Dual-energy CBCT pre-spectral-decomposition filtering with wavelet shrinkage networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

Convolutional Neural Networks (CNNs) are reshaping sig-nal processing and computer vision by providing data-drivensolutions for inverse problems such as noise reduction. How-ever, their relationship with established signal processingmethods is sometimes unclear and its development not fullyexploiting the existing knowledge. In this paper, rather thanimproving existing CNNs with wavelet transformations asexplored earlier, we improve the wavelet shrinkage approachto noise-reduction with a data-driven solution. The resultingCNN has clear encoding, decoding and processing paths.As application, we perform noise reduction in Dual-EnergyCone-Beam CT. The obtained results were compared to aUNet-like architecture, which reveal better noise-free imageswithout aliasing artifacts. This indicates that that our archi-tecture is able to preserve well the information contained inthe images because the architecture exploits explicitly theunderlying signal representation
Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781728166629
DOIs
Publication statusPublished - 21 Sep 2020
Event30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 - Aalto University Campus, Espoo, Finland
Duration: 21 Sep 202024 Sep 2020
Conference number: 30
https://ieeemlsp.cc/

Conference

Conference30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020
Abbreviated titleMLSP 2020
Country/TerritoryFinland
CityEspoo
Period21/09/2024/09/20
Internet address

Keywords

  • Computer tomography
  • Convolutional neural networks
  • Noise-reduction
  • Wavelets

Fingerprint

Dive into the research topics of 'Dual-energy CBCT pre-spectral-decomposition filtering with wavelet shrinkage networks'. Together they form a unique fingerprint.

Cite this