About this Research Topic
Artificial Intelligence (AI), particularly deep learning, has dramatically revolutionized biomedical physics, specifically in the field of multi-modality medical imaging. Multi-modality imaging, incorporating technologies such as MRI, CT, PET, and Ultrasound, provides a comprehensive anatomical perspective crucial for diagnosing and monitoring various diseases. However, considerable challenges persist, including the development of efficient imaging and noise-filtering algorithms for medical imaging. Another significant area of focus is the utilization of AI to fuse multi-modality information for enhanced disease diagnosis, prognosis, and treatment, including specialized applications like cancer therapy. These areas represent the frontiers of ongoing research and development in the field and offer exciting prospects for further breakthroughs.
The primary goal of this Research Topic is to explore and amplify the potentials of Artificial Intelligence (AI), notably deep learning, in enhancing the quality of multi-modality medical imaging and enriching its application in disease diagnosis, prognosis, and treatment, especially for cancer. The first area of interest pertains to harnessing AI technologies to improve imaging quality and perform advanced noise filtering. We seek high-quality research that explores innovative algorithms and techniques for image reconstruction and denoise to enhance the overall quality and interpretability of images from MRI, CT, PET, Ultrasound, and other modalities. The second focus area involves leveraging AI to fuse information from multi-modal imaging for a holistic approach to disease management. We invite research that explores the application of AI in amalgamating multi-modality information to provide enriched and comprehensive insights for disease diagnosis, prognosis, and treatment, including but not limited to specialized areas such as cancer therapy. These areas represent key challenges and opportunities in the current landscape of biomedical physics and hold exciting prospects for advancing the application of AI in multi-modality medical imaging. Through this Research Topic, we aim to foster a deeper understanding of these areas and accelerate progress toward the full realization of AI's potential in biomedical research and patient care.
This Research Topic explores the intersections of Artificial Intelligence and multi-modality medical imaging, focusing on the following areas:
- Image High-quality Reconstruction and denoising Algorithms: Contributions should focus on innovative algorithms for image reconstruction across various modalities, such as MRI, CT, PET, Ultrasound, etc.
- Fusion of Multi-Modal Information: Research should leverage AI to amalgamate multi-modal imaging data, aiming for improved disease diagnosis and prognosis.
- Handling Multi-Modal Data Missingness: Submissions should propose innovative solutions for managing the challenges of missing data in multi-modal imaging datasets.
- Application in Disease Treatment: Particularly, studies should utilize multi-modal information in devising treatment strategies, for instance, AI-guided cancer radiotherapy planning.
We welcome original research articles, reviews, method articles, and clinical trials that contribute to the existing body of knowledge in these areas, aiming to further the understanding and applicability of AI in multi-modality medical imaging.
The primary goal of this Research Topic is to explore and amplify the potentials of Artificial Intelligence (AI), notably deep learning, in enhancing the quality of multi-modality medical imaging and enriching its application in disease diagnosis, prognosis, and treatment, especially for cancer. The first area of interest pertains to harnessing AI technologies to improve imaging quality and perform advanced noise filtering. We seek high-quality research that explores innovative algorithms and techniques for image reconstruction and denoise to enhance the overall quality and interpretability of images from MRI, CT, PET, Ultrasound, and other modalities. The second focus area involves leveraging AI to fuse information from multi-modal imaging for a holistic approach to disease management. We invite research that explores the application of AI in amalgamating multi-modality information to provide enriched and comprehensive insights for disease diagnosis, prognosis, and treatment, including but not limited to specialized areas such as cancer therapy. These areas represent key challenges and opportunities in the current landscape of biomedical physics and hold exciting prospects for advancing the application of AI in multi-modality medical imaging. Through this Research Topic, we aim to foster a deeper understanding of these areas and accelerate progress toward the full realization of AI's potential in biomedical research and patient care.
This Research Topic explores the intersections of Artificial Intelligence and multi-modality medical imaging, focusing on the following areas:
- Image High-quality Reconstruction and denoising Algorithms: Contributions should focus on innovative algorithms for image reconstruction across various modalities, such as MRI, CT, PET, Ultrasound, etc.
- Fusion of Multi-Modal Information: Research should leverage AI to amalgamate multi-modal imaging data, aiming for improved disease diagnosis and prognosis.
- Handling Multi-Modal Data Missingness: Submissions should propose innovative solutions for managing the challenges of missing data in multi-modal imaging datasets.
- Application in Disease Treatment: Particularly, studies should utilize multi-modal information in devising treatment strategies, for instance, AI-guided cancer radiotherapy planning.
We welcome original research articles, reviews, method articles, and clinical trials that contribute to the existing body of knowledge in these areas, aiming to further the understanding and applicability of AI in multi-modality medical imaging.
Keywords: artificial intelligence, deep learning, multi-modality medical imaging, MRI, medical imaging, algorithms, cancer therapy
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
