Evaluating image denoising methods in myocardial perfusion single photon emission computed tomography (SPECT) imaging

Abstract

The statistical nature of single photon emission computed tomography (SPECT) imaging, due to the Poisson noise effect, results in the degradation of image quality, especially in the case of lesions of low signal-to-noise ratio (SNR). A variety of well-established single-scale denoising methods applied on projection raw images have been incorporated in SPECT imaging applications, while multi-scale denoising methods with promising performance have been proposed. In this paper, a comparative evaluation study is performed between a multi-scale platelet denoising method and the well-established Butterworth filter applied as a pre- and post-processing step on images reconstructed without and/or with attenuation correction. Quantitative evaluation was carried out employing (i) a cardiac phantom containing two different size cold defects, utilized in two experiments conducted to simulate conditions without and with photon attenuation from myocardial surrounding tissue and (ii) a pilot-verified clinical dataset of 15 patients with ischemic defects. Image noise, defect contrast, SNR and defect contrast-to-noise ratio (CNR) metrics were computed for both phantom and patient defects. In addition, an observer preference study was carried out for the clinical dataset, based on rankings from two nuclear medicine clinicians. Without photon attenuation conditions, denoising by platelet and Butterworth post-processing methods outperformed Butterworth pre-processing for large size defects, while for small size defects, as well as with photon attenuation conditions, all methods have demonstrated similar denoising performance. Under both attenuation conditions, the platelet method showed improved performance with respect to defect contrast, SNR and defect CNR in the case of images reconstructed without attenuation correction, however not statistically significant (p > 0.05). Quantitative as well as preference results obtained from clinical data showed similar performance of the denoising methods studied. In conclusion, the multi-scale platelet denoising method applied on raw projection images provides more efficient noise reduction while preserving image quality in a myocardial phantom SPECT imaging as compared to the Butterworth filter applied either on projection or reconstructed images. However, this trend in favour of the platelet denoising method was not observed on clinical data reconstructed either without or with attenuation correction.

A multiscale and multiblock fuzzy C-means classification method for brain MR images

“…Linear filters can reduce noise but may result in the degradation of image contrast and detail. 25,26 Bilateral filtering can overcome this drawback by introducing a partial edge detection step into the filtering so as to encourage intraregion smoothing and preserve the interregion edge. A bilateral filter replaces the pixel value of a neighborhood center with the average of similar and nearby pixel values.…”

Consensus image method for unknown noise removal

“…For instance, MRI, specifically MR magnitude image, are mainly characterized by Rician noise, although this noise is dependent on the number of coils or the reconstruction method (AjaFernandez et al (2009)). Furthermore CT, PET, SPECT or astronomical images are identified by Poisson noise (Suzuki (1985); Rosenthal et al (1995); Hsieh (1998); Molina et al (2001); La Riviere and Billmire (2005); Skiadopoulos et al (2009)).…”

“…The restoration of degraded images is an important task widely studied in computer vision (Rudin et al (1992); You et al (1996); Molina et al (2001); Buades et al (2005); La Riviere and Billmire (2005); Srinivasan and Ebenezer (2007); Aja-Fernandez et al (2008); Goossens et al (2008); Borsdorf et al (2008); Skiadopoulos et al (2009); Deledalle et al (2010)). It always received a lot of attention from many researchers of different fields.…”

“…It always received a lot of attention from many researchers of different fields. Denoising is one of the most fundamental image restoration techniques (Molina et al (2001); Buades et al (2005); Srinivasan and Ebenezer (2007); Aja-Fernandez et al (2008); Goossens et al (2008); Skiadopoulos et al (2009) ;Deledalle et al (2010)), due to random distortions which make it difficult to perform any required image processing. The desired goals of a denoising algorithm are to completely remove noise, while effective information (edge, corner, texture and contrast...) is preserved, at the same time that artifacts do not appear.…”

“…In the particular case of medical images, in computer tomography (CT), the decay of the signal is better modelled with a Poisson distribution (Suzuki (1985); Hsieh (1998); La Riviere and Billmire (2005)). Other medical images, as single-photon emission computed tomography (SPECT) or positron emission tomography (PET), can also be well modelled with a Poisson distribution (Rosenthal et al (1995); Skiadopoulos et al (2009)). In the case of magnetic resonance images (MRI), a Rice distribution better models the abnormalities in the image for a single-coil (Bernstein et al (1989); Aja-Fernandez et al (2008)).…”