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Change detection in sparse repeat CT scans with non-rigid deformations.

Naomi Shamul1, Leo Joskowicz1

  • 1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jeruzalem, Israel.

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|October 25, 2021
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Summary
This summary is machine-generated.

This study introduces a novel method for automatically detecting changes in repeat CT scans, even with patient movement and anatomical shifts. The technique accurately identifies genuine changes, aiding radiologists and potentially reducing the need for repeat scans.

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3D Radon spacelow-dose sparse repeat CT scanningnon-rigid transformationrepeat scan change detection

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Imaging

Background:

  • Interpreting changes in follow-up CT scans is challenging due to patient repositioning and anatomical deformations.
  • Reconstructing repeat CT scans for comparison is often necessary but limits low-dose sparse-view scanning.

Purpose of the Study:

  • To develop a method for automatic change detection in sparse-view repeat CT scans.
  • To address challenges posed by non-rigid anatomical deformations without image reconstruction.

Main Methods:

  • Utilizes sparse sinogram data from two CT scans to differentiate true changes from anatomical variations.
  • Employs Radon space non-rigid registration for scan alignment.
  • Identifies and back-projects rays crossing regions of interest to generate a likelihood map for change detection.

Main Results:

  • Achieved a mean recall rate >86% and precision rate >83% for detecting various simulated changes in clinical lung and liver CT scans.
  • Outperformed image space methods, especially for small, low-contrast changes, demonstrating superior recall and precision.

Conclusions:

  • The developed method enables automatic change detection in sparse-view repeat CT scans with non-rigid deformations.
  • Assists radiologists by highlighting changed regions and may eliminate the need for high-quality repeat scans when no changes are present.