Whitehead, Alexander;
Su, Kuan-Hao;
Emond, Elise;
Biguri, Ander;
Machado, Maria;
Porter, Joanna;
Garthwaite, Helen;
... Thielemans, Kris; + view all
(2022)
Data Driven Surrogate Signal Extraction for Dynamic PET Using Selective PCA.
In:
Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) 2022.
(pp. pp. 1-4).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
Respiratory motion correction is beneficial in PET. Methods of motion correction include gated reconstruction, where the acquisition is binned, based on a respiratory trace. To acquire these respiratory traces, an external device, like the Real Time Position Management System, or a data driven method, such as PCA, can be used. Data driven methods have the advantage that they are non-invasive, and can be performed post-acquisition. However, data driven methods have the disadvantage that they are adversely affected by the tracer kinetics of a dynamic acquisition. This work seeks to evaluate several adaptions of the PCA method, through which it can be used with dynamic data. The methods explored in this work include, using a moving window (similar to the KRG method of Schleyer et al. (PMB 2014)), extrapolation of the principal component from later time points to earlier time points, as well as a method to select and combine multiple respiratory components. The respiratory traces acquired, were evaluated on 21 patients, by calculating their correlation with a Real Time Position Management System surrogate signal. The results indicate that all methods produce better surrogate signals than when applying static PCA to dynamic data. Extrapolating a late principal component, produced more promising results than using a moving window, and selecting and combining components held benefits for all methods.
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