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AI-enabled Imaging

General purpose abnormality detection in whole body PET

Project ID: 2020_004

Student: Ashay Patel

1st Supervisor: Jorge Cardoso, King’s College London
2nd Supervisor: Seb Ourselin, King’s College London
Clinical Champion: Vicky Goh, King’s College London
Industry Supervisor: Renee Miller (GE Healthcare)

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.

The candidate should have a background in one of the following: biomedical engineering, applied mathematics/physics or computer science. The candidate should also have a keen interest in advanced Artificial Intelligence algorithms, and modelling of high-dimensional system.

Figure 1. Please refer to the description below the image.
Figure 1: Whole body MRI and PET images that will be used in this study


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