Summary: Researchers at Umea University have developed new imaging techniques using machine learning to optimize the quality and processing of magnetic resonance imaging (MRI) scans for cancer diagnosis and treatment. The techniques eliminate common artifacts and provide more accurate imaging, allowing for precise determination of the size and position of tumors. The researchers have also created a model to generate synthetic CT scans from MRI images. These advancements not only enhance the diagnostic capabilities of MRI but also improve precision treatment by guiding radiation therapy to target tumors while avoiding healthy surrounding tissues. The findings have been made publicly available for further research and comparisons.
With the continuous advancements in medical technology, the potential of MRI in diagnosing cancer has greatly expanded. However, the sensitivity of MRI poses challenges when patients make even slight movements during the scanning process, resulting in blurry images. This hinders accurate analysis of tumors and precise treatment planning.
To address this issue, Attila Simko and his colleagues at the Department of Diagnostics and Intervention have utilized machine learning to optimize the quality and efficient processing of MRI images. Through training machine learning models, they have successfully eliminated common artifacts such as noise and movement from MRI scans. This improvement in image quality allows for clearer visualization and more accurate determination of tumor size and location.
In addition to artifact elimination, Simko and his team have also developed a robust model that generates synthetic CT scans from MRI images. This innovation provides an alternative imaging modality that can be used alongside existing diagnostic tools, enhancing the ability to accurately assess tumors and plan treatment strategies.
To drive further exploration and collaboration, the researchers have made their methods and models publicly available for other researchers to utilize and compare. By sharing this research and encouraging collaboration, the aim is to accelerate advancements in cancer diagnosis and treatment through improved imaging techniques.
The web-based version of Attila Simko’s thesis includes interactive figures, facilitating a better understanding of the field and promoting wider dissemination of knowledge and expertise.
Through these pioneering advancements in imaging techniques, the field of cancer diagnosis and treatment stands to benefit from more precise and efficient processes, ultimately improving patient outcomes.
The source of the article is from the blog maltemoney.com.br