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.