文章基本信息

标题：A patch-based super resolution algorithm for improving image resolution in clinical mass spectrometry
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作者：Klára Ščupáková ; Vasilis Terzopoulos ; Saurabh Jain 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2019
卷号：9
期号：1
页码：1-11
DOI：10.1038/s41598-019-38914-y
出版社：Springer Nature
摘要：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.