一种用于医学影像格式的智能转换方法、系统及电子设备

By extracting the label information and performing linear transformation on the DICOM file list, NIfTI image objects are generated, the problem of data integration difficulties caused by differences in the output of equipment from different hospitals is solved. This achieves unified conversion and quality inspection of DICOM files to NIfTI format, improving the efficiency and reliability of multi-center data integration.

CN122025035BActive Publication Date: 2026-07-17FANTASTIC BIOIMAGING CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
FANTASTIC BIOIMAGING CO LTD
Filing Date
2026-04-14
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing medical image format conversion methods cannot adapt to the differences in DICOM files output by different hospitals and equipment, resulting in a lack of unified naming conventions and directory structures in the converted NIfTI files, which in turn makes it difficult to integrate multi-center data.

Method used

By obtaining a list of DICOM files, extracting tag information to construct a metadata dictionary, performing linear transformations and affine matrix calculations, generating NIfTI image objects, and compressing them, combined with a multi-dimensional quality detection mechanism, the reliability and consistency of the conversion results are ensured.

Benefits of technology

It enables the unified conversion of DICOM files from different hospitals and equipment into the standardized NIfTI format, supports multi-center data integration, improves data reliability and processing efficiency, adapts to multi-parameter and multi-temporal studies, and ensures data quality and consistency.

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Abstract

本申请涉及医学影像处理的技术领域,尤其是涉及一种用于医学影像格式的智能转换方法、系统及电子设备,其方法,将所述标签信息构建为元数据字典;遍历所有的DICOM切片,通过所述线性变换标签获取实际物理值矩阵;将所述实际物理值矩阵沿着预设第三维方向堆叠,以形成三维NumPy数组,并计算所述DICOM切片的仿射矩阵,将所述三维NumPy数组和仿射矩阵组合成NIfTI图像对象;将所述NIfTI图像对象以及所述NIfTI头文件信息进行压缩。本申请能够实现将DICOM影像到标准化NIfTI文件转换,且确保转换结果的高质量、高一致性与可追溯性,生成的NIfTI文件信息能够适应多中心协作、大规模AI模型训练。
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