Concurrent Segmentation and Estimation of Transmission Images for Attenuation Correction in Positron Emission Tomography

Anderson, J.M.M.; Kim, Yoon‐Chul; Votaw, John R.

doi:10.1109/tns.2008.2009312

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…2). The bone class was derived by thresholding CTAC images at 0.109 cm 21 . As can be seen, the main difference between MLAA and MRAC methods sits over the lungs and bones, for which the maximum PET quantification errors occur when using standard MRAC methods.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Clinical Assessment of Emission- and Segmentation-Based MR-Guided Attenuation Correction in Whole-Body Time-of-Flight PET/MR Imaging

Mehranian

Zaidi

2015

J Nucl Med

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The joint maximum-likelihood reconstruction of activity and attenuation (MLAA) for emission-based attenuation correction has regained attention since the advent of time-of-flight PET/MR imaging. Recently, we improved the performance of the MLAA algorithm using an MR imaging-constrained gaussian mixture model (GMM). In this study, we compare the performance of our proposed algorithm with standard 4-class MR-based attenuation correction (MRAC) implemented on commercial systems. Methods: Five head and neck 18 F-FDG patients were scanned on PET/MR imaging and PET/CT scanners. Dixon fat and water MR images were registered to CT images. MRAC maps were derived by segmenting the MR images into 4 tissue classes and assigning predefined attenuation coefficients. For MLAA-GMM, MR images were segmented into known tissue classes, including fat, soft tissue, lung, background air, and an unknown MR low-intensity class encompassing cortical bones, air cavities, and metal artifacts. A coregistered bone probability map was also included in the unknown tissue class. Finally, the GMM prior was constrained over known tissue classes of attenuation maps using unimodal gaussians parameterized over a patient population. Results:The results showed that the MLAA-GMM algorithm outperformed the MRAC method by differentiating bones from air gaps and providing more accurate patient-specific attenuation coefficients of soft tissue and lungs. It was found that the MRAC and MLAA-GMM methods resulted in average standardized uptake value errors of -5.4% and -3.5% in the lungs, -7.4% and -5.0% in soft tissues/lesions, and -18.4% and -10.2% in bones, respectively. Conclusion: The proposed MLAA algorithm is promising for accurate derivation of attenuation maps on time-of-flight PET/MR systems. Hybr id PET/MR imaging systems have provided new opportunities for enhancing the diagnostic confidence of PET and MR imaging findings through the fusion of complementary structural and molecular information (1). The potential of PET/MR imaging in establishing a new multiparametric imaging paradigm has been a driving force for developing innovative solutions to tackle the challenges of these dual-modality systems.Accurate attenuation correction (AC) of PET data is one of the major challenges of quantitative PET/MR imaging (2). In these systems, attenuation maps at 511 keV should ideally be derived from the acquired MR images. However, in contrast to CT, MR imaging signals are not correlated with electron density and photon-attenuating properties of tissues but rather to proton density and magnetic relaxation properties. Therefore, there is no unique global mapping technique to convert MR imaging intensities to attenuation coefficients. In addition, lung tissues and cortical bones, which are 2 important tissue types in attenuation maps, exhibit low signals on images acquired using conventional MR pulse sequences because of their low water content and short transverse relaxation time. Therefore, the lungs, bones, and air pockets, which also produce a low signal, c...

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Section: Quantitative Evaluationmentioning

confidence: 99%

Clinical Assessment of Emission- and Segmentation-Based MR-Guided Attenuation Correction in Whole-Body Time-of-Flight PET/MR Imaging

Mehranian

Zaidi

2015

J Nucl Med

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“…The mixture of Gaussians is used to reflect the fact that the histogram of attenuation maps is generally consists of a few distinct peaks corresponding to air, lung, fat, soft tissue and bone [3]. In this work, we incorporated spatial information derived from Dixon MR images into the GMM using a tissue prior map.…”

Section: A Objective Functionmentioning

confidence: 99%

Joint estimation of activity and attenuation in PET/MR using MR-constrained Gaussian priors

Mehranian

Zaidi

2014

2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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The maximum likelihood estimation of attenuation and activity (MLAA) has been proposed to jointly estimate activity and attenuation from emission data only. In this paper, we proposed an improved MLAA algorithm by imposing MR spatial and CT statistical constraints on the estimation of attenuation using a constrained Gaussian mixture model (GMM) and a Markov random field (MRF) smoothness prior. We compare the proposed MLAA-GMM algorithm with the MLAA algorithms proposed by Rezaei et al and Salomon et al as well as 4-class MRAC method. Dixon MR images were segmented into outside air, fat and soft tissue classes and an MR low-intensity class corresponding to air cavities, bone and susceptibility artifacts. To eliminate the miss-classification of bones with surrounding tissue, the unknown class was expanded by a co-registered bone probability map. A mixture of 4 Gaussians (air, fat/soft and bone) was used for the unknown class, while unimodal Gaussians were used for others. The algorithms were evaluated using simulation and clinical datasets. The bias in estimated attenuation and activity was evaluated against CT-based attenuation correction. Our results show that MLAA-Rezaei suffers from scale and noise problems. The performance of MLAA-Salomon algorithm is also affected by the scale and depends highly on MR quality and segmentation, especially at air/bone interfaces and vertebra. It was demonstrated the MLAA-GMM effectively exploits MR prior information, thereby results in noise-, crosstalk-and scalefree attenuation maps. The PET bias analyses showed that the MLAA-GMM outperformed the scale corrected MLAA-Rezaei and MLAA-Salomon algorithms as well as the 4-class MRAC method. Therefore, the proposed method can pave the way toward accurate emission-based estimation of attenuation in TOF PET/MR imaging.

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“…Correction in Positron Emission Tomography Anderson, J. M. M., +, TNS Feb. 2009. [136][137][138][139][140][141][142][143][144][145][146] Derivation of System Matrix From Simulation Data for an Animal SPECT WithSlit-Slat Collimator.…”

mentioning

confidence: 99%

2009 Index IEEE Transactions on Nuclear Science Vol. 56

2009

IEEE Trans. Nucl. Sci.

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Concurrent Segmentation and Estimation of Transmission Images for Attenuation Correction in Positron Emission Tomography

Cited by 6 publications

References 40 publications

Clinical Assessment of Emission- and Segmentation-Based MR-Guided Attenuation Correction in Whole-Body Time-of-Flight PET/MR Imaging

Clinical Assessment of Emission- and Segmentation-Based MR-Guided Attenuation Correction in Whole-Body Time-of-Flight PET/MR Imaging

Joint estimation of activity and attenuation in PET/MR using MR-constrained Gaussian priors

2009 Index IEEE Transactions on Nuclear Science Vol. 56

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