TY - JOUR
T1 - Change in positron emission tomography perfusion imaging quality with a data-driven motion correction algorithm
AU - Han, Yushui
AU - Ahmed, Ahmed Ibrahim
AU - Hayden, Charles
AU - Jung, Aaron K.
AU - Saad, Jean Michel
AU - Spottiswoode, Bruce
AU - Nabi, Faisal
AU - Al-Mallah, Mouaz H.
N1 - Funding Information:
This work was funded by a grant from Houston Methodist-Siemens Healthineers Research Collaboration. Dr. Mouaz H Al-Mallah receives research support from Siemens. Charles Hayden, Bruce Spottiswoode, and Aaron K Jung are employed by Siemens Medical Solutions USA, Inc. No other relevant disclosures.
Publisher Copyright:
© 2022, The Author(s) under exclusive licence to American Society of Nuclear Cardiology.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - Data-driven motion correction
KW - Positron emission tomography
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U2 - 10.1007/s12350-021-02902-5
DO - 10.1007/s12350-021-02902-5
M3 - Article
C2 - 35275348
AN - SCOPUS:85126113998
SN - 1071-3581
VL - 29
SP - 3426
EP - 3431
JO - Journal of Nuclear Cardiology
JF - Journal of Nuclear Cardiology
IS - 6
ER -