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Abstract

Photoacoustic imaging (PAI) is an emerging biomedical imaging modality that harnesses pulsed laser light to generate ultrasound waves through thermoelastic expansion, enabling high-resolution, non-invasive visualization of tissue structure and function at clinically relevant depths. By combining the optical contrast of molecular imaging with the spatial precision of ultrasound, PAI offers a unique capability to assess physiological and pathological processes in vivo. Its relatively low cost, safety, and imaging speed make it a highly promising tool for a wide range of clinical applications, including oncology and vascular medicine. The clinical adoption of PAI, however, has been limited by challenges in interpreting its data without sufficient spatial, temporal, and biophysical context. This thesis addresses this limitation by developing methods that incorporate contextual information across these dimensions, enabling more accurate and clinically meaningful PAI analysis.

The lack of spatial context was addressed with a framework for reconstructing threedimensional (3D) volumes from sets of two-dimensional (2D) images. The central innovation of this approach lies in the use of an optical pattern that encodes spatial information through specific light-absorption characteristics. An extension of the pattern, adding fiducial markers, further enables the multimodal fusion of PAI with magnetic resonance imaging (MRI) and computed tomography (CT), thereby situating PAI within the established clinical imaging landscape. The lack of temporal context, addressed by pattern-based longitudinal registration of 3D PAI volumes, enables a more comprehensive assessment of disease status and progression. Third, a digital twin model was introduced to analyze unexpected clinical observations by disentangling physiological mechanisms from photoacoustic image formation processes. To demonstrate the broad applicability of these methods, they were validated in diverse clinical settings, with applications ranging from cancer therapy to vascular disease diagnosis. In a clinical study on peripheral artery disease, optical pattern-based PAI successfully detected ischemia and muscular heterogeneities, indicating benefits over conventional 2D approaches by combining spatial and temporal context. This thesis also presents the first evidence that PAI can non-invasively capture molecular changes induced by radiotherapy in patients with head and neck cancer. In this study, digital twin modeling further provided a mechanistic explanation for unexpected oxygenation measurements, revealing that these anomalies arose from signal distortions in regions with low blood volume.

In conclusion, this work establishes the concept of context-aware PAI, integrating spatial and temporal, multimodal, and biophysical information to enhance both interpretability and clinical trust. By demonstrating feasibility in clinical studies, it outlines a pathway for translating context-aware PAI into routine medical practice.