Deep learning–based Lorentzian fitting of water saturation shift referencing spectra in MRI
Article Swipe
Sajad Mohammed Ali
,
Nirbhay N. Yadav
,
Ronnie Wirestam
,
Munendra Singh
,
Hye‐Young Heo
,
Peter C.M. van Zijl
,
Linda Knutsson
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/mrm.29718
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/mrm.29718
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.
Related Topics
Concepts
Robustness (evolution)
Algorithm
Spectral line
Approximation error
Mathematics
Mean squared error
Nuclear magnetic resonance
Computer science
Biological system
Statistics
Pattern recognition (psychology)
Analytical Chemistry (journal)
Artificial intelligence
Chemistry
Physics
Astronomy
Chromatography
Biochemistry
Gene
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/mrm.29718
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mrm.29718
- OA Status
- hybrid
- Cited By
- 15
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379509086
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379509086Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/mrm.29718Digital Object Identifier
- Title
-
Deep learning–based Lorentzian fitting of water saturation shift referencing spectra in
MRI Work title - Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-06Full publication date if available
- Authors
-
Sajad Mohammed Ali, Nirbhay N. Yadav, Ronnie Wirestam, Munendra Singh, Hye‐Young Heo, Peter C.M. van Zijl, Linda KnutssonList of authors in order
- Landing page
-
https://doi.org/10.1002/mrm.29718Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mrm.29718Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mrm.29718Direct OA link when available
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Robustness (evolution), Algorithm, Spectral line, Approximation error, Mathematics, Mean squared error, Nuclear magnetic resonance, Computer science, Biological system, Statistics, Pattern recognition (psychology), Analytical Chemistry (journal), Artificial intelligence, Chemistry, Physics, Astronomy, Chromatography, Biochemistry, Gene, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 10, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.errors, | 108 |
| abstract_inverted_index.fitting | 27 |
| abstract_inverted_index.network | 58 |
| abstract_inverted_index.points) | 199 |
| abstract_inverted_index.rapidly | 160 |
| abstract_inverted_index.sLoFNet | 88, 124, 172, 207, 222 |
| abstract_inverted_index.samples | 153 |
| abstract_inverted_index.scans). | 106 |
| abstract_inverted_index.several | 95 |
| abstract_inverted_index.whereas | 168 |
| abstract_inverted_index.Although | 146 |
| abstract_inverted_index.However, | 20 |
| abstract_inverted_index.Training | 66 |
| abstract_inverted_index.absolute | 134 |
| abstract_inverted_index.analysis | 22 |
| abstract_inverted_index.average, | 210 |
| abstract_inverted_index.chemical | 14 |
| abstract_inverted_index.commonly | 9 |
| abstract_inverted_index.compared | 91 |
| abstract_inverted_index.density, | 115, 182 |
| abstract_inverted_index.discrete | 78 |
| abstract_inverted_index.exchange | 15 |
| abstract_inverted_index.increase | 191 |
| abstract_inverted_index.methods, | 188 |
| abstract_inverted_index.occurred | 192 |
| abstract_inverted_index.overcome | 52 |
| abstract_inverted_index.proposed | 50 |
| abstract_inverted_index.sLoFNet. | 250 |
| abstract_inverted_index.sampling | 114, 181 |
| abstract_inverted_index.transfer | 17 |
| abstract_inverted_index.(sLoFNet) | 48 |
| abstract_inverted_index.compared. | 120 |
| abstract_inverted_index.conducted | 68 |
| abstract_inverted_index.decreased | 238 |
| abstract_inverted_index.frequency | 198 |
| abstract_inverted_index.increased | 159, 173, 185 |
| abstract_inverted_index.performed | 125 |
| abstract_inverted_index.reduction | 178 |
| abstract_inverted_index.simulated | 71, 100, 224 |
| abstract_inverted_index.Conclusion | 217 |
| abstract_inverted_index.Lorentzian | 26, 45, 84 |
| abstract_inverted_index.Prediction | 107 |
| abstract_inverted_index.advantages | 248 |
| abstract_inverted_index.comparably | 126 |
| abstract_inverted_index.increasing | 162 |
| abstract_inverted_index.optimized. | 65 |
| abstract_inverted_index.performed, | 208 |
| abstract_inverted_index.prediction | 183 |
| abstract_inverted_index.pronounced | 203 |
| abstract_inverted_index.robustness | 109, 234 |
| abstract_inverted_index.saturation | 2, 16 |
| abstract_inverted_index.Comparisons | 218 |
| abstract_inverted_index.Z‐spectra | 6, 230 |
| abstract_inverted_index.constructed | 61 |
| abstract_inverted_index.consumption | 118 |
| abstract_inverted_index.difference. | 145 |
| abstract_inverted_index.marginally. | 175 |
| abstract_inverted_index.parameters. | 86 |
| abstract_inverted_index.performance | 89 |
| abstract_inverted_index.referencing | 4, 12 |
| abstract_inverted_index.resolution, | 240 |
| abstract_inverted_index.significant | 144, 247 |
| abstract_inverted_index.unavoidable | 37 |
| abstract_inverted_index.Furthermore, | 206 |
| abstract_inverted_index.LS‐method. | 216 |
| abstract_inverted_index.Z‐spectral | 180 |
| abstract_inverted_index.architecture | 59 |
| abstract_inverted_index.consumption, | 245 |
| abstract_inverted_index.corresponding | 83 |
| abstract_inverted_index.shortcomings. | 54 |
| abstract_inverted_index.statistically | 143 |
| abstract_inverted_index.vivo–paired | 74 |
| abstract_inverted_index.hyperparameters | 64 |
| abstract_inverted_index.least‐squares | 24 |
| abstract_inverted_index.learning–based | 43 |
| abstract_inverted_index.time‐consuming | 29 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5002886384 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I1298154867, https://openalex.org/I145311948, https://openalex.org/I187531555 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.94075583 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |