Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies

Burgos, Ninon; Cardoso, M. Jorge; Thielemans, Kris; Modat, Marc; Pedemonte, Stefano; Dickson, John; Barnes, Anna; Ahmed, Rebekah M.; Mahoney, Colin; Schott, Jonathan M.; Duncan, John S.; Atkinson, David; Arridge, Simon; Hutton, Brian F.; Ourselin, Sébastien

doi:10.1109/tmi.2014.2340135

Cited by 331 publications

(391 citation statements)

References 39 publications

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“…NMI exhibited the poorest performance as image similarity measure in this particular study. However, in other experiments such as in Burgos et al (2014 ), the LNCC similarity measure outperformed other techniques. This issue largely depends on the employed MR sequence, level of noise, inter-subject intensity normalization and registration algorithm.…”

Section: Nmimentioning

confidence: 89%

“…Atlas-guided segmentation has been successfully applied in various image segmentation tasks using different imaging modalities, particularly for cases with very low contrast to the surrounding tissues ( Lorenzo-Valdés et al, 2004 ). Atlasbased methods are of special interest since they have so far exhibited superior performance in terms of bone identification ( Burgos et al, 2014 ) particularly in whole-body imaging ( Hofmann et al, 2011 ). Burgos et al (2014 ) demonstrated superior performance of atlas-based methods in CT synthesis and PET quantitative accuracy compared to a segmentation method using an UTE MRI sequence in brain imaging.…”

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

confidence: 99%

“…Atlasbased methods are of special interest since they have so far exhibited superior performance in terms of bone identification ( Burgos et al, 2014 ) particularly in whole-body imaging ( Hofmann et al, 2011 ). Burgos et al (2014 ) demonstrated superior performance of atlas-based methods in CT synthesis and PET quantitative accuracy compared to a segmentation method using an UTE MRI sequence in brain imaging. Likewise, Mehranian et al (2016 ) demonstrated that atlas-based methods provide the most accurate attenuation maps compared to simultaneous activity-attenuation estimation and state-of-the-art 3-class segmentation method.…”

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

confidence: 99%

“…(15) or ( 16 ) to perform the final segmentation step. The same local weighting atlas fusion strategy was exploited by Burgos et al for attenuation map synthesis in brain PET/MRI ( Burgos et al, 2013( Burgos et al, , 2014.…”

Section: Lnc C Dmentioning

confidence: 99%

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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: Nmimentioning

confidence: 89%

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

confidence: 99%

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

confidence: 99%

Section: Lnc C Dmentioning

confidence: 99%

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

Arabi

Zaidi

2017

Medical Image Analysis

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“…Quantitative analysis demonstrated that their proposed approach reduces the errors to less than 3% on average. Burgos et al (2014) proposed a pseudo-CT synthesization method using multi-atlas registration and local weighting of aligned CT-MRI atlases using a local image similarity measure, such as local normalized cross-correlation (LNCC). Comparison with UTE segmentation-based AC using 42 brain datasets showed that this method results in a mean relative error of b 1%, while the UTE-based approach resulted in a mean error of 12%.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI

2016

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Purpose: In quantitative PET/MR imaging, attenuation correction (AC) of PET data is markedly challenged by the need of deriving accurate attenuation maps from MR images. A number of strategies have been developed for MRI-guided attenuation correction with different degrees of success. In this work, we compare the quantitative performance of three generic AC methods, including standard 3-class MR segmentation-based, advanced atlasregistration-based and emission-based approaches in the context of brain time-of-flight (TOF) PET/MRI. Materials and methods: Fourteen patients referred for diagnostic MRI and 18 F-FDG PET/CT brain scans were included in this comparative study. For each study, PET images were reconstructed using four different attenuation maps derived from CT-based AC (CTAC) serving as reference, standard 3-class MR-segmentation, atlas-registration and emission-based AC methods. To generate 3-class attenuation maps, T1-weighted MRI images were segmented into background air, fat and soft-tissue classes followed by assignment of constant linear attenuation coefficients of 0, 0.0864 and 0.0975 cm −1 to each class, respectively. A robust atlas-registration based AC method was developed for pseudo-CT generation using local weighted fusion of atlases based on their morphological similarity to target MR images. Our recently proposed MRI-guided maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm was employed to estimate the attenuation map from TOF emission data. The performance of the different AC algorithms in terms of prediction of bones and quantification of PET tracer uptake was objectively evaluated with respect to reference CTAC maps and CTAC-PET images.Results: Qualitative evaluation showed that the MLAA-AC method could sparsely estimate bones and accurately differentiate them from air cavities. It was found that the atlas-AC method can accurately predict bones with variable errors in defining air cavities. Quantitative assessment of bone extraction accuracy based on Dice similarity coefficient (DSC) showed that MLAA-AC and atlas-AC resulted in DSC mean values of 0.79 and 0.92, respectively, in all patients. The MLAA-AC and atlas-AC methods predicted mean linear attenuation coefficients of 0.107 and 0.134 cm . The evaluation of the relative change in tracer uptake within 32 distinct regions of the brain with respect to CTAC PET images showed that the 3-class MRAC, MLAA-AC and atlas-AC methods resulted in quantification errors of −16.2 ± 3.6%, −13.3 ± 3.3% and 1.0 ± 3.4%, respectively. Linear regression and Bland-Altman concordance plots showed that both 3-class MRAC and MLAA-AC methods result in a significant systematic bias in PET tracer uptake, while the atlas-AC method results in a negligible bias. Conclusion: The standard 3-class MRAC method significantly underestimated cerebral PET tracer uptake. While current state-of-the-art MLAA-AC methods look promising, they were unable to noticeably reduce quantification errors in the context of brain imaging. Conversely, the propose...

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Investigating reliable amyloid accumulation in Centiloids: Results from the AMYPAD Prognostic and Natural History Study

Bollack,

Collij,

García

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONTo support clinical trial designs focused on early interventions, our study determined reliable early amyloid‐β (Aβ) accumulation based on Centiloids (CL) in pre‐dementia populations.METHODSA total of 1032 participants from the Amyloid Imaging to Prevent Alzheimer's Disease–Prognostic and Natural History Study (AMYPAD‐PNHS) and Insight46 who underwent [18F]flutemetamol, [18F]florbetaben or [18F]florbetapir amyloid‐PET were included. A normative strategy was used to define reliable accumulation by estimating the 95th percentile of longitudinal measurements in sub‐populations (NPNHS = 101/750, NInsight46 = 35/382) expected to remain stable over time. The baseline CL threshold that optimally predicts future accumulation was investigated using precision‐recall analyses. Accumulation rates were examined using linear mixed‐effect models.RESULTSReliable accumulation in the PNHS was estimated to occur at >3.0 CL/year. Baseline CL of 16 [12,19] best predicted future Aβ‐accumulators. Rates of amyloid accumulation were tracer‐independent, lower for APOE ε4 non‐carriers, and for subjects with higher levels of education.DISCUSSIONOur results support a 12–20 CL window for inclusion into early secondary prevention studies. Reliable accumulation definition warrants further investigations.

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Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies

Cited by 331 publications

References 39 publications

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

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

Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI

Investigating reliable amyloid accumulation in Centiloids: Results from the AMYPAD Prognostic and Natural History Study

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