Optimization of Butterworth filter for brain SPECT imaging

Minoshima, Shinsei; Maruno, Hirotaka; Yui, Nobuharu; Togawa, Takashi; Kinoshita, Fujimi; Kubota, Masahiro; Berger, Kevin L.; Uchida, Yoshitaka; Uno, Kimiichi; Arimizu, N

doi:10.1007/bf03164571

Cited by 17 publications

(14 citation statements)

References 3 publications

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“…This result made agreement with the previous studies [34], which showed the optimal Fc of Butterworth filter increased with higher count density. Generally, a low Fc is preferred for low count density projections while a high Fc should be implemented on high count density projections.…”

Section: Optimization Of Fc For Butterworth Filtersupporting

confidence: 93%

“…When optimizing parameters of Butterworth filter, the order was fixed at 6 [33], which also found that there was no major difference in their influence on the Fc of 0.5 Nq or above when estimating the optimized values of Butterworth filter for the quantification of the cardiac volumes and left ventricular ejection fraction for Tc-99m gated myocardial SPECT. Therefore, when optimizing the parameters of Butterworth filter, many studies optimized the value of Fc while fixing the value of order [4,7,10,34].…”

Section: Optimization Parameters Of Butterworth Filtermentioning

confidence: 99%

“…MSE is an indicator that can give an objective and reproducible measure of the reconstruction accuracy and it also makes agreement with visual assessment. Therefore, MSE was used for parameter optimization [34]. It can be calculated as follows:…”

Section: Optimization Parameters Of Butterworth Filtermentioning

confidence: 99%

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Optimizing Breast Cancer Images from Wire Mesh Collimator SPECT Camera Using Characterized Butterworth Filter

Mi¹

2018

IJNMRS

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Background: Previous studies have shown that SPECT camera coupled with Wire Mesh Collimator (WMC) has good sensitivity in detecting breast cancer. In this paper, we intend to improve the breast cancer detection through enhancing images by characterized Butterworth filter. The characterization was based on trade-off between noise suppression and spatial resolution degradation. Methods: Monte Carlo simulation studies were performed to obtain projections for a half-ellipsoidal breast phantom in the prone position with five different tumor sizes, and four projection sets with acquisitions of 20, 40, 80, 160 seconds were simulated which gave different count density in the range of 28 to 229 counts/cm 2 for each tumor size. Butterworth filter was used to filter the sinograms with 13 different cutoff frequency (Fc), ranging from 0.2 to 0.8 Nyquist frequency (Nq) with 0.05 steps. The relationship between optimum Fc, count density and tumor size were revealed. Results:The results showed that the optimum Fc not only depended on count density but also linked to the change of tumor size. For the case of tumor size, it is suggested that the Fc can be relocated to a higher spatial frequency when tumor size was close to the spatial resolution of SPECT system in order to preserve tumor signal. Conclusion:It may be possible to use a priori knowledge of tumor size and count density as guideline for choosing the optimal parameters of Butterworth filter in Tc-99m breast SPECT imaging.

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Section: Optimization Of Fc For Butterworth Filtersupporting

confidence: 93%

Section: Optimization Parameters Of Butterworth Filtermentioning

confidence: 99%

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Optimizing Breast Cancer Images from Wire Mesh Collimator SPECT Camera Using Characterized Butterworth Filter

Mi¹

2018

IJNMRS

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“…In another report [17], the optimization of Butterworth filter for brain SPECT imaging was studied. The aim of the work was to find a relationship between the total counts and the optimal cut off frequencies of the Butterworth filter.…”

Section: Type Of Filters Depending On Type Of Studymentioning

confidence: 99%

Filtering in SPECT Image Reconstruction

Lyra

Ploussi

2011

International Journal of Biomedical Imaging

115

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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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“…Noise removal of raw SPECT projection data has been typically conducted by a low-pass filter to eliminate high-frequency noise which causes artifacts of SPECT reconstruction [2] [3] [4] [5] [6]. A Butterworth filter, which is usually used as a low-pass filter, however, cannot distinguish signal from noise.…”

Section: Introductionmentioning

confidence: 99%

Denoising Projection Data with a Robust Adaptive Bilateral Filter in Low-Count SPECT

Nakabayashi¹,

Chikamatsu²,

Okamoto³

et al. 2018

IJMPCERO

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Low-count SPECT images are well known to be smoothed strongly by a Butterworth filter for statistical noise reduction. Reconstructed images have a low signal-to-noise ratio (SNR) and spatial resolution because of the removal of high-frequency signal components. Using the developed robust adaptive bilateral filter (RABF), which was designed as a pre-stage filter of the Butterworth filter, this study was conducted to improve SNR without degrading the spatial resolution for low-count SPECT imaging. The filter can remove noise while preserving spatial resolution. To evaluate the proposed method, we extracted SNR and spatial resolution in a phantom study. We also conducted paired comparison for visual image quality evaluation in a clinical study. Results show that SNR was increased 1.4 times without degrading the spatial resolution. Visual image quality was improved significantly (p < 0.01) for clinical low-count data. Moreover, the accumulation structure became sharper. A structure embedded in noise emerged. Our method, which denoises without degrading the spatial resolution for low-count SPECT images, is expected to increase the effectiveness of diagnosis for low-dose scanning and short acquisition time scanning.

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Optimization of Butterworth filter for brain SPECT imaging

Cited by 17 publications

References 3 publications

Optimizing Breast Cancer Images from Wire Mesh Collimator SPECT Camera Using Characterized Butterworth Filter

Optimizing Breast Cancer Images from Wire Mesh Collimator SPECT Camera Using Characterized Butterworth Filter

Filtering in SPECT Image Reconstruction

Denoising Projection Data with a Robust Adaptive Bilateral Filter in Low-Count SPECT

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