People

"I believe that AI is the key to a new era of healthcare."

March 2023

Song Xue is a biomedical engineer specialized in deep learning, conducting postdoctoral research at the AI for Translational Theranostics (AITT) group of the Department of Nuclear Medicine, Inselspital. Applying artificial intelligence to nuclear medicine, Song aims to reduce radiation in diagnostic PET imaging and to personalize dose prediction for radionuclide therapy. With new therapeutic fields just opening for nuclear medicine, Song feels that his research is at an exciting intersection of data science, clinical practice, and industry.

Song Xue (middle) Jimin Hong (left) and Carlos Gomes Ferreira (right) aim to extend the possibilities of PET imaging and radionuclide therapy via AI. (© Xiaodong Li)

Song, what are you working on?
I am working on dose optimization in nuclear medicine for both diagnostic imaging and radioligand therapy with the help of artificial intelligence.
For the imaging part, the most widely used scanner is the PET/CT (positron emission tomography/computer tomography). I apply AI for image denoising or image quality recovery from low-dose imaging. A second part is attaining the same function as a CT via deep learning (attenuation and scatter correction) for CT-free PET imaging.
In radioligand therapy higher-dose injected tracer drugs destroy tumor cells. The problem is that today a standard dose is given for that which may be too high (harming other organs like the kidneys) or too low (not optimal results) for the patient. As this therapy form is newly accredited for prostate cancer, we have proposed a method for predicting the dose of radionuclide therapy based on pre-treatment information, enabling personalized treatment with greater precision.

Deep learning analysis of different PET scanner models in a recent study of the AITT group. (https://doi.org/10.1038/s41467-022-33562-9)

Who will benefit from this?
With our research we aim to reduce adverse effects for the patients by minimizing the injected dose (as low as possible to the effective) and this way also try to make PET available for pediatric patients. But our AI-enhanced imaging also has advantages for hospitals as they need less time for image acquisition per patient, allowing them to treat more patients with the same resources.
Future work will focus on two aspects. On the one hand, we will develop a set of standardized guidelines for radionuclide therapy for prostate cancer and a software package for personalized treatment planning. On the other hand, we will develop a low-dose PET imaging system based on deep learning, which will enable high-performance noise reduction algorithms, CT-free PET imaging, and high-precision, high-sensitivity PET systems. These technologies will make PET imaging more convenient and feasible in scenarios such as routine medical screening.

Dr. Lorenzo Mercolli discusses clinical challenges in PET imaging with Song Xue at the Nuclear Medicine Department, Inselspital, Bern University Hospital. (© Xiaodong Li)

Through domain knowledge we aim to improve robustness and generalizability of AI for nuclear medicine.

Do you see yourself as part of a bigger research community?
Definitely! I have previously worked on different imaging modalities as well as genomics data with the help of AI. So, I see myself as an AI developer for healthcare in general. To stay connected in this field, we have started a few collaborations here in Bern as this offers more inspiration from other groups using similar techniques but for other tasks. Maybe one group develops an innovative approach around AI that we could also use. It also helps to see that others face similar challenges in the algorithm development.
As we work in healthcare, it is very useful to understand how doctors think and how they would formulate a problem. This helps us to define our research tasks much better. In nuclear medicine, for example, we try to integrate the domain knowledge from physics into the design of the AI to improve its robustness and generalizability. We have the additional advantage that Switzerland is leading radionuclide prostate cancer therapy and thus has data from clinical trials available.

What motivated you to your field of research?

Biomedical engineering has been my passion since I was an undergrad. At the beginning of my master’s studies, I was witnessing a boom in AI technology, which I believe is the key to a new era of healthcare. As a result, I switched to data science and then worked as a deep learning engineer at two start-ups that focused on the application of AI in medical imaging. There, I not only gained valuable experience in AI applications, but more importantly, an insight into the operations of such start-ups and the market potential of these applications.

I joined the nuclear medicine department at Inselspital, one of the leading PET centers in the world, to be part of the exciting recent developments in nuclear medicine and PET imaging. I can see how my research can help people and I like getting feedback from the clinic.

It´s exciting for me to be part of the future of nuclear medicine. I could also imagine working on predicting the chemical structure of new PET tracers with advanced deep learning techniques. This could open many more application fields for cancer treatment.

(© CAIM, University of Bern)

Song Xue is a postdoctoral researcher at the AI for Translational Theranostics group of the Department of Nuclear Medicine, Inselspital, Bern University Hospital. After his bachelor in biomedical engineering, he did a Masters in business intelligence and analytics at the Stevens Institute of Technology in New Jersey, USA. After working as a deep learning engineer in industry, Song obtained his doctoral degree in biomedical science from the University of Bern last year working on AI applications in Nuclear Medicine.

He now continues this line of study as a postdoc under the supervision of informatics specialist Prof. Kuanqyu Shi and Department Head Prof. Dr. med. Axel Rominger. 

Song’s main research area is in dose optimization for nuclear medicine imaging and radionuclide therapy. For imaging, his research has pioneered the integration of domain knowledge and deep learning models to achieve high-quality imaging at low radiation doses, while reducing the radiation dose from injected radiopharmaceuticals and CT. With his team he also works on predicting the dose of radionuclide therapy for prostate cancer to enable personalized treatment planning with greater precision.

Recent article in Nature Communications on CT-free PET imaging

Publication of low-dose PET on EJNMM

Publication of PSMA dose prediction on EJNMMI