Optimisation of quantitative lung SPECT applied to mild COPD: a Monte Carlo-based analysis
2014 (English)Manuscript (preprint) (Other academic)
The amount of inhomogeneities in a single photon emission computed tomography (SPECT) lung image, caused by reduced ventilation in lung regions affected by chronic obstructive pulmonary disease (COPD), is correlated to disease advancement. A quantitative analysis method, the CVT-method, measuring these inhomogeneities was proposed in earlier work (Norberg et al., 2013). To detect mild COPD, which is a difficult task, optimized parameter values are needed. In this work, the CVT-method was optimized with respect to the parameter values of acquisition, reconstruction and analysis. The ordered subset expectation maximization (OSEM) algorithm was used for reconstructing the lung SPECT images. As a first step towards clinical application of the CVT-method in detecting mild COPD, this study was based on simulated SPECT images of an advanced anthropomorphic lung phantom including respiratory and cardiac motion, where the mild COPD lung had an overall ventilation reduction of 5%. The largest separation between healthy and mild COPD lung images as determined using the CVT-measure of ventilation inhomogeneity and 125 MBq 99mTc was obtained using a low-energy high-resolution collimator and a Butterworth postfilter with a cut-off frequency of 0.6-0.7 cm-1. Sixty-four reconstruction updates should be used when the whole lung is analysed and for the reduced lung a greater number of updates is needed.
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IdentifiersURN: urn:nbn:se:liu:diva-106665OAI: oai:DiVA.org:liu-106665DiVA: diva2:717915