Synthetic Brain Imaging with Diffusion Models: A Novel Approach to Medical Image Generation
Abstract
Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are two essential neuroimaging modalities that offer complementary anatomical and functional information for diagnosing brain diseases. However, PET imaging and some advance MRI are expensive and exposes patients to ionizing radiation, limiting its routine clinical use. This thesis explores a novel diffusion model-based framework for synthetic these advanced brain image modalities that aims to reduce healthcare costs and minimize patient pain by replacing the need for real scans with high-fidelity synthetic alternatives. The first contribution of this work addresses the limitations of existing deep learning approaches that typically require separate models for each PET tracer. To overcome this inflexibility, we propose a unified diffusion framework capable of generating multiple types of PET images from multi-sequence MRI inputs within a single training. The model incorporates a cross-attention-based encoder to capture rich inter-modality dependencies and leverages a categorical embedding to condition the generation on specific tracer types, enabling flexible and efficient multi-tracer PET synthesis. The second contribution tackles the frequent issue of missing MRI modalities in clinical practice. We introduce FMM-Diff (Feature Mapping and Merging Diffusion Model), a modality-robust generative architecture designed to synthesize accurate PET images even when some MRI sequences are unavailable. Instead of…
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