Yushui Han1, Ahmed Ibrahim Ahmed1, Charles Hayden2, Aaron K Jung2, Jean Michel Saad1, Bruce Spottiswoode2, Faisal Nabi1, Mouaz H Al-Mallah3,4. 1. Houston Methodist Debakey Heart and Vascular Center, 6550 Fannin Street, Smith Tower-Suite 1801, Houston, TX, 77030, USA. 2. Siemens Medical Solutions USA, Inc., Knoxville, TN, USA. 3. Houston Methodist Debakey Heart and Vascular Center, 6550 Fannin Street, Smith Tower-Suite 1801, Houston, TX, 77030, USA. mal-mallah@houstonmethodist.org. 4. Medicine and Cardiology, Weill Cornell Medical College, New York, USA. mal-mallah@houstonmethodist.org.
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.
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.