Projects per year
Abstract
Goal: This study introduces Temporal Reliability and Accuracy via Correlation Enhanced Registration (TRACER), a novel image processing pipeline that addresses motion artefacts in real-time Fluorescence Lifetime Imaging (FLIm) data for in-vivo pulmonary Optical Endomicroscopy (OEM). Its primary objective is to improve the accuracy and reliability of FLIm image sequences. Methods: The proposed TRACER pipeline comprises a comprehensive sequence of pre-processing steps and a novel registration approach. This includes the removal of uninformative frames and motion characterisation through dense optical flow, followed by a tracking-based Normalised Cross Correlation image registration method leveraging Channel and Spatial Reliability Tracker for precise alignment. Results: The complete TRACER pipeline delivers significant performance improvements, with 20% to 30% enhancement across different metrics for all tested registration methods. In particular, the unique TRACER registration approach outperforms state-of-theart methods in image registration performance and achieves an order-of-magnitude faster runtime than the next best-performing approach. Conclusion: By addressing motion artefacts through its integrated pre-processing and novel registration strategy, TRACER offers a robust solution that ensures improved image quality and real-time feasibility for FLIm data processing in in-vivo pulmonary OEM.
Original language | English |
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Pages (from-to) | 432 - 441 |
Journal | IEEE Open Journal of Engineering in Medicine and Biology |
Volume | 6 |
Early online date | 8 Apr 2025 |
DOIs | |
Publication status | E-pub ahead of print - 8 Apr 2025 |
Keywords / Materials (for Non-textual outputs)
- Fluorescence Lifetime Imaging
- Medical Image Registration
- Motion Compensation
- Optical Endomicroscopy
Fingerprint
Dive into the research topics of 'Motion Compensation in Pulmonary Fluorescence Lifetime Imaging: An Image Processing Pipeline for Artefact Reduction and Clinical Precision'. Together they form a unique fingerprint.Projects
- 2 Finished
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Machine Learning Techniques for Evaluating Disease and Drug Effectiveness in Fibre-Bundle Endo Microscopy Systems
Hopgood, J. (Principal Investigator)
UK industry, commerce and public corporations
1/03/21 → 28/02/25
Project: Research
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(HIPS) - Next-generation sensing for human in vivo pharmacology- accelerating drug development in inflammatory diseases
Dhaliwal, K. (Principal Investigator), Akram, A. (Co-investigator), Bradley, M. (Co-investigator), Henderson, R. (Co-investigator), Hopgood, J. (Co-investigator) & Walmsley, S. (Co-investigator)
Engineering and Physical Sciences Research Council
1/10/19 → 30/09/23
Project: Research
Research output
- 1 Article
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Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: A Review
Gouzou, D., Taimori, A., Haloubi, T., Finlayson, N., Wang, Q., Hopgood, J. R. & Vallejo, M., 1 Apr 2024, In: Methods and Applications in Fluorescence. 12, 2, 022001.Research output: Contribution to journal › Article › peer-review
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