Multimodal Imaging in Oncology: Challenges and Future Directions

Yanqi Zong; Sihao Wang; Zhengrong Cui; Luqi Lin; Yizhi Chen

doi:10.54097/qw4tja89

Authors

Yanqi Zong
Sihao Wang
Zhengrong Cui
Luqi Lin
Yizhi Chen

DOI:

https://doi.org/10.54097/qw4tja89

Keywords:

Multimodal imaging, Oncology, Computed tomography, Magnetic resonance imaging, Positron emission tomography, Image Guided Radiation Therapy, Stereotactic Body Radiation Therapy, image registration, Fusion, Algorithm optimization, Hardware advancements, Computer vision; Artificial intelligence (AI).

Abstract

Multimodal imaging, a vital tool in oncology, combines various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to offer valuable insights for tumor detection, diagnosis, staging, and treatment planning. Despite its advantages, challenges persist in image registration, fusion, optimization of algorithms, hardware advancements, and the integration of artificial intelligence (AI) and computer vision techniques. This paper delves into the current state of multimodal imaging in oncology, addresses the challenges faced, explores potential solutions, and underscores future developments to augment diagnostic and therapeutic capacities, including the application of AI, computer vision, and big data analytics to enhance the efficiency and effectiveness of multimodal imaging in cancer diagnosis and treatment. Additionally, it highlights how AI-powered algorithms can automate image analysis, assist in tumor segmentation, and provide predictive insights, ultimately improving the precision and speed of diagnosis and treatment planning in oncology.

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