Opening statement

I view machine learning as a truly disruptive technology, or more accurately a paradigm shift,1 and believe that it has transformative potential in the medical physics field, valid for both medical imaging and treatment planning. Clearly, the interest in machine learning seems much greater than that in compressed sensing, as evidenced by my quick PubMed search for the title to contain “machine learning” and “compressed sensing”, respectively. The number of hits for “machine learning” has increased from 151 to 450 over the past 5 years, while the number for “compressed sensing” has only gone from 84 to 102. Given the successes of machine learning in other areas, I have little doubt that machine intelligence will reshape medical physics, and more generally radiology, and we should immediately make major efforts toward this direction.

Intelligence is essentially the capability to extract knowledge that allows comprehension and prediction, which can be in most cases performed computationally. When data are becoming diversified and explosive in either medical imaging or radiotherapy, the classic methods cannot model and utilize huge data effectively and efficiently. It seems that big data and deep learning promise numerous opportunities for medical physicists. Instead of trying to enumerating all the possibilities, without loss of generality, let me discuss this transformative approach as related to two transforms: the Radon transform (from an underlying image to its projections) and radiation treatment planning (from a source distribution to therapeutic beam profiles).

This year is for the first centenary celebration of the Radon transform, which is fundamental to not only CT but also other tomographic modalities. In practice, Radon data are never ideal; for example, in x‐ray imaging, projections are compromised by source spot size, beam hardening, detector imperfection, geometric mismatch, patient motion, metal artifacts, photon fluctuations, and other factors. Over the past decades, excellent analytic and iterative reconstruction methods have been developed. However, the assumed data model is only approximate and compromises image quality; for example, it is challenging to convert photon‐counting data into linear integrals, especially when radiation dose is low. In this aspect, image quality can be potentially improved via a deep neural network. This is to perform the Radon transform via machine learning, a freshly new way to recharge the existing reconstruction algorithms for more quantitative results.

The optimization of a therapeutic plan needs to ensure tumor killing while sparing healthy/sensitive tissues for the best prognosis.2-7 In this context, there is a critical need for a high‐quality predictive model which integrates a huge amount of heterogeneous data via machine learning,8, 9 including electronic health records, tomographic and therapeutic images, and genomic profiles. Tomographic images can be improved via machine learning to reduce metal artifacts,10 estimate an attenuation background,11 target tumors,12 and so on. Hence, the potential of machine learning ought to be significant for radiotherapy. We expect that the ultimate therapeutic system will be able to reconstruct images and design plans with high confidence, and keep learning from huge, distributed, and living data sources.