, 2D X-ray explosiv...
research
04/02/2018

Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks

Accelerated magnetic resonance (MR) scan acquisition with compressed sen...
research
08/04/2020

Stabilizing Deep Tomographic Reconstruction Networks

While the field of deep tomographic reconstruction has been advancing ra...
research
12/13/2020

LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT

Compressed sensing (CS) computed tomography has been proven to be import...
research
06/02/2017

Image Restoration from Patch-based Compressed Sensing Measurement

A series of methods have been proposed to reconstruct an image from comp...
research
03/10/2020

Superiorized method for metal artifact reduction

Metal artifact reduction (MAR) is a challenging problem in computed tomo...
research
03/20/2019

Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

Over the past few years, dictionary learning (DL)-based methods have bee...

Please sign up or login with your details

Forgot password? Click here to reset