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Adaptive autoregressive model for reduction of noise in SPECT

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Adaptive autoregressive model for reduction of noise in SPECT. / Takalo, Reijo; Hytti, Heli; Ihalainen, Heimo; Sohlberg, Antti.

In: Computational and Mathematical Methods in Medicine, Vol. 2015, 494691, 2015.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Takalo, R, Hytti, H, Ihalainen, H & Sohlberg, A 2015, 'Adaptive autoregressive model for reduction of noise in SPECT', Computational and Mathematical Methods in Medicine, vol. 2015, 494691. https://doi.org/10.1155/2015/494691

APA

Takalo, R., Hytti, H., Ihalainen, H., & Sohlberg, A. (2015). Adaptive autoregressive model for reduction of noise in SPECT. Computational and Mathematical Methods in Medicine, 2015, [494691]. https://doi.org/10.1155/2015/494691

Vancouver

Takalo R, Hytti H, Ihalainen H, Sohlberg A. Adaptive autoregressive model for reduction of noise in SPECT. Computational and Mathematical Methods in Medicine. 2015;2015. 494691. https://doi.org/10.1155/2015/494691

Author

Takalo, Reijo ; Hytti, Heli ; Ihalainen, Heimo ; Sohlberg, Antti. / Adaptive autoregressive model for reduction of noise in SPECT. In: Computational and Mathematical Methods in Medicine. 2015 ; Vol. 2015.

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@article{fad732f02eb1402ea089a8f0d5e442ac,
title = "Adaptive autoregressive model for reduction of noise in SPECT",
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.",
author = "Reijo Takalo and Heli Hytti and Heimo Ihalainen and Antti Sohlberg",
year = "2015",
doi = "10.1155/2015/494691",
language = "English",
volume = "2015",
journal = "Computational and Mathematical Methods in Medicine",
issn = "1748-670X",
publisher = "Hindawi Publishing Corporation",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Adaptive autoregressive model for reduction of noise in SPECT

AU - Takalo, Reijo

AU - Hytti, Heli

AU - Ihalainen, Heimo

AU - Sohlberg, Antti

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84930684783&partnerID=8YFLogxK

U2 - 10.1155/2015/494691

DO - 10.1155/2015/494691

M3 - Article

VL - 2015

JO - Computational and Mathematical Methods in Medicine

JF - Computational and Mathematical Methods in Medicine

SN - 1748-670X

M1 - 494691

ER -