Noise propagation in resolution modeled PET imaging and its impact on detectability

Abstract

PET imaging is affected by a number of resolution degrading phenomena, including positron range, photon non-collinearity and inter-crystal blurring. An approach to this issue is to model some or all of these effects within the image reconstruction task, referred to as resolution modeling (RM). This approach is commonly observed to yield images of higher resolution and subsequently contrast, and can be thought of as improving the modulation transfer function (MTF). Nonetheless, RM can substantially alter the noise distribution. In this work, we utilize noise propagation models in order to accurately characterize the noise texture of reconstructed images in the presence of RM. Furthermore we consider the task of lesion or defect detection, which is highly determined by the noise distribution as quantified using the noise power spectrum (NPS). Ultimately, we use this framework to demonstrate why conventional trade-off analyses (e.g. contrast vs. noise, using simplistic noise metrics) do not provide a complete picture of the impact of RM and that improved performance of RM according to such analyses does not necessarily translate to the superiority of RM in detection task performance.”

Comparative analysis of iterative reconstruction algorithms with resolution recovery and time of flight modeling for 18F-FDG cardiac PET: A multi-center phantom study

“…It has been suggested that the noise propagation associated with RM is more complex than what simplistic metrics are able to adequately characterize. 21 Enhanced quantification does not necessarily translate into enhanced defect detection, and therefore, each has to be studied on its own. In this study, we evaluated the effects of PSF, TOF, and combined TOF ?…”

Resolution modeling enhances PET imaging

“…Dualmetric resolution (contrast) vs. noise trade-off analyses commonly depict improved curves for RM whether noise is defined as σ spatial or σ ensemble (though to a lesser extent in the latter case, as explained above). Nonetheless, as demonstrated recently, 9 such simplified analyses do not properly capture the impact of the modified noise texture in PET images. In fact, detection task performance can be expressed as a function of the noise power spectrum (NPS), which is amplified at midfrequencies with RM and competes against the RM-enhanced modulation transfer function (MTF).…”

“…RM has been shown to reduce voxel variances but increase intervoxel correlations. 6,9 The first effect decreases both σ spatial and σ ensemble , while the latter further decreases σ spatial , but shifts σ ensemble in the opposite direction. 6 Subsequently, σ spatial is reduced in RM, but σ ensemble can increase especially for small ROIs.…”

Towards continualized task-based resolution modeling in PET imaging

“…It reduces voxel variances, while increasing inter-voxel correlations [5][6][7][8]. These two effects impact noise differently based on the very definition of noise [6].…”

“…(2) The noise texture is more thoroughly captured by the noise power spectrum (NPS), which in fact determines detection task performance [1]: with RM, the NPS is amplified at frequencies in which the modulation transfer function (MTF) is improved, and turns out to compete against it, limiting detection performance in RM [8].…”