Being able to accurately model the progression of Alzheimer’s disease (AD) is important for the diagnosis and prognosis of the disease, as well as to evaluate the effect of disease modifying treatments. Whilst there has been success in modeling the progression of AD related clinical biomarkers and image derived features over the course of the disease, modeling the expected progression as observed by magnetic resonance (MR) images directly remains a challenge. Here, we apply some recently developed ideas from the field of generative adversarial networks (GANs) which provide a powerful way to model and manipulate MR images directly though the technique of image arithmetic. This allows for synthetic images based upon an individual subject’s MR image to be produced expressing different levels of the features associated with AD. We demonstrate how the model can be used to both introduce and remove AD-like features from two regions in the brain, and show that these predicted changes correspond well to the observed changes over a longitudinal examination. We also propose a modification to the GAN training procedure to encourage the model to better represent the more extreme cases of AD present in the dataset. We show the benefit of this modification by comparing the ability of the resulting models to encode and reconstruct real images with high atrophy and other unusual features.
Being able to automate the location of individual foetal body parts has the potential to dramatically reduce the work required to analyse time resolved foetal Magnetic Resonance Imaging (cine-MRI) scans, for example, for use in the automatic evaluation of the foetal development. Currently, manual preprocessing of every scan is required to locate body parts before analysis can be performed, leading to a significant time overhead. With the volume of scans becoming available set to increase as cine-MRI scans become more prevalent in clinical practice, this stage of manual preprocessing is a bottleneck, limiting the data available for further analysis. Any tools which can automate this process will therefore save many hours of research time and increase the rate of new discoveries in what is a key area in understanding early human development. Here we present a series of techniques which can be applied to foetal cine-MRI scans in order to first locate and then differentiate between individual body parts. A novel approach to maternal movement suppression and segmentation using Fourier transforms is put forward as a preprocessing step, allowing for easy extraction of short movements of individual foetal body parts via the clustering of optical flow vector fields. These body part movements are compared to a labelled database and probabilistically classified before being spatially and temporally combined to give a final estimate for the location of each body part.
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