Image reconstruction and machine learning approaches for enhanced medical imaging. Cases in computed tomography and magnetic resonance imaging

Thesis event information

Date and time of the thesis defence

Place of the thesis defence

Aapistie 5B, Auditorium P117. Remote access: https://oulu.zoom.us/j/63686167663?pwd=UDBBL2FuTkVBNkQ4eXlrc0ZNcnh0UT09

Topic of the dissertation

Image reconstruction and machine learning approaches for enhanced medical imaging. Cases in computed tomography and magnetic resonance imaging

Doctoral candidate

Master of Science Juuso Ketola

Faculty and unit

University of Oulu Graduate School, Faculty of Medicine, Research Unit of Medical Imaging, Physics, and Technology

Subject of study

Medical physics and imaging

Opponent

Professor Tanja Tarvainen, University of Eastern Finland

Custos

Professor Miika Nieminen, University of Oulu

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Computational methods for enhanced medical imaging

In this doctoral dissertation, advanced computational methods and artificial intelligence were harnessed for medical imaging. These methods can be used to lower the radiation dose in imaging methods utilizing X-rays, improve the image quality in radiological images, and automatize diagnostics.

The methods applied and developed in the research allowed X-ray imaging with lower radiation dose and faster imaging speed, while image quality remained good in the regions-of-interest. Additionally, an artificial intelligence –based method was developed for detecting features of low back pain from magnetic resonance images.

Medical imaging is an essential part of the diagnostic chain of many diseases. The role of computational methods and artificial intelligence is increasingly important in the lowering of radiation dose, improvement of image quality, and acceleration of imaging time. Artificial intelligence will offer major benefits in enhancing the radiology of the future.
Last updated: 11.11.2021