Deep learning-based Lorentzian fitting of water saturation shift referencing spectra in MRI
Mohammed Ali, Sajad LU ; Yadav, Nirbhay N ; Wirestam, Ronnie LU
; Singh, Munendra
; Heo, Hye-Young
; van Zijl, Peter C
and Knutsson, Linda
LU
(2023)
In Magnetic Resonance in Medicine
90(4).
p.1610-1624
- Abstract
PURPOSE: Water saturation shift referencing (WASSR) Z-spectra are used commonly for field referencing in chemical exchange saturation transfer (CEST) MRI. However, their analysis using least-squares (LS) Lorentzian fitting is time-consuming and prone to errors because of the unavoidable noise in vivo. A deep learning-based single Lorentzian Fitting Network (sLoFNet) is proposed to overcome these shortcomings.
METHODS: A neural network architecture was constructed and its hyperparameters optimized. Training was conducted on a simulated and in vivo-paired data sets of discrete signal values and their corresponding Lorentzian shape parameters. The sLoFNet performance was compared with LS on several WASSR data sets (both simulated and... (More)
PURPOSE: Water saturation shift referencing (WASSR) Z-spectra are used commonly for field referencing in chemical exchange saturation transfer (CEST) MRI. However, their analysis using least-squares (LS) Lorentzian fitting is time-consuming and prone to errors because of the unavoidable noise in vivo. A deep learning-based single Lorentzian Fitting Network (sLoFNet) is proposed to overcome these shortcomings.
METHODS: A neural network architecture was constructed and its hyperparameters optimized. Training was conducted on a simulated and in vivo-paired data sets of discrete signal values and their corresponding Lorentzian shape parameters. The sLoFNet performance was compared with LS on several WASSR data sets (both simulated and in vivo 3T brain scans). Prediction errors, robustness against noise, effects of sampling density, and time consumption were compared.
RESULTS: LS and sLoFNet performed comparably in terms of RMS error and mean absolute error on all in vivo data with no statistically significant difference. Although the LS method fitted well on samples with low noise, its error increased rapidly when increasing sample noise up to 4.5%, whereas the error of sLoFNet increased only marginally. With the reduction of Z-spectral sampling density, prediction errors increased for both methods, but the increase occurred earlier (at 25 vs. 15 frequency points) and was more pronounced for LS. Furthermore, sLoFNet performed, on average, 70 times faster than the LS-method.
CONCLUSION: Comparisons between LS and sLoFNet on simulated and in vivo WASSR MRI Z-spectra in terms of robustness against noise and decreased sample resolution, as well as time consumption, showed significant advantages for sLoFNet.
(Less)
- author
- Mohammed Ali, Sajad
LU
; Yadav, Nirbhay N
; Wirestam, Ronnie
LU
; Singh, Munendra
; Heo, Hye-Young
; van Zijl, Peter C
and Knutsson, Linda
LU
- organization
- publishing date
- 2023-06-06
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Magnetic Resonance in Medicine
- volume
- 90
- issue
- 4
- pages
- 1610 - 1624
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:85161553987
- pmid:37279008
- ISSN
- 1522-2594
- DOI
- 10.1002/mrm.29718
- language
- English
- LU publication?
- yes
- id
- dbc762ea-86c1-4393-b251-2551014e86d3
- date added to LUP
- 2023-06-11 19:29:12
- date last changed
- 2024-04-19 22:44:49
@article{dbc762ea-86c1-4393-b251-2551014e86d3,
abstract = {{<p>PURPOSE: Water saturation shift referencing (WASSR) Z-spectra are used commonly for field referencing in chemical exchange saturation transfer (CEST) MRI. However, their analysis using least-squares (LS) Lorentzian fitting is time-consuming and prone to errors because of the unavoidable noise in vivo. A deep learning-based single Lorentzian Fitting Network (sLoFNet) is proposed to overcome these shortcomings.</p><p>METHODS: A neural network architecture was constructed and its hyperparameters optimized. Training was conducted on a simulated and in vivo-paired data sets of discrete signal values and their corresponding Lorentzian shape parameters. The sLoFNet performance was compared with LS on several WASSR data sets (both simulated and in vivo 3T brain scans). Prediction errors, robustness against noise, effects of sampling density, and time consumption were compared.</p><p>RESULTS: LS and sLoFNet performed comparably in terms of RMS error and mean absolute error on all in vivo data with no statistically significant difference. Although the LS method fitted well on samples with low noise, its error increased rapidly when increasing sample noise up to 4.5%, whereas the error of sLoFNet increased only marginally. With the reduction of Z-spectral sampling density, prediction errors increased for both methods, but the increase occurred earlier (at 25 vs. 15 frequency points) and was more pronounced for LS. Furthermore, sLoFNet performed, on average, 70 times faster than the LS-method.</p><p>CONCLUSION: Comparisons between LS and sLoFNet on simulated and in vivo WASSR MRI Z-spectra in terms of robustness against noise and decreased sample resolution, as well as time consumption, showed significant advantages for sLoFNet.</p>}},
author = {{Mohammed Ali, Sajad and Yadav, Nirbhay N and Wirestam, Ronnie and Singh, Munendra and Heo, Hye-Young and van Zijl, Peter C and Knutsson, Linda}},
issn = {{1522-2594}},
language = {{eng}},
month = {{06}},
number = {{4}},
pages = {{1610--1624}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Magnetic Resonance in Medicine}},
title = {{Deep learning-based Lorentzian fitting of water saturation shift referencing spectra in MRI}},
url = {{http://dx.doi.org/10.1002/mrm.29718}},
doi = {{10.1002/mrm.29718}},
volume = {{90}},
year = {{2023}},
}