An automated data processing tool for Hyperpolarized Nuclear Magnetic Resonance: advancing precision medicine research.

In biomedical research, a comprehensive approach to detecting and monitoring diseases during different stages and measuring metabolic processes is essential for early diagnosis, managing chronic illnesses, and improving health outcomes. Precision medicine exemplifies this approach by tailoring treatment to individual patients, thereby enabling more accurate diagnoses, better disease prediction, and personalized therapies. Central to the advancement of precision medicine are innovative technologies such as Hyperpolarized Nuclear Magnetic Resonance (HP-NMR) spectroscopy, which significantly enhances the sensitivity of molecular analysis. HP-NMR facilitates real-time, non-invasive observation of molecular processes, providing unprecedented insights into dynamic biological phenomena. However, the complex signals generated by HP-NMR require efficient and accurate pre-processing methods to extract meaningful information. Addressing these challenges is essential for fully realizing HP-NMR's potential in advancing precision medicine. To address these challenges, this thesis focuses on developing an automated tool for processing HP-NMR data. The primary goal is to overcame the inefficiency and potential inaccuracies associated with manual NMR data processing. Manual methods are time-consuming and prone to human error, making them unreliable for handling the intricate and voluminous data produced by HP-NMR spectroscopy. Hence, my primary research question is: "How can an automated tool improve the accuracy and efficiency of preprocessing HP-NMR spectra?" To answer this question, the research explores several methodologies, including phase correction and noise reduction techniques. I evaluated three distinct approaches for phase correction: a coarse and fine tuning strategy, entropy minimization, and absolute spectrum comparison. Similarly, I tested three techniques for noise reduction: deep learning autoencoders, singular value decomposition (SVD), and moving average filters. I tested these methods separately to determine their performance in their respective tasks. The results indicate that the automated tool provides the accuracy and efficiency of NMR data processing. Each phase correction and noise reduction method shows varying strengths and limitations, but collectively, they contribute to a more reliable and standardized preprocessing workflow. The tool's ability to improve signal clarity and accuracy holds promise for advancing precision medicine by enabling better diagnostic and therapeutic decisions. Hence, this thesis demonstrates the effectiveness of an automated tool in preprocessing HP-NMR spectra, addressing the challenges of noise and phase distortion. Future research should focus on refining these methodologies and exploring their applications in diverse NMR datasets to further enhance the tool's robustness and applicability in precision medicine.