Eintrag in der Universitätsbibliographie der TU Chemnitz
Volltext zugänglich unter
URN: urn:nbn:de:bsz:ch1-qucosa2-1016282
Hafsa, Mariem
Kanoun, Olfa (Prof. Dr.); Da Silva, Marco Jose (Prof. Dr.)
Advanced Image Reconstruction for Electrical Impedance Tomography via Ensemble Learning with Consideration of Measurement Data Quality and Prior-Knowledge-Guided Denoising
Kurzfassung in englisch
Electrical Impedance Tomography (EIT) is a non-invasive imaging method with significant potential for lung state assessment, yet constrained by insufficient image quality. Image reconstruction is a non-linear, ill-posed inverse problem, highly sensitive to measurement perturbations. Existing methods fail to address the dual challenge of conductivity accuracy and structural boundary preservation simultaneously, while post-processing approaches remain computationally intensive and lack prior-knowledge integration, causing persistent residual artifacts.This thesis introduces a holistic framework tackling multiple reconstruction stages. A Gaussian Process regression-based pre-processing achieves 99.97% Mean Squared Error reduction, improving Signal-to-Noise Ratio from 1 dB to 36 dB. An ensemble learning strategy combines a 1D-Residual-CNN-GRU optimized for conductivity accuracy with an enhanced U-Net for structural preservation, integrated via Ridge regression stacking, yielding 3.7% Image Correlation Coefficient (ICC) improvement and 60.8% Relative Image Error (RIE) reduction over state-of-the-art methods. A prior-knowledge-guided post-processing applies targeted denoising, achieving 2.9% ICC improvement and 16.7% RIE reduction. Extended to lung state assessment, the ensemble module achieves 2.9% ICC improvement and 79.3% RIE reduction. Experimental validation on water tank setups and custom PCB thoracic phantoms confirms robustness under real measurement conditions.
| Universität: | Technische Universität Chemnitz | |
| Förderung: | Sonstiges | |
| Institut: | Professur Mess- und Sensortechnik | |
| Fakultät: | Fakultät für Elektrotechnik und Informationstechnik | |
| Dokumentart: | Dissertation | |
| Betreuer: | Kanoun, Olfa (Prof. Dr.); Essoukri Ben Amara, Najoua (Prof. Dr.) | |
| ISBN/ISSN: | 978-3-96100-308-2 (print), 978-3-96100-309-9 (online) | |
| DOI: | doi:10.51382/978-3-96100-309-9 | |
| URL/URN: | https://nbn-resolving.org/urn:nbn:de:bsz:ch1-qucosa2-1016282 | |
| Quelle: | Chemnitz : Universitätsverlag Chemnitz, 2026. - 278 S. - Scientific Reports on Measurement and Sensor Technology ; Volume 41 | |
| SWD-Schlagwörter: | Impedanztomografie , Bildrekonstruktion , Künstliche Intelligenz , Kompensationsmethode , Rauschunterdrückung , Lunge , Diagnose | |
| Freie Schlagwörter (Englisch): | Electrical Impedance Tomography , measurement deviation compensation , advanced ensemble learning , prior-knowledge-guided denoising , lung state assessment | |
| DDC-Sachgruppe: | Technik, Medizin, angewandte Wissenschaften, Ingenieurwissenschaften | |
| Sprache: | englisch | |
| Tag der mündlichen Prüfung | 16.12.2025 | |
| OA-Lizenz | CC BY 4.0 |
