title: Detection of spatio-temporal patterns in medical image stacks
Creators: name: Zerweck, Lukas Benjamin
Publisher: name: UNSPECIFIED
Year: 2025

description: In this work, the possibilities of exploiting the medical imaging modality of near-infrared fluorescence optical imaging (NIR-FOI) for the detection of psoriatic arthritis (PsA) are investigated. PsA is an autoinflammatory, chronic disease of the musculoskeletal system, which affects millions of people worldwide. This disease has a wide variety of manifestations such as arthritis with pain, joint stiffness, enthesitis, and more. Therefore, it can reduce the quality of life drastically if left untreated. Even though treatment exists to slow down disease progression, many questions are still unanswered ranging from exact causes over exact definitions to specific disease mechanisms. Thus, researchers across fields are still in the process of defining clear criteria for the definition of this disease as well as developing new detection methods. The unique structure of the measured data requires approaches tailored specifically, not only to the use-case but also to the properties of the data itself. From each measurement, a spatio-temporal image stack with only partially visible objects is received. The general idea is to classify spatio-temporal patterns of the distribution process of an injected color agent. To classify the spatio-temporal patterns a neural network for the classification of individual joints is developed. Nonetheless, additional pre-processing steps are required to ensure the correctness of the measured data. This contains segmentation and artifact detection, which includes three different detection steps: The checking of basic requirements, motion detection, and ambient light detection. Similar to the classification model, the existing approaches do not solve the given task. Besides the approach of classifying the spatio-temporal data a research study was performed to investigate non-pathological factors impacting the acquired images. Finally, two additional applications of NIR-FOI are discussed in the surrounding of developing an entire therapy concept around this imaging modality. One application focuses on therapy monitoring by investigating longitudinal data and in the second application NIR-FOI is merged with a volumetric imaging modality (MRI). For the segmentation and artifact detection, satisfying results are achieved, while the classification still leaves room for improvement. Furthermore, the investigation of non-pathological factors reveals some parameters with an impact on the imaging data but the main driver for the data heterogeneity of different patients cannot be identified. The therapy monitoring approach does not allow a final conclusion if NIR-FOI is suitable for that task but it can be shown that even in the longitudinal data a normalization step needs to be included since the data across multiple visits is not directly comparable. Furthermore, the necessary steps for such a monitoring system are introduced. Finally, a well-working registration for combining NIR-FOI and MRI is presented. Although the different steps in this thesis achieve very different results, the groundwork for developing a comprehensive well-working analysis approach for the detection of PsA in NIR-FOI data is presented in this thesis.