A patch-based super resolution algorithm for improving image resolution in clinical mass spectrometry
Open accessAbstract
Mass spectrometry imaging (MSI) and histology are complementary analytical tools. Integration of the two imaging modalities can enhance the spatial resolution of the MSI beyond its experimental limits. Patch-based super resolution (PBSR) is a method where high spatial resolution features from one image modality guide the reconstruction of a low resolution image from a second modality. The principle of PBSR lies in image redundancy and aims at finding similar pixels in the neighborhood of a central pixel that are then used to guide reconstruction of the central pixel. In this work, we employed PBSR to increase the resolution of MSI. We validated the proposed pipeline by using a phantom image (micro-dissected logo within a tissue) and mouse cerebellum samples. We compared the performance of the PBSR with other well-known methods: linear interpolation (LI) and image fusion (IF). Quantitative and qualitative assessment showed advantage over the former and comparability with the latter. Furthermore, we demonstrated the potential applicability of PBSR in a clinical setting by accurately integrating structural (i.e., histological) and molecular (i.e., MSI) information from a case study of a dog liver.
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Multimodal image alignment provides spatial mapping between images taken by sensors of different modalities and can be categorized into feature-based and patch-based methods.
“Specifically, multimodal image alignment provides a spatial mapping capability between images taken by sensors of different modalities, which may be categorized into feature-based [167, 168] and patch-based [165, 166] methods.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [165]
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
3D Slicer as an image computing platform for the Quantitative Imaging Network
Fedorov, A., Beichel, R., Kalpathy–Cramer, J., Finet, J., Fillion‐Robin, J., Pujol, S., Bauer, C., Jennings, D. L., Fennessy, F., Milan Sonka, A., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.05.001 · Cited by 9,095 · see citing works
Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311 · see citing works
THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED
WELCH, B. L. (1947) · Biometrika · doi:10.1093/biomet/34.1-2.28 · Cited by 4,135 · see citing works
Super-resolution image reconstruction: a technical overview
Park, S. C., Park, M., & Kang, M. G. (2003) · IEEE Signal Processing Magazine · doi:10.1109/msp.2003.1203207 · Cited by 3,325 · see citing works
Multi-frame image restoration and registration
Huang, T. S. (1984) · Medical Entomology and Zoology · Cited by 966 · see citing works
Multiframe image restoration and registration
Tsai, R. (1984) · Medical Entomology and Zoology · Cited by 804 · see citing works
Mass Spectrometric Imaging for Biomedical Tissue Analysis
Chughtai, K., & Heeren, R. M. A. (2010) · Chemical Reviews · doi:10.1021/cr100012c · Cited by 652 · see citing works
Self-citationImage super-resolution: the techniques, applications, and future
Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., & Zhang, L. (2016) · Signal Processing · doi:10.1016/j.sigpro.2016.05.002 · Cited by 554 · see citing works
Integrating Histology and Imaging Mass Spectrometry
Chaurand, P., Schwartz, S. A., Billheimer, D. D., et al. (2004) · Analytical Chemistry · doi:10.1021/ac0351264 · Cited by 366 · see citing works
de Plas, R. V., Yang, J., Spraggins, J. M., & Caprioli, R. M. (2015) · Nature Methods · doi:10.1038/nmeth.3296 · Cited by 312 · see citing works
Mass spectrometry imaging for plant biology: a review
Boughton, B. A., Thinagaran, D., Sarabia, L. D., et al. (2015) · Phytochemistry Reviews · doi:10.1007/s11101-015-9440-2 · Cited by 305 · see citing works
Bodzoń‐Kułakowska, A., & Suder, P. (2015) · Mass Spectrometry Reviews · doi:10.1002/mas.21468 · Cited by 174 · see citing works
Imaging Mass Spectrometry: Principles and Potentials
Chaurand, P., Schwartz, S. A., Reyzer, M. L., & Caprioli, R. M. (2005) · Toxicologic Pathology · doi:10.1080/01926230590881862 · Cited by 171 · see citing works
Balluff, B., Schöne, C., Höfler, H., & Walch, A. K. (2011) · Histochemistry and Cell Biology · doi:10.1007/s00418-011-0843-x · Cited by 122 · see citing works
Current State and Future Challenges of Mass Spectrometry Imaging for Clinical Research
Addie, R. D., Balluff, B., Bovée, J. V. M. G., et al. (2015) · Analytical Chemistry · doi:10.1021/acs.analchem.5b00416 · Cited by 121 · see citing works
Correlating MALDI and SIMS imaging mass spectrometric datasets of biological tissue surfaces
Eijkel, G. B., Kaletaş, B. K., van der Wiel, I. M., Kros, J. M., Luider, T. M., & Heeren, R. M. A. (2009) · Surface and Interface Analysis · doi:10.1002/sia.3088 · Cited by 69 · see citing works
Self-citationPatch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis
Jain, S., Sima, D. M., Nezhad, F. S., et al. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00013 · Cited by 38 · see citing works
Self-citationFlinders, B., Huizing, L. R. S., van Heerden, M., Cuyckens, F., Neumann, U. P., van der Laan, L. J. W., Damink, S. W. M. O., Heeren, R. M. A., Schaap, F. G., & Vreeken, R. J. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b01378 · Cited by 34 · see citing works
Self-citationSTUDY ON THE METHODS OF SUPER-RESOLUTION IMAGE RECONSTRUCTION
Ji, S. (2007) · Cited by 18 · see citing works
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Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
- Authors
- Ščupáková, K., Terzopoulos, V., Jain, S., Smeets, D., & Heeren, R. M. A.
- Affiliations
- Maastricht University (NL)
- Icometrix (Belgium) (BE)
- Corresponding author
- Ščupáková, Klára
- Heeren, Ron M. A.
- Venue
- Scientific Reports
- Published
- 2019-02-27
- Volume
- 9
- Issue
- 1
- Pages
- 2915-2915
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6393664
- OpenAlex
- W2917807569
- Cited by
- 23
- References
- 20
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2022: 6 · 2021: 3 · 2020: 3 · 2019: 2
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- European Commission (612360, FP7-PEOPLE-2013-IAPP, FP7-PEOPLE-2013)
- National Institutes of Health
- National Cancer Institute (R01CA213492)
- FP7 People: Marie-Curie Actions (612360, FP7-PEOPLE-2013-IAPP, FP7-PEOPLE-2013)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- mass spectrometry imaging, spatial resolution enhancement, image fusion, linear interpolation, mouse cerebellum, canine liver, clinical mass spectrometry
- MeSH terms
- Algorithms, Animals, Cerebellum, Diagnostic Imaging, Dogs, Humans, Image Enhancement, Liver, Mass Spectrometry, Phantoms, Imaging, Clinical Laboratory Techniques, Mice
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