Adaptively Re-weighting Multi-Loss Untrained Transformer for Sparse-View Cone-Beam CT Reconstruction

03/23/2022
by   Minghui Wu, et al.
0

Cone-Beam Computed Tomography (CBCT) has been proven useful in diagnosis, but how to shorten scanning time with lower radiation dosage and how to efficiently reconstruct 3D image remain as the main issues for clinical practice. The recent development of tomographic image reconstruction on sparse-view measurements employs deep neural networks in a supervised way to tackle such issues, whereas the success of model training requires quantity and quality of the given paired measurements/images. We propose a novel untrained Transformer to fit the CBCT inverse solver without training data. It is mainly comprised of an untrained 3D Transformer of billions of network weights and a multi-level loss function with variable weights. Unlike conventional deep neural networks (DNNs), there is no requirement of training steps in our approach. Upon observing the hardship of optimising Transformer, the variable weights within the loss function are designed to automatically update together with the iteration process, ultimately stabilising its optimisation. We evaluate the proposed approach on two publicly available datasets: SPARE and Walnut. The results show a significant performance improvement on image quality metrics with streak artefact reduction in the visualisation. We also provide a clinical report by an experienced radiologist to assess our reconstructed images in a diagnosis point of view. The source code and the optimised models are available from the corresponding author on request at the moment.

READ FULL TEXT

page 7

page 12

research
11/21/2021

DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction

While Computed Tomography (CT) reconstruction from X-ray sinograms is ne...
research
11/28/2021

MIST-net: Multi-domain Integrative Swin Transformer network for Sparse-View CT Reconstruction

The deep learning-based tomographic image reconstruction methods have be...
research
02/19/2022

A Lightweight Dual-Domain Attention Framework for Sparse-View CT Reconstruction

Computed Tomography (CT) plays an essential role in clinical diagnosis. ...
research
01/17/2019

Cone-beam CT to Planning CT synthesis using generative adversarial networks

Cone-beam computed tomography (CBCT) offers advantages over conventional...
research
07/11/2023

Image Reconstruction using Enhanced Vision Transformer

Removing noise from images is a challenging and fundamental problem in t...
research
07/12/2023

FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction

Sparse-view computed tomography (CT) is a promising solution for expedit...

Please sign up or login with your details

Forgot password? Click here to reset