Filtering in SPECT image reconstruction

Department of Radiology, Radiation Physics Unit, University of Athens, 76, Vas. Sophias Ave., Athens 11528, Greece.
International Journal of Biomedical Imaging 06/2011; 2011(693795):693795. DOI: 10.1155/2011/693795
Source: PubMed


Single photon emission computed tomography (SPECT) imaging is widely implemented in nuclear medicine as its clinical role in the diagnosis and management of several diseases is, many times, very helpful (e.g., myocardium perfusion imaging). The quality of SPECT images are degraded by several factors such as noise because of the limited number of counts, attenuation, or scatter of photons. Image filtering is necessary to compensate these effects and, therefore, to improve image quality. The goal of filtering in tomographic images is to suppress statistical noise and simultaneously to preserve spatial resolution and contrast. The aim of this work is to describe the most widely used filters in SPECT applications and how these affect the image quality. The choice of the filter type, the cut-off frequency and the order is a major problem in clinical routine. In many clinical cases, information for specific parameters is not provided, and findings cannot be extrapolated to other similar SPECT imaging applications. A literature review for the determination of the mostly used filters in cardiac, brain, bone, liver, kidneys, and thyroid applications is also presented. As resulting from the overview, no filter is perfect, and the selection of the proper filters, most of the times, is done empirically. The standardization of image-processing results may limit the filter types for each SPECT examination to certain few filters and some of their parameters. Standardization, also, helps in reducing image processing time, as the filters and their parameters must be standardised before being put to clinical use. Commercial reconstruction software selections lead to comparable results interdepartmentally. The manufacturers normally supply default filters/parameters, but these may not be relevant in various clinical situations. After proper standardisation, it is possible to use many suitable filters or one optimal filter.

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    • "We consider these deleted Gaussians as a reference region because almost all of them are located in areas with a low uptake value, i.e. the occipital cortex. Thus, GMM is used as a filtering strategy to remove artifacts and noise [30] [31], preserving the image details after the preprocessing stage. The proposed method was implemented using Matlab software, as well as, the experiments carried out to evaluate it. "
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    ABSTRACT: This paper presents a novel method for intensity normalization of DaTSCAN SPECT brain images. The proposed methodology is based on Gaussian mixture models (GMMs) and considers not only the intensity levels, but also the coordinates of voxels inside the so-defined spatial Gaussian functions. The model parameters are obtained according to a maximum likelihood criterion employing the expectation maximization (EM) algorithm. First, an averaged control subject image is computed to obtain a threshold-based mask that selects only the voxels inside the skull. Then, the GMM is obtained for the DaTSCAN-SPECT database, performing space quantization by populating it with Gaussian kernels whose linear combination approximates the image intensity. According to a probability threshold that measures the weight of each kernel or “cluster” in the striatum area, the voxels in the non-specific region are intensity-normalized by removing clusters whose likelihood is negligible.
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    • "Numerous methods for removing noise from SPECT images have been proposed [1] [2]. This indicates the difficulty of the task. "
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    Full-text · Article · Jun 2015 · Computational and Mathematical Methods in Medicine
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    • "The cutoff frequency relative to Nyquist frequency for the Hamming filter [17] was varied for every run of the 2-D FBP algorithm. The result is shown in figure 3a. "
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