Browsing by Author "Gutierrez Munoz, Jose Alejandro"

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    Acute Ischemic Stroke Analysis Using Deep Learning-based Image-to-image Translation
    (2023-08) Gutierrez Munoz, Jose Alejandro; Forkert, Nils Daniel; Pike, G. Bruce; Lee, Joon; LeVan, Pierre; Almekhlafi, Mohammed
    Acute ischemic stroke occurs due to the sudden occlusion of a cerebral artery, leading to a disruption in metabolic homeostasis and cell damage. Accurate diagnosis and informed treatment decision-making rely on clinical assessments accompanied by medical imaging. Deep learning methods offer the potential to enhance this decision-making by enabling complex pattern recognition. However, they often rely on large amounts of data, which poses a challenge in stroke centers due to the diverse range of imaging modalities employed. Moreover, specialized processing is often required for the meaningful interpretation of valuable imaging methods like perfusion imaging. Recent advancements in deep learning have made it easier to process and analyze perfusion data by predicting the follow-up tissue outcome. However, these models rely on manual binary lesion segmentations as prediction targets, which may hinder interpretability and limit the amount of available data. To address these limitations, the work described in this thesis utilizes a set of deep learning techniques known as image-to-image translation networks in two distinct ways. First, a method was developed to simulate computed tomography datasets based on magnetic resonance imaging scans and vice versa. The results showed that the proposed approach produces realistic outputs, effectively changing the modality while preserving stroke lesions and brain morphology in follow-up scans. This increases the availability of single-modality data and provides an alternative imaging option for follow-up stroke evaluation. Second, a method was developed to predict stroke tissue outcomes from perfusion scans, without relying on manual lesion segmentations and predicting the follow-up image instead. The results show that the proposed method is able to capture the effects of different treatments, highlighting its potential as a tool for treatment guidance or efficacy evaluation. In conclusion, the application of image-to-image generative modelling proves to be valuable for enhancing acute ischemic stroke analysis and care.