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

Luis Zavala Mondragon; Daniel Ruijters; Peter van de Haar; Peter H.N. de With; Fons van der Sommen

doi:10.1109/MLSP49062.2020.9231535

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

Luis Zavala Mondragon, Daniel Ruijters, Peter van de Haar, Peter H.N. de With, Fons van der Sommen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (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 language	English
Title of host publication	Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	6
ISBN (Electronic)	9781728166629
DOIs	https://doi.org/10.1109/MLSP49062.2020.9231535
Publication status	Published - 21 Sep 2020
Event	30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 - Aalto University Campus, Espoo, Finland Duration: 21 Sep 2020 → 24 Sep 2020 Conference number: 30 https://ieeemlsp.cc/

Conference

Conference	30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020
Abbreviated title	MLSP 2020
Country/Territory	Finland
City	Espoo
Period	21/09/20 → 24/09/20
Internet address	https://ieeemlsp.cc/

Keywords

Computer tomography
Convolutional neural networks
Noise-reduction
Wavelets

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10.1109/MLSP49062.2020.9231535

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Zavala Mondragon, L., Ruijters, D., van de Haar, P., de With, P. H. N., & van der Sommen, F. (2020). Dual-energy CBCT pre-spectral-decomposition filtering with wavelet shrinkage networks. In Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020 [9231535] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/MLSP49062.2020.9231535

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title = "Dual-energy CBCT pre-spectral-decomposition filtering with wavelet shrinkage networks",

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",

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author = "{Zavala Mondragon}, Luis and Daniel Ruijters and {van de Haar}, Peter and {de With}, {Peter H.N.} and {van der Sommen}, Fons",

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Zavala Mondragon, L , Ruijters, D, van de Haar, P, de With, PHN & van der Sommen, F 2020, Dual-energy CBCT pre-spectral-decomposition filtering with wavelet shrinkage networks. in Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020., 9231535, Institute of Electrical and Electronics Engineers, 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020, Espoo, Finland, 21/09/20. https://doi.org/10.1109/MLSP49062.2020.9231535

Dual-energy CBCT pre-spectral-decomposition filtering with wavelet shrinkage networks. / Zavala Mondragon, Luis ; Ruijters, Daniel; van de Haar, Peter et al.

Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. Institute of Electrical and Electronics Engineers, 2020. 9231535.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Dual-energy CBCT pre-spectral-decomposition filtering with wavelet shrinkage networks

AU - Zavala Mondragon, Luis

AU - Ruijters, Daniel

AU - van de Haar, Peter

AU - de With, Peter H.N.

AU - van der Sommen, Fons

N1 - Conference code: 30

PY - 2020/9/21

Y1 - 2020/9/21

N2 - 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

AB - 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

KW - Computer tomography

KW - Convolutional neural networks

KW - Noise-reduction

KW - Wavelets

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers

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Zavala Mondragon L , Ruijters D, van de Haar P, de With PHN , van der Sommen F. Dual-energy CBCT pre-spectral-decomposition filtering with wavelet shrinkage networks. In Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. Institute of Electrical and Electronics Engineers. 2020. 9231535 doi: 10.1109/MLSP49062.2020.9231535

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

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