Change in positron emission tomography perfusion imaging quality with a data-driven motion correction algorithm

Yushui Han, Ahmed Ibrahim Ahmed, Charles Hayden, Aaron K. Jung, Jean Michel Saad, Bruce Spottiswoode, Faisal Nabi, Mouaz H. Al-Mallah

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Cardiac motion frequently reduces the interpretability of PET images. This study utilized a prototype data-driven motion correction (DDMC) algorithm to generate corrected images and compare DDMC images with non-corrected images (NMC) to evaluate image quality and change of perfusion defect size and severity. Methods: Rest and stress images with NMC and DDMC from 40 consecutive patients with motion were rated by 2 blinded investigators on a 4-point visual ordinal scale (0: minimal motion; 1: mild motion; 2: moderate motion; 3: severe motion/uninterpretable). Motion was also quantified using Dwell Fraction, which is the fraction of time the motion vector shows the heart to be within 6 mm of the corrected position and was derived from listmode data of NMC images. Results: Minimal motion was seen in 15% of patients, while 40%, 30%, and 15% of patients had mild moderate and severe motion, respectively. All corrected images showed an improvement in quality and were interpretable after processing. This was confirmed by a significant correlation (Spearman’s correlation coefficient 0.626, P < .001) between machine measurement of motion quantification and physician interpretation. Conclusion: The novel DDMC algorithm improved quality of cardiac PET images with motion. Correlation between machine measurement of motion quantification and physician interpretation was significant.

Original languageEnglish (US)
JournalJournal of Nuclear Cardiology
DOIs
StateAccepted/In press - 2022

Keywords

  • Data-driven motion correction
  • Positron emission tomography

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

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