Motion estimation for nuclear medicine

A probabilistic approach

Rhodri Smith, Ashrani Aizzuddin Abd Rahni, John Jones, Fatemeh Tahavori, Kevin Wells

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Accurate, Respiratory Motion Modelling of the abdominal-thoracic organs serves as a pre-requisite for motion correction of Nuclear Medicine (NM) Images. Many respiratory motion models to date build a static correspondence between a parametrized external surrogate signal and internal motion. Mean drifts in respiratory motion, changes in respiratory style and noise conditions of the external surrogate signal motivates a more adaptive approach to capture non-stationary behavior. To this effect we utilize the application of our novel Kalman model with an incorporated expectation maximization step to allow adaptive learning of model parameters with changing respiratory observations. A comparison is made with a popular total least squares (PCA) based approach. It is demonstrated that in the presence of noisy observations the Kalman framework outperforms the static PCA model, however, both methods correct for respiratory motion in the computational anthropomorphic phantom to < 2mm. Motion correction performed on 3 dynamic MRI patient datasets using the Kalman model results in correction of respiratory motion to 3mm.

Original languageEnglish
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
PublisherSPIE
Volume9034
ISBN (Print)9780819498274
DOIs
Publication statusPublished - 2014
EventMedical Imaging 2014: Image Processing - San Diego, CA
Duration: 16 Feb 201418 Feb 2014

Other

OtherMedical Imaging 2014: Image Processing
CitySan Diego, CA
Period16/2/1418/2/14

Fingerprint

Nuclear medicine
nuclear medicine
Nuclear Medicine
Motion estimation
Passive Cutaneous Anaphylaxis
Magnetic resonance imaging
static models
Least-Squares Analysis
organs
learning
Noise
Thorax
Learning

Keywords

  • 4D MRI
  • Bayesian Estimation
  • Nuclear Medicine
  • Registration
  • Respiratory motion

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Smith, R., Abd Rahni, A. A., Jones, J., Tahavori, F., & Wells, K. (2014). Motion estimation for nuclear medicine: A probabilistic approach. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 9034). [90342Z] SPIE. https://doi.org/10.1117/12.2044141

Motion estimation for nuclear medicine : A probabilistic approach. / Smith, Rhodri; Abd Rahni, Ashrani Aizzuddin; Jones, John; Tahavori, Fatemeh; Wells, Kevin.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 9034 SPIE, 2014. 90342Z.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Smith, R, Abd Rahni, AA, Jones, J, Tahavori, F & Wells, K 2014, Motion estimation for nuclear medicine: A probabilistic approach. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 9034, 90342Z, SPIE, Medical Imaging 2014: Image Processing, San Diego, CA, 16/2/14. https://doi.org/10.1117/12.2044141
Smith R, Abd Rahni AA, Jones J, Tahavori F, Wells K. Motion estimation for nuclear medicine: A probabilistic approach. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 9034. SPIE. 2014. 90342Z https://doi.org/10.1117/12.2044141
Smith, Rhodri ; Abd Rahni, Ashrani Aizzuddin ; Jones, John ; Tahavori, Fatemeh ; Wells, Kevin. / Motion estimation for nuclear medicine : A probabilistic approach. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 9034 SPIE, 2014.
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