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Advances in Radiotherapy
& Nuclear Medicine Image fusion’s significance in medicine

representation from a 3D patch and then devise a Considering the aforementioned limitations, several
systematic method for joint feature representation from future research directions can be envisioned for image
paired patches of MRI and PET using a multimodal DBM. fusion technology in NMMI:
(i) As artificial intelligence continues to advance, image
4. Challenges and future directions of fusion technology can be combined with deep
image fusion in nuclear medicine learning, neural networks, and other techniques to
In summary, both traditional image fusion methods improve the automation and accuracy of medical
and deep learning-based image fusion methods have image processing.
shown good performance and are playing an increasingly (ii) The utilization of 3D image fusion technology is
important role in NMMI. As computer hardware and expected to expand significantly, providing more
algorithms continue to evolve, the application prospects accurate medical image information. By effectively
of image fusion technology are becoming more and more evaluating the morphology and structure of organs,
extensive. However, despite the progress, there are still 3D medical image fusion technology can contribute
some challenges and problems in the development of to early disease diagnosis and treatment.
image fusion technology in NMMI. The challenges and (iii) Real-time image fusion technology will be further
future development trends of image fusion in NMMI are developed and applied, thereby enhancing medical
discussed in Section 4. decision-making during complex procedures,
including surgeries. By providing doctors with
Although image fusion technology has made significant accurate and real-time medical image information,
progress in recent years, several challenges and issues still this innovative technology significantly impacts
require attention and resolution. First, different types of patient care and treatment outcomes.
medical image data may exhibit different levels of noise, (iv) The combination of image fusion technology with big
distortions, and spatial and temporal resolutions, which data and cloud computing can significantly improve
may affect the quality and visual effects of the fused data, the speed and efficiency of medical image processing,
resulting in distorted or missing information. Ensuring the thus allowing for faster diagnosis and treatment.
quality and visual effects of the fusion results should be a
top priority in the development of image fusion technology. 5. Conclusion
Second, the heterogeneity of diseases and the complexity of Medical image fusion technology holds promising
human tissue in clinical work can give rise to different fusion
requirements. However, most algorithms lack universality, prospects in NMMI. By fusing different types of medical
making it difficult to meet these clinical needs. Ensuring image information, it offers more comprehensive and
precise medical image information, enabling doctors to
the robustness of the algorithm to different types of images make more accurate diagnoses and treatments. Despite
and data is a prerequisite for further development of image
fusion technology in NMMI. Third, as medical image fusion the significant progress achieved in medical image fusion
involves the sharing and transmission of sensitive medical technology, there remain challenges and issues that need to
be addressed. To further improve the accuracy, efficiency,
data, ensuring privacy and security becomes an important and reliability of medical image fusion technology,
challenge. It is essential to address concerns related to data
privacy, secure transmission, and storage in medical image continuous exploration of new algorithms and technologies
fusion. Fourth, medical image fusion algorithms require the is imperative. Integrating new technologies such as artificial
intelligence, big data, and cloud computing is essential to
processing of large amounts of data and multiple variables,
necessitating efficient computing and processing algorithms meet the ever-growing demands in medical imaging and
to ensure accuracy and real-time performance. Improving clinical applications.
the speed of image fusion and achieving real-time fusion Acknowledgments
present additional challenges to image fusion technology.
Finally, methods like deep learning rely on substantial None.
data for validation, with publicly available datasets being
frequently used to assess robustness and generalizability. Funding
However, most of the current multimodal publicly The research was funded by Beijing Hospitals Authority
available datasets are centered around brain imaging data. Dengfeng Project (Grant No.: DFL20191102), the Pilot
Consequently, research on image fusion methods primarily Project (4 Round) to Reform Public Development of
th
focuses on brain images, imposing certain requirements for Beijing Municipal Medical Research Institute (2021), and
the robustness of other organ sites. the Third Foster Plan in 2019 “Molecular Imaging Probe

Volume 1 Issue 2 (2023) 7 https://doi.org/10.36922/arnm.0870