Mass spectrum image super-resolution reconstruction method based on deep learning

Abstract

The invention relates to a mass spectrum image super-resolution reconstruction method based on deep learning, and belongs to the image processing field. The mass spectrum image super-resolution reconstruction method based on deep learning includes the following steps: using a matrix-assisted laser desorption / ionization mass spectrometry imaging instrument to perform imaging on a biological sample,according to different m / z values, obtaining multiple mass spectrum images, obtaining multiple low resolution images through down-sampling, solving the morphological information of the acquired high / low resolution images and taking the morphological information as a training sample set, designing and training deep convolutional neural networks, according to the input training sample set, predicting the missing information in the low resolution images; during the test stage, by means of the prior knowledge obtained through networks, guiding reconstruction of the morphological information of the low resolution mass spectrum images; and reconstructing the morphological information into high resolution mass spectrum images by establishing a partial differential equation. The mass spectrum image super-resolution reconstruction method based on deep learning avoids the defect that a traditional technology improves the imaging quality by improving imaging equipment or by means of repeated sampling, can reduce the cost and the experimental cycle, and can breakthrough limitation of image resolution for a hardware system.

Description

technical field [0001] The invention belongs to the technical field of biochemical image processing, and in particular relates to a method for improving the resolution of mass spectrometry images through super-resolution reconstruction of mass spectrometry images based on deep learning. Background technique [0002] Matrix-assisted laser desorption / ionization-time-of-flight mass spectrometry (MALDI-TOF-MS) imaging (MSI), as a technique that combines molecular mass analysis and spatial information to directly detect biomolecular patterns from tissues and cells, has received great attention in recent years. It has been developed rapidly and has been widely used in the fields of clinical medicine, biomolecules and pharmacy. However, limited by the experimental period, cost and imaging equipment, the mass spectrometry images involved in practical applications have the characteristics of low quality and low resolution. This type of mass spectrometry image is difficult to accurat...

AI Technical Summary

Problems solved by technology

Traditional methods based on interpolation, reconstruction, learning models, and sparse representation are not universal for mass spectrometry image features. It is necessary to establish a more effective mass spectrometry image super-resolution reconstruction method for mass spectrometry image features.

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