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Spatiotemporal Reconstruction of the Breathing Function

This study introduces a novel method to reconstruct breathing patterns by preserving spatial heat distribution and temporal evolution, offering valuable insights for clinical applications and sleep studies. The methodology involves temporal registration, segmentation, and stacking, enhanced by phase correlation techniques and level set equations. Validation is conducted using ground-truth data, and preliminary results show promise for detecting abnormal breathing patterns. Future work aims to enhance image registration and segmentation for airflow analysis.

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Spatiotemporal Reconstruction of the Breathing Function

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  1. Spatiotemporal Reconstruction of the Breathing Function Duc Duong Advisor: Dr. IoannisPavlidis

  2. Motivation • A need of a less obtrusive sleep study • Virtual thermistor* • Preserves the temporal component: breathing waveform and rate • Loses spatial heat distribution * J. Fei and I. Pavlidis, “Virtual thermistor”, Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, pp. 250-3, August, 2007

  3. A New Approach – Spatiotemporal Reconstruction • Preserve spatial heat distribution at nostrils (or heat signature) • Temporal evolution (or changes) of heat signature’s boundaries • More information to clinical need

  4. Methodology - Overview Temporal Registration Segmentation Stacking Registration Segmentation Reference frame y y y y x x x Next temporal frame y x Stacking t x

  5. Methodology Temporal Registration Segmentation Stacking • To register thermal images to a fixed global reference frame • To retain only the evolution of heat signature at nostrils Solution: Phase correlating the Laplacians of two input thermal images Real Motion = Evolution + Body motion Phase Correlation Registration

  6. Methodology Temporal Registration Segmentation Stacking • To capture nostril region(s) whose spatial heat is changing by time • To constrain boundaries of captured regions in a temporaladvective relation Solution: Level set equation and level set curve

  7. Validation Temporal Registration Segmentation Stacking Registration positions/orientations are checked against ground-truth values Qualitative Analysis Quantitative Analysis Manual Transform: Rot. Ѳ = 14.48 Tran. tx = 4.40, ty = 2.24 Auto Realignment: Rot. Ѳ = 16 Tran. tx = 5, ty = 2 Auto Alignment: Rot. Ѳ = 16 Tran. tx = 5, ty = 2 Manual Transform: Rot. Ѳ = 14.48 Tran. tx = 4.40, ty = 2.24

  8. Validation Temporal Registration Segmentation Stacking • Six ground-truth sets of hand segmentation by three experts • Make use of PRI (Probability Rand Index*) to measure a consistency between auto-segmentation and ground-truth sets Hand Segmentation * R. Unnikrishnan and M. Hebert, “Measures of Similarity”, 7th IEEE Workshop on Applications of Computer Vision, January, 2005, pp. 394-400.

  9. Preliminary Results • Visualization of 3D cloud of heat changes

  10. Applications • Deliver the same information as virtual thermistor Normal Breathing Waveform Abnormal Airway Obstruction Mean temperature signal measure at left nostril Left nostril Left nostril

  11. Applications • Detect irregular breathing patterns A failure tissue part inside right nostril Failure tissues Failure tissues can not be identified from 1D waveform Abrupt breathing at right nostril Left nostril Right nostril

  12. Future Work • Improve the image registration • Improve the segmentation • Compute the airflow velocity and the volume of exchanged gas Thank you Q & A

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