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Related Experiment Video

Updated: Jan 25, 2026

Breathing-controlled Electrical Stimulation BreEStim for Management of Neuropathic Pain and Spasticity
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Higher-order singular value decomposition-based lung parcellation for breathing motion management.

Samadrita Roy Chowdhury1, Joyita Dutta1,2

  • 1University of Massachusetts Lowell, Department of Electrical and Computer Engineering, Lowell, Massachusetts, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|May 9, 2019
PubMed
Summary

Respiratory motion blurs lung PET scans, affecting accuracy. This study introduces a lung parcellation framework using 4D MR imaging and HOSVD to guide marker placement, improving quantitative PET accuracy for lung cancer management.

Keywords:
deformation tensorhigher-order singular value decompositioninternal–external correlationmagnetic resonance imagingpositron emission tomographyrespiratory motion tracking

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Anatomy

Background:

  • Respiratory motion artifacts degrade quantitative accuracy in pulmonary Positron Emission Tomography (PET) imaging.
  • Current motion correction methods often rely on external surrogates in the absence of precise internal deformation data.

Purpose of the Study:

  • To develop a group-level lung parcellation framework to guide the placement of motion-monitoring markers for improved PET imaging.
  • To identify synchronous internal and surface regions for effective motion tracking.

Main Methods:

  • Utilized higher-order singular value decomposition (HOSVD) on deformation tensors derived from 4D-magnetic resonance (MR) imaging.
  • Employed nonrigid registration of gated MR images to compute deformation tensors.
  • Performed group-level clustering of voxels and leave-one-out cross-validation for robustness assessment.

Main Results:

  • The HOSVD-based framework successfully identified synchronous areas within the torso and on the skin surface.
  • Parcellation results demonstrated high consistency and overlap across subjects and varying parameters.
  • PET simulations indicated that selecting synchronous regions of interest (ROIs) can improve quantitative accuracy (recovery coefficient).

Conclusions:

  • A data-driven lung parcellation framework based on 4D MR imaging and HOSVD can effectively guide motion monitoring for pulmonary PET.
  • This approach enhances the accuracy of quantitative PET imaging, crucial for lung cancer management.
  • Guided motion monitoring is vital for optimizing pulmonary PET imaging interpretation.