Fourier block noise reduction: an adaptive filter for reducing Poisson noise in scintigraphic images

Abstract

Both qualitative and quantitative analysis in nuclear medicine can be undermined by Poisson noise in low-count clinical images. Whilst the conventional smoothing filters are typically used do reduce noise, they also degrade the image structure. Fourier block noise reduction (FBNR) is an adaptive filtering approach, which attempts to reduce image noise and maintain image resolution and structure. Although a degree of automated flexibility is possible using conventional stationary pre-filtering, e.g. using a total image count-dependent Metz filter, resolution and contrast is degraded across the image. Adaptive non-stationary filtering has been applied by others in an attempt to maintain structure whilst reducing noise: instead of analysing the whole image, only a subset is used to determine each pixel's correction. Whilst the new software algorithm FBNR shares some common components with other adaptive non-stationary filters, it expressly includes the Poisson noise model within a simple and robust algorithm that can be applied to a diverse range of clinical studies. No additional artefacts were seen post-application of FBNR during evaluation using simulated and clinical images. Mean normalised error values indicate FBNR processing is equivalent to obtaining an unprocessed image with at least 2.5 times the number of counts.