Simultaneous reconstruction of activity and attenuation in Time-of-Flight PET

Rezaei, Ahmadreza; Nuyts, Johan; Defrise, Michel; Bal, Girish; Michel, Christian; Conti, Maurizio; Watson, Charles C.

doi:10.1109/nssmic.2011.6153883

Cited by 155 publications

(78 citation statements)

References 30 publications

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“…Basically two categories have emerged: atlas-guided attenuation map generation approaches ( Hofmann et al, 2011 ;Bezrukov et al, 2013 ;Arabi and Zaidi, 2016a ;Marshall et al, 2013 ;Arabi and Zaidi, 2016b ) and emission-based approaches ( Rezaei et al, 2012 ;Mehranian and Zaidi, 2015 ). Atlas-guided methods primarily rely on prior information provided by registration of an atlas into target image coordinates to allow classification of bone tissues.…”

Section: E-mail Address: Habibzaidi@hcugech (H Zaidi)mentioning

confidence: 99%

Comparison of atlas-based techniques for whole-body bone segmentation

Arabi

Zaidi

2017

Medical Image Analysis

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a b s t r a c tWe evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean square distance (MSD) as image similarity measures for calculating the weighting factors, along with other atlas-dependent algorithms, such as (v) shape-based averaging (SBA) and (vi) Hofmann's pseudo-CT generation method. The performance evaluation of the different segmentation techniques was carried out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice criterion, global weighting atlas fusion methods provided moderate improvement of whole-body bone segmentation (DSC = 0.65 ± 0.05) compared to non-weighted IA (DSC = 0.60 ± 0.02). The local weighed atlas fusion approach using the MSD similarity measure outperformed the other strategies by achieving a DSC of 0.81 ± 0.03 while using the NCC and NMI measures resulted in a DSC of 0.78 ± 0.05 and 0.75 ± 0.04, respectively. Despite very long computation time, the extracted bone obtained from both SBA (DSC = 0.56 ± 0.05) and Hofmann's methods (DSC = 0.60 ± 0.02) exhibited no improvement compared to non-weighted IA. Finding the optimum parameters for implementation of the atlas fusion approach, such as weighting factors and image similarity patch size, have great impact on the performance of atlas-based segmentation approaches. The voxel-wise atlas fusion approach exhibited excellent performance in terms of cancelling out the non-systematic registration errors leading to accurate and reliable segmentation results. Denoising and normalization of MR images together with optimization of the involved parameters play a key role in improving bone extraction accuracy.

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Section: E-mail Address: Habibzaidi@hcugech (H Zaidi)mentioning

confidence: 99%

Comparison of atlas-based techniques for whole-body bone segmentation

Arabi

Zaidi

2017

Medical Image Analysis

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“…Algorithmic methods like MLAA (maximum likelihood activity and attenuation) [16] attempt to add arms to the mu map based on consistency between the measured PET and mu map. However, this needs to be performed for each bed position and the result is not always stable.…”

Section: Foreword Xmentioning

confidence: 99%

PET/MR Imaging

Gupta

Matthews

Bangiyev

et al. 2018

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“…One is to use a low-dose CT image to acquire the attenuation image [78][79][80]. Another is to perform simultaneous estimation of activity and attenuation from time-of-flight (TOF) PET [81,82] based on the recent theoretical advance that TOF PET can reduce cross-talk between activity and attenuation images [83]. The last one is to extract attenuation information from MR images, especially for recently developed simultaneous PET-MR scanners [84].…”

Section: Sources Of Attenuation/scatter Informationmentioning

confidence: 99%

The Use of Anatomical Information for Molecular Image Reconstruction Algorithms: Attenuation/Scatter Correction, Motion Compensation, and Noise Reduction

Chun

2016

Nucl Med Mol Imaging

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PET and SPECT are important tools for providing valuable molecular information about patients to clinicians. Advances in nuclear medicine hardware technologies and statistical image reconstruction algorithms enabled significantly improved image quality. Sequentially or simultaneously acquired anatomical images such as CT and MRI from hybrid scanners are also important ingredients for improving the image quality of PET or SPECT further. High-quality anatomical information has been used and investigated for attenuation and scatter corrections, motion compensation, and noise reduction via post-reconstruction filtering and regularization in inverse problems. In this article, we will review works using anatomical information for molecular image reconstruction algorithms for better image quality by describing mathematical models, discussing sources of anatomical information for different cases, and showing some examples.

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Simultaneous reconstruction of activity and attenuation in Time-of-Flight PET

Cited by 155 publications

References 30 publications

Comparison of atlas-based techniques for whole-body bone segmentation

Comparison of atlas-based techniques for whole-body bone segmentation

PET/MR Imaging

The Use of Anatomical Information for Molecular Image Reconstruction Algorithms: Attenuation/Scatter Correction, Motion Compensation, and Noise Reduction

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