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Evaluation of a commercial deformable image registration algorithm for dual-energy CT processing.

Jessie Y Huang1,2, Michael J Lawless1, Charles K Matrosic2

  • 1Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA.

Journal of Applied Clinical Medical Physics
|July 26, 2020
PubMed
Summary

This study introduces a new quality assurance method for dual-energy computed tomography (DECT) deformable image registration. The Syngo algorithm is accurate for abdominal imaging but caution is advised for areas with sliding motion.

Keywords:
TG-132deformable image registrationdual-energy CTquality assurance

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Area of Science:

  • Medical Imaging
  • Radiology
  • Image Processing

Background:

  • Dual-energy computed tomography (DECT) techniques often require deformable image registration to correct for motion artifacts between low and high energy data acquisitions.
  • Current DECT software lacks tools for assessing registration accuracy or exporting deformed images, hindering quality assurance (QA).

Purpose of the Study:

  • To present a novel methodology for evaluating the accuracy of DECT deformable image registration.
  • To quantify the impact of registration errors on the final dual-energy images.

Main Methods:

  • Evaluation of the Syngo deformable registration algorithm using both a deformable abdomen phantom and a rigid phantom simulating thoracic sliding motion.
  • Imaging was performed using sequential 80 and 140 kVp scans with motion introduced between acquisitions.
  • Focus on quantifying end-product dual-energy image accuracy due to limitations in exporting deformed images from the software.

Main Results:

  • The Syngo algorithm demonstrated high accuracy for the abdomen phantom, with a mean registration error of 0.4 mm and Dice Similarity Coefficients (DSCs) > 0.90 for multiple organs.
  • Performance was poorer with rigid sliding motion, showing noticeable errors and DSCs as low as 0.41 for iodine rods.
  • Local iodine concentration errors in misregistered areas exceeded 3 mg/mL.

Conclusions:

  • This study establishes the first methodology for DECT image registration QA utilizing commercial software.
  • The Syngo algorithm is suitable for abdominal imaging with minimal motion, such as for the pancreas and liver.
  • Quantitative measurements from dual-energy images acquired in areas with significant sliding motion should be approached with caution.