Fei Tang1, Ying Bi1, Jiuming He2, Tiegang Li2, Zeper Abliz2, Xiaohao Wang1. 1. State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China. 2. State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
Abstract
RATIONALE: Mass Spectrometry Imaging (MSI) is useful for analyzing biological samples directly, as a spatially resolved, label-free technique. Here we present a method for super-resolution reconstruction of sparse representation to improve resolution of MSI data. METHODS: Air Flow-Assisted Ionization Mass Spectrometry Imaging (AFAI-MSI) was used to acquire MSI data from ink samples, thyroid tumour samples, rat renal biopsies, and rat brain biopsy samples. Super-resolution reconstruction of sparse representation was adopted for the collected MSI data. RESULTS: After comparison of the reconstructed high-resolution image and the original high-resolution image, it is found that super-resolution reconstruction image is closer to the original high-resolution image than the image obtained with the interpolation method, and the highest Peak Signal-to-Noise Ratio (PSNR) difference value is over 1.4dB. Therefore, the application of the super-resolution reconstruction technique, based on sparse representation MSI, is feasible and effective. CONCLUSIONS: The method proposed here not only improves the resolution of MSI in post-data processing, but also acquires fewer sampling points at the same resolution, thereby greatly reducing the sampling time, with great application value for large-volume sample MSI, high-resolution MSI, etc.
RATIONALE: Mass Spectrometry Imaging (MSI) is useful for analyzing biological samples directly, as a spatially resolved, label-free technique. Here we present a method for super-resolution reconstruction of sparse representation to improve resolution of MSI data. METHODS: Air Flow-Assisted Ionization Mass Spectrometry Imaging (AFAI-MSI) was used to acquire MSI data from ink samples, thyroid tumour samples, rat renal biopsies, and rat brain biopsy samples. Super-resolution reconstruction of sparse representation was adopted for the collected MSI data. RESULTS: After comparison of the reconstructed high-resolution image and the original high-resolution image, it is found that super-resolution reconstruction image is closer to the original high-resolution image than the image obtained with the interpolation method, and the highest Peak Signal-to-Noise Ratio (PSNR) difference value is over 1.4dB. Therefore, the application of the super-resolution reconstruction technique, based on sparse representation MSI, is feasible and effective. CONCLUSIONS: The method proposed here not only improves the resolution of MSI in post-data processing, but also acquires fewer sampling points at the same resolution, thereby greatly reducing the sampling time, with great application value for large-volume sample MSI, high-resolution MSI, etc.