The multimodal fusion technology of medical imaging equipment aims to integrate the information obtained by different imaging methods to provide a more comprehensive and accurate basis for clinical diagnosis, but it faces many difficulties in the development process.
First, data registration is a major difficulty. Different modal images, such as anatomical information of CT and functional metabolic information of PET, differ in the spatial resolution, scale, position and other aspects of the image. To achieve accurate registration, it is necessary to overcome the effects caused by changes in patient position, organ movement, and inconsistent scanning ranges and angles of different equipment. For example, during respiratory movement, the shape and position of the lungs are constantly changing, making it more difficult to register lung CT and PET images, and it is necessary to develop complex motion correction algorithms to ensure the accuracy of registration.
Secondly, the inconsistency of data formats and standards also hinders the advancement of fusion technology. Different medical imaging equipment manufacturers use different data storage formats and transmission protocols, which makes data integration and interaction complicated. The lack of unified data standards makes it difficult to directly read and process data from different devices in the fusion system, requiring a lot of data conversion and adaptation work, which increases the complexity and cost of system development.
Image feature extraction and fusion algorithm optimization is another key difficulty. The features of different modal images are diverse and complex. How to effectively extract and reasonably fuse these features to highlight lesion information and avoid information redundancy or loss is a challenge. For example, when fusing CT and MRI images, MRI has high soft tissue contrast but the anatomical structure is not as clear as CT. It is necessary to design an intelligent algorithm to balance the advantages of the two. The current algorithm still has limitations when dealing with complex lesions or subtle structures.
In terms of computing resources and processing speed, multimodal fusion involves the processing and analysis of a large amount of data, which requires extremely high computing resources. Real-time or fast fusion processing is particularly important in clinical applications, but current hardware technology may not be able to meet the needs of fast computing when facing large-scale data, resulting in a long fusion process and affecting clinical work efficiency.
In terms of breakthrough direction, with the development of artificial intelligence technology, deep learning algorithms are expected to make breakthroughs in data registration, feature extraction and fusion. Through the training of a large amount of image data, deep learning models can automatically learn the mapping relationship between different modal images to improve registration accuracy and fusion effect. For example, the image registration method based on convolutional neural network has shown good application prospects.
In terms of data standards, international and domestic medical imaging standardization organizations are committed to formulating unified data formats and transmission protocols to promote the interconnection and interoperability of multimodal imaging data. Once the standard is widely used, it will greatly simplify the development and integration of fusion systems.
The continuous advancement of hardware technology, such as the emergence of high-performance GPUs and the application of cloud computing technology in the field of medical imaging, provides powerful computing support for multimodal fusion, which is expected to significantly improve the fusion processing speed, promote the widespread application of multimodal fusion technology in clinical practice, and provide more powerful technical support for precision medicine.
