General purpose abnormality detection in whole body PET

Aim of the PhD Project:

Develop AI methods that can predict healthy PET radiotracer uptake from anatomical data.
Model the uncertainty of such predictions in a heteroscedastic (spatially varying) manner using Bayesian deep learning.
Translate these algorithms to clinical practice and integrate them within the clinical workflow.
Project Description / Background:

Cancers can have a highly heterogeneous pattern of anomalies in positron emission tomography (PET). These specific patterns of anomaly are important to detect, stage and predict the evolution of disease. In a research context, images are often analysed by performing group comparisons. This approach does not correspond to the clinic scenario, where the analysis has to be performed at the individual level to detect subject-specific patterns. In clinical practice, PET images are mostly analysed visually. The sensitivity and specificity of this approach greatly depends on the observer's experience and is not in favour of centres where advanced expertise in image reading is unavailable [Perani et al., 2014]. Quantitative analysis of PET images would alleviate this problem by helping define an objective limit between normal and pathological findings.

PET uptake can be quantitatively evaluated either regionally or on a voxel-by-voxel basis. In regional analysis, the regional uptake is compared with the regional uptake expected in a normal control population. This analysis usually requires prior knowledge to select the appropriate atlas and relevant discriminant regions, which should be adapted to a specific pathology, limiting its use (Signorini et al., 2019).

In voxel-wise analysis, a subject's PET image is usually aligned to a standardised group space to compare the metabolic activity of the spatially normalised scan to a distribution obtained from normal control scans, on a voxel-by-voxel basis. The approach implemented in software such as Neurostat consists of registering the PET image of the subject under investigation to a standard space and comparing it to a population of controls by means of a Z-score. The Z-score map is then projected onto different surfaces resulting in three-dimensional stereotactic surface projections that are used for image interpretation. Other software tools implementing a similar technique have been used for the analysis of PET data, such as NeuroGam by GE Healthcare. These packages have been mostly developed for brain imaging, limiting their applicability for other bodily diseases, but also have several non-optimal assumptions (noise model, parametric distribution, etc) about the statistical models of abnormality.

The PhD candidate will work towards creating patient- and tracer-specific models of healthy PET tracer distribution in an optimal way for full body data using artificial intelligence semantic regression models (Klaser et al. 2019). Due to the non-Gaussian nature of the expected tracer distribution, these models will use deep learning based uncertainty estimation using Dropout (Gal et al. 2015), jointly with multiple hypothesis output predictions (M-heads) to create a robust statistical model of tracer distribution. Due to the sampling nature of such model, a non-parametric statistical test can then be used to estimate a per-pixel degree of abnormality of the PET signal (Burgos et al. 2017), making the application of this technique safe in a clinical setting. Further to this, as PET imaging has both local and global uptake patterns, architectures will have to be optimised to take the full body semantics into account as to appropriately model PET at multiple spatial scales. These models will be applied to a large cohort of cancer patients with full body PET imaging, either in a PET-CT or PET-MRI setting.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.