Real-time quality control in optoacoustic mesoscopy for enhancing data quality and standardization in clinical studies.
Authors
Affiliations (18)
Affiliations (18)
- Chair of Biological Imaging, Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine and Health & School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- Institute of Biological and Medical Imaging, Bioengineering Center, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute of AI for Health, Helmholtz Zentrum München, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Otfried-Müller-Str. 10, Tübingen 72076, Germany.
- Internal Medicine IV, Department of Diabetology, Endocrinology and Nephrology, University of Tübingen, Otfried-Müller-Str. 10, Tübingen 72076, Germany.
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Otfried-Müller-Str. 10, Tübingen 72076, Germany.
- Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry, Eberhard-Karls University Tübingen, Auf der Morgenstelle 8, Tübingen 72076, Germany.
- Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections, University of Tübingen, Tübingen, Germany.
- M3 Research Center, Malignome, Metabolome, Microbiome, Otfried-Müller-Str. 37, Tübingen 72076, Germany.
- Diabetes & Obesity Theme, School of Cardiovascular and Metabolic Medicine & Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK.
- DZHK (German Centre for Cardiovascular Research), Partner site Munich Heart Alliance, Munich, Germany.
- Chair for Computer Aided Medical Procedures & Augmented Reality, Technical University of Munich, Munich, Germany.
- Clinic for Vascular Surgery, Helios Klinikum München West, Munich, Germany.
- Clinic and Polyclinic for Vascular and Endovascular Surgery, TUM University Hospital, Hospital Rechts der Isar, Technical University of Munich, Munich, Germany.
- Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany.
- Forschergruppe Diabetes, Technical University Munich, Klinikum Rechts Der Isar, Munich, Germany.
- Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany.
Abstract
Raster scan optoacoustic mesoscopy (RSOM) has matured as a medical imaging modality that enables unique high-resolution visualization of optical contrast at depths of several millimeters. Compared with other optical methods, optoacoustics is less affected by photon scattering, enabling superior imaging of dermatological, cardiometabolic, and other conditions. A critical requirement for clinical adoption is the development of methodology that ensures quality control and standardization across subjects, time points, and acquisition environments. We present a machine-learning-based automated real-time quality control method for RSOM using signal-derived metrics for noise and motion. The model was trained and evaluated on 1725 clinical RSOM scans benchmarked against visually perceived image quality ratings from eight experts. The method enables real-time feedback during acquisition to identify suboptimal scans and support standardized high-quality data acquisition. We discuss the impact of the method on clinical RSOM deployment, and the cost benefits achieved through data standardization.