TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Metal Artifact Reduction Based on Automated Sinogram Segmentation and Adaptive Multiresolution MAP Reconstruction Method

Tutkimustuotosvertaisarvioitu

Standard

Metal Artifact Reduction Based on Automated Sinogram Segmentation and Adaptive Multiresolution MAP Reconstruction Method. / Us, Defne; Acar, Erman; Ruotsalainen, Ulla.

2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2015.

Tutkimustuotosvertaisarvioitu

Harvard

Us, D, Acar, E & Ruotsalainen, U 2015, Metal Artifact Reduction Based on Automated Sinogram Segmentation and Adaptive Multiresolution MAP Reconstruction Method. julkaisussa 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE, 1/01/00. https://doi.org/10.1109/NSSMIC.2015.7582104

APA

Us, D., Acar, E., & Ruotsalainen, U. (2015). Metal Artifact Reduction Based on Automated Sinogram Segmentation and Adaptive Multiresolution MAP Reconstruction Method. teoksessa 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) IEEE. https://doi.org/10.1109/NSSMIC.2015.7582104

Vancouver

Us D, Acar E, Ruotsalainen U. Metal Artifact Reduction Based on Automated Sinogram Segmentation and Adaptive Multiresolution MAP Reconstruction Method. julkaisussa 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE. 2015 https://doi.org/10.1109/NSSMIC.2015.7582104

Author

Us, Defne ; Acar, Erman ; Ruotsalainen, Ulla. / Metal Artifact Reduction Based on Automated Sinogram Segmentation and Adaptive Multiresolution MAP Reconstruction Method. 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2015.

Bibtex - Lataa

@inproceedings{add71562c68c4abe9714c6caf7edf01c,
title = "Metal Artifact Reduction Based on Automated Sinogram Segmentation and Adaptive Multiresolution MAP Reconstruction Method",
abstract = "High density objects in the field of view (FOV) cause artifacts in medical imaging. In X-ray computed tomography (CT), there are several ways to eliminate the effects of these artifacts. This paper aims to evaluate the performance of a novel reconstruction algorithm which accurately segments the metallic regions and reconstruct sharp metal/tissue boundaries, while reducing the artifacts around the metallic regions. This algorithm uses a multilevel segmentation algorithm based on Otsu’s threshold and adaptive multiresolution maximum a-posteriori expectation maximization (amMAP-EM). The qualities of Gaussian noise contaminated images were evaluated quantitatively using mean squared error and line profile analysis. The reconstructed image were compared with filtered backprojection (FBP) and maximum likelihood expectation maximization (MLEM) methods. According to the results, it is possible to reconstruct the images with more clear and sharper metal/tissue boundaries using amMAP-EM compared to MLEM and FBP, while avoiding the undesired artifacts such as blurring, streak artifacts or ringing.",
author = "Defne Us and Erman Acar and Ulla Ruotsalainen",
year = "2015",
doi = "10.1109/NSSMIC.2015.7582104",
language = "English",
publisher = "IEEE",
booktitle = "2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Metal Artifact Reduction Based on Automated Sinogram Segmentation and Adaptive Multiresolution MAP Reconstruction Method

AU - Us, Defne

AU - Acar, Erman

AU - Ruotsalainen, Ulla

PY - 2015

Y1 - 2015

N2 - High density objects in the field of view (FOV) cause artifacts in medical imaging. In X-ray computed tomography (CT), there are several ways to eliminate the effects of these artifacts. This paper aims to evaluate the performance of a novel reconstruction algorithm which accurately segments the metallic regions and reconstruct sharp metal/tissue boundaries, while reducing the artifacts around the metallic regions. This algorithm uses a multilevel segmentation algorithm based on Otsu’s threshold and adaptive multiresolution maximum a-posteriori expectation maximization (amMAP-EM). The qualities of Gaussian noise contaminated images were evaluated quantitatively using mean squared error and line profile analysis. The reconstructed image were compared with filtered backprojection (FBP) and maximum likelihood expectation maximization (MLEM) methods. According to the results, it is possible to reconstruct the images with more clear and sharper metal/tissue boundaries using amMAP-EM compared to MLEM and FBP, while avoiding the undesired artifacts such as blurring, streak artifacts or ringing.

AB - High density objects in the field of view (FOV) cause artifacts in medical imaging. In X-ray computed tomography (CT), there are several ways to eliminate the effects of these artifacts. This paper aims to evaluate the performance of a novel reconstruction algorithm which accurately segments the metallic regions and reconstruct sharp metal/tissue boundaries, while reducing the artifacts around the metallic regions. This algorithm uses a multilevel segmentation algorithm based on Otsu’s threshold and adaptive multiresolution maximum a-posteriori expectation maximization (amMAP-EM). The qualities of Gaussian noise contaminated images were evaluated quantitatively using mean squared error and line profile analysis. The reconstructed image were compared with filtered backprojection (FBP) and maximum likelihood expectation maximization (MLEM) methods. According to the results, it is possible to reconstruct the images with more clear and sharper metal/tissue boundaries using amMAP-EM compared to MLEM and FBP, while avoiding the undesired artifacts such as blurring, streak artifacts or ringing.

U2 - 10.1109/NSSMIC.2015.7582104

DO - 10.1109/NSSMIC.2015.7582104

M3 - Conference contribution

BT - 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

PB - IEEE

ER -