Mats Persson awarded with starting Grant

The grant is directed to the project “Highly accurate spectral photon counting CT for improved cancer diagnosis”

Cancer is one of the leading causes of mortality with an expected 10 million deaths annually. A very widely used imaging modality for diagnosing cancer is x-ray computed tomography (CT), which provides three-dimensional images of the human body is reconstructed from x-ray measurements. Despite its high usefulness, there are limitations with the current CT technology with respect to diagnostic quality and quantitative accuracy. The emerging photon-counting CT technology can overcome these limitations with its higher spatial resolution, lower image noise, and improved material-selective imaging.

To achieve the full potential of the technology, new image reconstruction methods need to be developed. Deep-learning-based image reconstruction, a new technology for image CT reconstruction, has demonstrated substantial image quality improvement and fast reconstruction. We will develop a deep-learning-based CT image reconstruction method for generating highly accurate photon counting images together with maps of image uncertainty, using a CT scanner prototype developed in our lab.

We will evaluate the usefulness of the new imaging technique for diagnosis and radiomic characterization of tumors. The anticipated outcome is that photon-counting spectral CT with deep-learning reconstruction can give drastically improved diagnostic quality and radiomic measurement accuracy without extra dose. This can lead to saved lives and new research avenues in the field of data-driven cancer diagnosis.