An adaptive multi-part scanning imaging method and system based on deep learning

Abstract

The invention discloses an adaptive multi-part scanning imaging method and system based on deep learning, which is applied to multi-modal imaging equipment mainly based on nuclear medicine, and includes the following steps: Step A, aiming at multiple imaging targets Perform single-modal or multi-modal reconnaissance scanning imaging of the target site; step B, use image analysis software based on deep learning technology to analyze the reconnaissance scanning image data in step A, and combine relevant prior information to detect further Focus on the local area of ​​imaging and quantify its importance or risk; step C, based on the results of step B, select the optimized process and parameters to implement the next scan. The self-adaptive multi-part scanning imaging method proposed by the present invention maximizes the image diagnostic value contributed by the unit scanning time and / or radiation dose, realizes individual optimized precise image inspection for multi-part scanning and imaging applications such as tumors, and improves diagnostic efficiency. Has important practical value.

Description

technical field [0001] The invention relates to the technical field of nuclear medicine imaging, in particular to an adaptive multi-part scanning imaging method and system based on deep learning. Background technique [0002] SPECT (Single Photon Emission Computed Tomography, Single Photon Emission Computed Tomography) and PET (Positron Emission Tomography, Positron Emission Tomography) are two imaging technologies in nuclear medicine, which generate radioactive indicators through gamma photon detection and imaging technology. Static or dynamic images that track the uptake, distribution and excretion of drugs in humans or animals, so as to provide functional information of related systems, organs, and tissues in humans or animals, and in some cases, reveal the biochemical reactions of special cells at the molecular level process, so as to be used in clinical medical diagnosis and basic medical research. The main difference between SPECT and PET lies in the gamma photon coll...

AI Technical Summary

Problems solved by technology

There are two main problems with the above-mentioned principles and research work guiding the scanning protocol: the first is that it lacks an intuitive and clear connection with the clinical application goals of imaging-diagnosis or efficacy evaluation, thus making it difficult to have a truly accurate, personalized and Quantitative standards that are generally recognized by clinics are generated; on this basis, the selection of specific scanning acquisition procedures and parameters is greatly influenced by subjective factors such as user habits and operator experience in practice, which further leads to the questioning of the clinical value of personalized acquisition procedures , so that it is difficult to really play a role

On the basis of obtaining relatively sufficient input information, how to select parameters and procedures to achieve the ideal compromise of the above-mentioned comprehensive conditions is also a relatively complicated optimization problem.

Images