Abstract
This paper presents improved autoregressive modelling (AR) to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-AR method). The performance of this method was compared with filtered back projection (FBP) preceded by Butterworth filtering (BW-FBP method) and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method). A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR%) and the full width at half maximum (FWHM) of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot objects.
Highlights
Numerous methods for removing noise from SPECT images have been proposed [1, 2]
An autoregressive modelling (AR) filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-AR method)
The performance of this method was compared with filtered back projection (FBP) preceded by Butterworth filtering (BW-FBP method) and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method)
Summary
Numerous methods for removing noise from SPECT images have been proposed [1, 2]. This indicates the difficulty of the task. Noise removal can be performed before reconstruction (prefiltering), during reconstructions or after reconstruction (postfiltering). Earlier we introduced an adaptive autoregressive (AR) filter to reduce noise in scintigraphic planar images or projection images of a SPECT study [4]. The AR filter was further improved to reduce noise from the projection images and from three-dimensionally reconstructed data. It is important to apply the best AR filter to the projection data of SPECT, because a small change in the projection data may cause a large change in the estimated transaxial image [5]. The methodical comparison was carried out using a threedimensional mathematical cylinder phantom (3D-MAC) [6], and it was illustrated with patient data
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