Tensor-Based Method for Residual Water Suppression in $^1$H Magnetic Resonance Spectroscopic Imaging

Nagaraja, Bharath Halandur; Debals, Otto; Sima, Diana M.; Himmelreich, Uwe; Lathauwer, Lieven De; Huffel, Sabine Van

doi:10.1109/tbme.2018.2850911

Cited by 7 publications

(10 citation statements)

References 34 publications

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“…To remove the large water signal in waterunsuppressed MRSI we investigated the performance of HLSVD 24,25,40 and Löwner BSS 22 and compared these with results of conventional water-signal suppression during acquisition. In the phantom study, we showed that these post-acquisition water removal methods did not interfere with the quantification of the metabolites of interest.…”

Section: Discussionmentioning

confidence: 99%

“…21 For the water-signal removal, we investigated Hankel Lanczos singular value decomposition (HLSVD) 24,25 and Löwner tensor-based blind source separation (BSS). 22 The performance of these algorithms was compared with each other and with that of conventional water suppression in a phantom and volunteers. Finally, we used the extracted water signal, for absolute metabolite quantification from MRSI's of the prostate recorded with and without water-signal suppression to investigate any effect of this suppression and to reconstruct metabolite maps.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an MRSI acquisition without water signal suppression is an attractive option, provided the metabolite signals of interest can robustly be recovered. 15,16,21,22 This may be challenging as localization gradient vibrations generate water sideband artifacts, which can overlap with metabolite signals. 16 A common way to remove these sidebands is by computing the modulus of the time-domain MRS signal.…”

Section: Introductionmentioning

confidence: 99%

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Post‐acquisition water‐signal removal in 3D water‐unsuppressed ¹H‐MR spectroscopic imaging of the prostate

Stamatelatou¹,

Sima

Huffel³

et al. 2022

Magnetic Resonance in Med

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To develop a robust processing procedure of raw signals from water-unsuppressed MRSI of the prostate for the mapping of absolute tissue concentrations of metabolites. Methods:Water-unsuppressed 3D MRSI data were acquired from a phantom, from healthy volunteers, and a patient with prostate cancer. Signal processing included sequential computation of the modulus of the FID to remove water sidebands, a Hilbert transformation, and k-space Hamming filtering. For the removal of the water signal, we compared Löwner tensor-based blind source separation (BSS) and Hankel Lanczos singular value decomposition techniques.Absolute metabolite levels were quantified with LCModel and the results were statistically analyzed to compare the water removal methods and conventional water-suppressed MRSI. Results:The post-processing algorithms successfully removed the water signal and its sidebands without affecting metabolite signals. The best water removal performance was achieved by Löwner tensor-based BSS. Absolute tissue concentrations of citrate in the peripheral zone derived from water-suppressed and unsuppressed 1 H MRSI were the same and as expected from the known physiology of the healthy prostate. Maps for citrate and choline from water-unsuppressed 3D 1 H-MRSI of the prostate showed expected spatial variations in metabolite levels. Conclusion:We developed a robust relatively simple post-processing method of water-unsuppressed MRSI of the prostate to remove the water signal. Absolute quantification using the water signal, originating from the same location as the metabolite signals, avoids the acquisition of additional reference data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Post‐acquisition water‐signal removal in 3D water‐unsuppressed ¹H‐MR spectroscopic imaging of the prostate

Stamatelatou¹,

Sima

Huffel³

et al. 2022

Magnetic Resonance in Med

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“…In addition, thorough studies are needed to explore accurate matching of estimated sources to the activity they symbolize. The assignment of sources can indeed require a priori knowledge of the activities, such as expecting a certain activity to be prominent over the others (De Vos et al, 2007), or defining frequency bands for dividing the signal subspace (Nagaraja et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition

Erol¹,

Soloukey²,

Generowicz³

et al. 2022

Preprint

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Functional ultrasound (fUS) indirectly measures brain activity by recording changes in cerebral blood volume and flow in response to neural activation.Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input (i.e., source) signal based on the stimulus onsets, the so-called experimental paradigm (EP). How-

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“…Recent growths in the deep learning (DL) algorithms have shown that models based on convolutional neuronal network (CNN) have a variety of applications on MRS [12][13][14][15][16] . Studies have shown that CNN can be used to sort the spectra according to spectral quality 14,17 .…”

Section: Introductionmentioning

confidence: 99%

Comparison of Convolutional Neural Networks Based Method and LCmodel on the Quantification of in Vivo Magnetic Resonance Spectroscopy

Huang

Lin

Tsai

2022

Preprint

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Quantification of metabolites concentrations in institutional unit (IU) for between subject and long-term comparison is considered important strategy in the applications of magnetic resonance spectroscopy (MRS). The aim of this study is to investigate if metabolite concentrations quantified by convolutional neuronal network (CNN) based method associated with a proposed scaling procedure can reflect variations of the metabolite concentrations in institution unit (IU) at different brain regions with different signal-to-noise-ratio (SNR) and linewidth (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels on the prediction for metabolites. In vivo MRS spectra were collected at 3 brain regions from 44 subjects at 3T system. Metabolite concentrations in IU quantified by LCModel and CNN from 44 subjects were compared. For in vivo spectra characterized under different spectral quality in terms of SNR and LW, line narrowing and noise free spectra were successfully exported by CNN. Concentrations of five metabolites quantified by CNN and LCModel are in similar range with statistically significant Pearson’s correlation coefficients (0.28~0.70). SE of the metabolites show positive correlation with Cramer-Rao lower bound (CRLB) (r=0.60) and with absolute CRLB (r=0.84). In conclusion, the CNN based method with the proposed scaling procedures can be used to quantify in vivo MRS spectra. The concentrations of five major metabolites were reported in IU, which are in the same range as those quantified using a routine MRS quantification procedures by LCModel. The SE can be used as error index indicating predicted uncertainties for metabolites with the information similar to the absolute CRLB.

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Tensor-Based Method for Residual Water Suppression in $^1$H Magnetic Resonance Spectroscopic Imaging

Abstract: A tensor method suppresses residual water simultaneously from all the voxels in the MRSI grid and helps in preventing the failure of the water suppression in single voxels.

Cited by 7 publications

References 34 publications

Post‐acquisition water‐signal removal in 3D water‐unsuppressed ¹H‐MR spectroscopic imaging of the prostate

Post‐acquisition water‐signal removal in 3D water‐unsuppressed ¹H‐MR spectroscopic imaging of the prostate

Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition

Comparison of Convolutional Neural Networks Based Method and LCmodel on the Quantification of in Vivo Magnetic Resonance Spectroscopy

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Tensor-Based Method for Residual Water Suppression in $^1$H Magnetic Resonance Spectroscopic Imaging

Abstract: A tensor method suppresses residual water simultaneously from all the voxels in the MRSI grid and helps in preventing the failure of the water suppression in single voxels.

Cited by 7 publications

References 34 publications

Post‐acquisition water‐signal removal in 3D water‐unsuppressed 1H‐MR spectroscopic imaging of the prostate

Post‐acquisition water‐signal removal in 3D water‐unsuppressed 1H‐MR spectroscopic imaging of the prostate

Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition

Comparison of Convolutional Neural Networks Based Method and LCmodel on the Quantification of in Vivo Magnetic Resonance Spectroscopy

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Post‐acquisition water‐signal removal in 3D water‐unsuppressed ¹H‐MR spectroscopic imaging of the prostate

Post‐acquisition water‐signal removal in 3D water‐unsuppressed ¹H‐MR spectroscopic imaging of the prostate