Implicit Regression in Subspace for High-Sensitivity CEST Imaging

Chu CHEN; Yang LIU; Se Weon PARK; Jizhou Li; Kannie W.Y. CHAN; Raymond H.F. CHAN

doi:10.1109/ISBI56570.2024.10635440

Implicit Regression in Subspace for High-Sensitivity CEST Imaging

Chu CHEN, Yang LIU, Se Weon PARK, Jizhou Li, Kannie W.Y. CHAN, Raymond H.F. CHAN^*

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

Abstract

Chemical Exchange Saturation Transfer (CEST) MRI demonstrates its capability in significantly enhancing the detection of proteins and metabolites with low concentrations through exchangeable protons. The clinical application of CEST, however, is constrained by its low contrast and low signal-to-noise ratio (SNR) in the acquired data. Denoising, as one of the post-processing stages for CEST data, can effectively improve the accuracy of CEST quantification. In this work, by modeling spatial variant z-spectrums into low-dimensional subspace, we introduce Implicit Regression in Subspace (IRIS), which is an unsupervised denoising algorithm utilizing the excellent property of implicit neural representation for continuous mapping. Experiments conducted on both synthetic and in-vivo data demonstrate that our proposed method surpasses other CEST denoising methods regarding both qualitative and quantitative performance.

Original language	English
Title of host publication	IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350313338
DOIs	https://doi.org/10.1109/ISBI56570.2024.10635440
Publication status	Published - 22 Aug 2024
Externally published	Yes
Event	21st IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Athens, Greece Duration: 27 May 2024 → 30 May 2024

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
Country/Territory	Greece
City	Athens
Period	27/05/24 → 30/05/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Funding

This work is supported by HKRGC GRFgrants CityU1101120, CityU11309922, CRFgrant C1013-21GF, and HKRGC-NSFC Grant NCityU214/19.

Keywords

CEST MRI
Denoising
Neural Network

Access to Document

10.1109/ISBI56570.2024.10635440

Cite this

CHEN, C., LIU, Y., PARK, S. W., Li, J., CHAN, K. W. Y., & CHAN, R. H. F. (2024). Implicit Regression in Subspace for High-Sensitivity CEST Imaging. In IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI56570.2024.10635440

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title = "Implicit Regression in Subspace for High-Sensitivity CEST Imaging",

abstract = "Chemical Exchange Saturation Transfer (CEST) MRI demonstrates its capability in significantly enhancing the detection of proteins and metabolites with low concentrations through exchangeable protons. The clinical application of CEST, however, is constrained by its low contrast and low signal-to-noise ratio (SNR) in the acquired data. Denoising, as one of the post-processing stages for CEST data, can effectively improve the accuracy of CEST quantification. In this work, by modeling spatial variant z-spectrums into low-dimensional subspace, we introduce Implicit Regression in Subspace (IRIS), which is an unsupervised denoising algorithm utilizing the excellent property of implicit neural representation for continuous mapping. Experiments conducted on both synthetic and in-vivo data demonstrate that our proposed method surpasses other CEST denoising methods regarding both qualitative and quantitative performance.",

keywords = "CEST MRI, Denoising, Neural Network",

author = "Chu CHEN and Yang LIU and PARK, {Se Weon} and Jizhou Li and CHAN, {Kannie W.Y.} and CHAN, {Raymond H.F.}",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024 ; Conference date: 27-05-2024 Through 30-05-2024",

year = "2024",

month = aug,

day = "22",

doi = "10.1109/ISBI56570.2024.10635440",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

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CHEN, C, LIU, Y, PARK, SW, Li, J, CHAN, KWY & CHAN, RHF 2024, Implicit Regression in Subspace for High-Sensitivity CEST Imaging. in IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings. Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, Greece, 27/05/24. https://doi.org/10.1109/ISBI56570.2024.10635440

Implicit Regression in Subspace for High-Sensitivity CEST Imaging. / CHEN, Chu; LIU, Yang; PARK, Se Weon et al.
IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings. IEEE Computer Society, 2024. (Proceedings - International Symposium on Biomedical Imaging).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - CHEN, Chu

AU - LIU, Yang

AU - PARK, Se Weon

AU - Li, Jizhou

AU - CHAN, Kannie W.Y.

AU - CHAN, Raymond H.F.

PY - 2024/8/22

Y1 - 2024/8/22

N2 - Chemical Exchange Saturation Transfer (CEST) MRI demonstrates its capability in significantly enhancing the detection of proteins and metabolites with low concentrations through exchangeable protons. The clinical application of CEST, however, is constrained by its low contrast and low signal-to-noise ratio (SNR) in the acquired data. Denoising, as one of the post-processing stages for CEST data, can effectively improve the accuracy of CEST quantification. In this work, by modeling spatial variant z-spectrums into low-dimensional subspace, we introduce Implicit Regression in Subspace (IRIS), which is an unsupervised denoising algorithm utilizing the excellent property of implicit neural representation for continuous mapping. Experiments conducted on both synthetic and in-vivo data demonstrate that our proposed method surpasses other CEST denoising methods regarding both qualitative and quantitative performance.

AB - Chemical Exchange Saturation Transfer (CEST) MRI demonstrates its capability in significantly enhancing the detection of proteins and metabolites with low concentrations through exchangeable protons. The clinical application of CEST, however, is constrained by its low contrast and low signal-to-noise ratio (SNR) in the acquired data. Denoising, as one of the post-processing stages for CEST data, can effectively improve the accuracy of CEST quantification. In this work, by modeling spatial variant z-spectrums into low-dimensional subspace, we introduce Implicit Regression in Subspace (IRIS), which is an unsupervised denoising algorithm utilizing the excellent property of implicit neural representation for continuous mapping. Experiments conducted on both synthetic and in-vivo data demonstrate that our proposed method surpasses other CEST denoising methods regarding both qualitative and quantitative performance.

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Implicit Regression in Subspace for High-Sensitivity CEST Imaging

Abstract

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Keywords

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