A user independent denoising method for x-nuclei MRI and MRS

Nichlas Vous Christensen; Michael Vaeggemose; Nikolaj Bøgh; Esben S S Hansen; Jonas L Olesen; Yaewon Kim; Daniel B Vigneron; Jeremy W Gordon; Sune N Jespersen; Christoffer Laustsen

doi:10.1002/mrm.29817

A user independent denoising method for x-nuclei MRI and MRS

Nichlas Vous Christensen^*, Michael Vaeggemose, Nikolaj Bøgh, Esben S S Hansen, Jonas L Olesen, Yaewon Kim, Daniel B Vigneron, Jeremy W Gordon, Sune N Jespersen, Christoffer Laustsen

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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Abstract

PURPOSE: X-nuclei (also called non-proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x-nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state-of-the-art denoising method.

METHODS: Denoising approaches were compared on human acquisitions of sodium ( 23 Na) brain, deuterium ( 2 H) brain, carbon ( 13 C) heart and brain, and simulated dynamic hyperpolarized 13 C brain scans, with and without additional noise. The current state-of-the-art denoising method Global-local higher order singular value decomposition (GL-HOSVD) was compared to the few-input method tensor Marchenko-Pastur principal component analysis (tMPPCA). Noise-removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean-square-error and Bland-Altman analysis to quantify agreement between original and denoised results of noise-added data.

RESULTS: GL-HOSVD and tMPPCA showed similar performance for the variety of x-nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL-HOSVD. The mean ratio between noise-added and denoising reproducibility coefficients of the Bland-Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL-HOSVD and tMPPCA, respectively.

CONCLUSION: The strength of tMPPCA lies in the few-input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x-nuclei imaging improvements and the preferred denoising method.

Original language	English
Journal	Magnetic Resonance in Medicine
Volume	90
Issue	6
Pages (from-to)	2539-2556
Number of pages	18
ISSN	0740-3194
DOIs	https://doi.org/10.1002/mrm.29817
Publication status	Published - Dec 2023

Keywords

Denoising
deuterium
hyperpolarized carbon-13
MRI
sodium

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Magnetic Resonance in Med - 2023 - Christensen - A user independent denoising method for x‐nuclei MRI and MRSFinal published version, 9.37 MBLicence: CC BY

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title = "A user independent denoising method for x-nuclei MRI and MRS",

abstract = "PURPOSE: X-nuclei (also called non-proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x-nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state-of-the-art denoising method.METHODS: Denoising approaches were compared on human acquisitions of sodium ( 23 Na) brain, deuterium ( 2 H) brain, carbon ( 13 C) heart and brain, and simulated dynamic hyperpolarized 13 C brain scans, with and without additional noise. The current state-of-the-art denoising method Global-local higher order singular value decomposition (GL-HOSVD) was compared to the few-input method tensor Marchenko-Pastur principal component analysis (tMPPCA). Noise-removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean-square-error and Bland-Altman analysis to quantify agreement between original and denoised results of noise-added data. RESULTS: GL-HOSVD and tMPPCA showed similar performance for the variety of x-nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL-HOSVD. The mean ratio between noise-added and denoising reproducibility coefficients of the Bland-Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL-HOSVD and tMPPCA, respectively.CONCLUSION: The strength of tMPPCA lies in the few-input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x-nuclei imaging improvements and the preferred denoising method.",

keywords = "Deuterium, Hyperpolarisation, MRI, carbon-13, Denoising, deuterium, hyperpolarized carbon-13, MRI, sodium",

author = "Christensen, {Nichlas Vous} and Michael Vaeggemose and Nikolaj B{\o}gh and Hansen, {Esben S S} and Olesen, {Jonas L} and Yaewon Kim and Vigneron, {Daniel B} and Gordon, {Jeremy W} and Jespersen, {Sune N} and Christoffer Laustsen",

year = "2023",

month = dec,

doi = "10.1002/mrm.29817",

language = "English",

volume = "90",

pages = "2539--2556",

journal = "Magnetic Resonance in Medicine",

issn = "0740-3194",

publisher = "John Wiley & Sons Inc.",

number = "6",

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T1 - A user independent denoising method for x-nuclei MRI and MRS

AU - Christensen, Nichlas Vous

AU - Vaeggemose, Michael

AU - Bøgh, Nikolaj

AU - Hansen, Esben S S

AU - Olesen, Jonas L

AU - Kim, Yaewon

AU - Vigneron, Daniel B

AU - Gordon, Jeremy W

AU - Jespersen, Sune N

AU - Laustsen, Christoffer

PY - 2023/12

Y1 - 2023/12

N2 - PURPOSE: X-nuclei (also called non-proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x-nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state-of-the-art denoising method.METHODS: Denoising approaches were compared on human acquisitions of sodium ( 23 Na) brain, deuterium ( 2 H) brain, carbon ( 13 C) heart and brain, and simulated dynamic hyperpolarized 13 C brain scans, with and without additional noise. The current state-of-the-art denoising method Global-local higher order singular value decomposition (GL-HOSVD) was compared to the few-input method tensor Marchenko-Pastur principal component analysis (tMPPCA). Noise-removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean-square-error and Bland-Altman analysis to quantify agreement between original and denoised results of noise-added data. RESULTS: GL-HOSVD and tMPPCA showed similar performance for the variety of x-nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL-HOSVD. The mean ratio between noise-added and denoising reproducibility coefficients of the Bland-Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL-HOSVD and tMPPCA, respectively.CONCLUSION: The strength of tMPPCA lies in the few-input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x-nuclei imaging improvements and the preferred denoising method.

AB - PURPOSE: X-nuclei (also called non-proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x-nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state-of-the-art denoising method.METHODS: Denoising approaches were compared on human acquisitions of sodium ( 23 Na) brain, deuterium ( 2 H) brain, carbon ( 13 C) heart and brain, and simulated dynamic hyperpolarized 13 C brain scans, with and without additional noise. The current state-of-the-art denoising method Global-local higher order singular value decomposition (GL-HOSVD) was compared to the few-input method tensor Marchenko-Pastur principal component analysis (tMPPCA). Noise-removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean-square-error and Bland-Altman analysis to quantify agreement between original and denoised results of noise-added data. RESULTS: GL-HOSVD and tMPPCA showed similar performance for the variety of x-nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL-HOSVD. The mean ratio between noise-added and denoising reproducibility coefficients of the Bland-Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL-HOSVD and tMPPCA, respectively.CONCLUSION: The strength of tMPPCA lies in the few-input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x-nuclei imaging improvements and the preferred denoising method.

KW - Deuterium

KW - Hyperpolarisation

KW - MRI

KW - carbon-13

KW - Denoising

KW - deuterium

KW - hyperpolarized carbon-13

KW - MRI

KW - sodium

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U2 - 10.1002/mrm.29817

DO - 10.1002/mrm.29817

M3 - Journal article

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EP - 2556

JO - Magnetic Resonance in Medicine

JF - Magnetic Resonance in Medicine

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ER -

A user independent denoising method for x-nuclei MRI and MRS

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Keywords

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