MR-based attenuation correction for a whole-body sequential PET/MR system

Hu, Zhiqiang; Ojha, Navdeep; Renisch, Steffen; Schulz, Volkmar; Torres, I.; Buhl, Alexandra; Pal, Debashish; Muswick, Gary; Penatzer, J.; Guo, T.; Bonert, P.; Tung, Chi-hua; Kaste, J.; Morich, Michael; Havens, Timothy; Maniawski, Piotr; Schäfer, Wolfgang; Günther, Rolf W.; Krombach, Gabriele A.; Shao, L. G.

doi:10.1109/nssmic.2009.5401802

Cited by 61 publications

(53 citation statements)

References 4 publications

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“…The authors (10) performed a quantitative evaluation on 7 regions of interest in 2 patients, demonstrating the relative difference between the results attained using their segmentation approach and those attained using routine CT-based AC (CTAC), with the gap varying between 26% and 1%. Similar no-bone approaches were suggested by Hu et al (15) and Steinberg et al (16).…”

supporting

confidence: 75%

MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods

Hofmann¹,

Bezrukov²,

Mantlik³

et al. 2011

J Nucl Med

255

211

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PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). Methods: Eleven patients each underwent a whole-body PET/ CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. Results: In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% 6 10.2% and 7.7% 6 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% 6 7.9% for the segmentation method and 5.7% 6 4.7% for the AT&PR method. Conclusion: The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.

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supporting

confidence: 75%

MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods

Hofmann¹,

Bezrukov²,

Mantlik³

et al. 2011

J Nucl Med

255

211

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“…Because LACs predicted by gaussian process regression are used only for bone tissue voxels, gaussian process regression may be less affected by potential errors in the atlas registration. Thus, if a segmentationbased method is used as a standard for clinical attenuation correction, as is currently the case with clinically available PET/MR systems (15,17,27), a retrospective reconstruction with SEG2w- BONE for increased quantification precision for osseous lesions would introduce a smaller change for soft-tissue lesions than would AT&PR, thus potentially facilitating clinical acceptance. The reduced computational complexity of SEG2wBONE resulted in 24% faster runtime than that of AT&PR.…”

Section: Discussionmentioning

confidence: 99%

“…The first can be described as segmentation of MR images and assignment of specific attenuation values to the different tissue types (11)(12)(13)(15)(16)(17)(18)(19). The second uses external knowledge in the form of aligned MR images and attenuation templates that are registered to the patient's MR image to obtain an attenuation map (16,20) or are used to learn a mapping function to predict continuous LACs from the MR data (9,10,21,22).…”

mentioning

confidence: 99%

“…While a large fraction of MRAC methods has been evaluated or designed for brain imaging (10)(11)(12)(13)16,(20)(21)(22), several methods were recently proposed and evaluated on whole-body data as well (9,15,(17)(18)(19)24). Some studies (15,(17)(18)(19) have presented segmentation-based algorithms that do not account for bone.…”

mentioning

confidence: 99%

“…Some studies (15,(17)(18)(19) have presented segmentation-based algorithms that do not account for bone. Our group adapted the atlas-and pattern recognition (AT&PR)-based method, which predicts attenuation values on a continuous scale and accounts for bone for whole-body application (9).…”

mentioning

confidence: 99%

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MR-Based Attenuation Correction Methods for Improved PET Quantification in Lesions Within Bone and Susceptibility Artifact Regions

et al. 2013

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Hybrid PET/MR systems have recently entered clinical practice. Thus, the accuracy of MR-based attenuation correction in simultaneously acquired data can now be investigated. We assessed the accuracy of 4 methods of MR-based attenuation correction in lesions within soft tissue, bone, and MR susceptibility artifacts: 2 segmentation-based methods (SEG1, provided by the manufacturer, and SEG2, a method with atlas-based susceptibility artifact correction); an atlas-and pattern recognition-based method (AT&PR), which also used artifact correction; and a new method combining AT&PR and SEG2 (SEG2wBONE). Methods: Attenuation maps were calculated for the PET/MR datasets of 10 patients acquired on a whole-body PET/MR system, allowing for simultaneous acquisition of PET and MR data. Eighty percent iso-contour volumes of interest were placed on lesions in soft tissue (n 5 21), in bone (n 5 20), near bone (n 5 19), and within or near MR susceptibility artifacts (n 5 9). Relative mean volume-of-interest differences were calculated with CT-based attenuation correction as a reference. Results: For soft-tissue lesions, none of the methods revealed a significant difference in PET standardized uptake value relative to CT-based attenuation correction (SEG1, 22.6% 6 5.8%; SEG2, 21.6% 6 4.9%; AT&PR, 24.7% 6 6.5%; SEG2wBONE, 0.2% 6 5.3%). For bone lesions, underestimation of PET standardized uptake values was found for all methods, with minimized error for the atlas-based approaches (SEG1, 216.1% 6 9.7%; SEG2, 211.0% 6 6.7%; AT&PR, 26.6% 6 5.0%; SEG2wBONE, 24.7% 6 4.4%). For lesions near bone, underestimations of lower magnitude were observed (SEG1, 212.0% 6 7.4%; SEG2, 29.2% 6 6.5%; AT&PR, 24.6% 6 7.8%; SEG2wBONE, 24.2% 6 6.2%). For lesions affected by MR susceptibility artifacts, quantification errors could be reduced using the atlas-based artifact correction (SEG1, 254.0% 6 38.4%; SEG2, 215.0% 6 12.2%; AT&PR, 24.1% 6 11.2%; SEG2w-BONE, 0.6% 6 11.1%). Conclusion: For soft-tissue lesions, none of the evaluated methods showed statistically significant errors. For bone lesions, significant underestimations of 216% and 211% occurred for methods in which bone tissue was ignored (SEG1 and SEG2). In the present attenuation correction schemes, uncorrected MR susceptibility artifacts typically result in reduced attenuation values, potentially leading to highly reduced PET standardized uptake values, rendering lesions indistinguishable from background. While AT&PR and SEG2wBONE show accurate results in both soft tissue and bone, SEG2wBONE uses a two-step approach for tissue classification, which increases the robustness of prediction and can be applied retrospectively if more precision in bone areas is needed. Hybr id PET/MR systems have recently been introduced into clinical practice and are attracting increasing interest in the research and clinical communities (1-3). While PET/CT is an established hybrid modality with a wide range of applications, specific application trends for PET/MR are currently under investigation (4-7). MR-based atten...

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Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging

et al. 2018

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MR-based attenuation correction for a whole-body sequential PET/MR system

Cited by 61 publications

References 4 publications

MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods

MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods

MR-Based Attenuation Correction Methods for Improved PET Quantification in Lesions Within Bone and Susceptibility Artifact Regions

Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging

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