Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Improved PET/CT clinical workflow and productivity with AI-enabled reconstruction and processing methods.

Graphic image AI and machine learning icons surrounding a human body.


 Project Leads: GE Healthcare with Fergus Gleeson, Oxford University Hospital

Machine and deep learning (ML, DL) have shown potential to improve both efficiency, accuracy and quality of health care delivery. There are many potential areas where ML or DL can interact, from pre-population of information for a patient scan based on prior information, improvement of the quality of data produced by an imaging system, or deeper pattern recognition applied to the data from a procedure.

All of these areas are currently impacted by the complexity of today’s modern systems – and while the capability of current systems has never been higher, it often comes at the cost of more burden on those who use the systems.

A simple example is the production of more and better imaging data, often read in combination with data from other modalities and prior scans to form a current action for the patient.

Machine learning can be used to improve the efficiency of the diagnostic process, improve the quality of data gained from the process and find deeper patterns within the data. All of these will improve the value to the clinician and patient, enabling earlier and more accurate decisions as well as better overall patient outcomes. It should be noted that emphasis will be placed on solutions which improve performance, time, workflow or cost – or ideally all of these. 

Project Aim

The study aims to develop and implement machine learning enhanced methods which improve workflow and data quality for PET/CT imaging studies and to produce improvements in healthcare delivery that reduce time and patient radiation dose and increase quality and accuracy of diagnoses while improving the overall workflow. 


  1. Reduce dose and/or scan time for PET/CT by utilizing ML-based data enhancement. Such enhancement may include de-noising of lower dose or shorter scan time PET data. 
  2. Enable multi-gate respiratory motion correction for PET/CT for whole-body imaging. Automated PET respiratory motion correction can improve image quality and quantification. Machine learning will be used to improve the motion estimation by reducing noise in the time-constrained gated image data and therefore increase the accuracy of a 3D, non-rigid registration as compared to the current approach.
  3. Decrease PET image reconstruction time for the state-of-the-art Q.Clear method by using ML to reduce the number of required reconstruction iterations. This is an important factor by itself, but becomes even more important in combination with whole-body automated all-counts respiratory motion correction or whole-body dynamic PET imaging. 
  4. Decrease scan prescription, setup and acquisition time and improve patient experience by utilizing ML techniques to eliminate or optimize the tasks performed during a typical PET/CT Scan.