PET/MR brain imaging: evaluation of clinical UTE-based attenuation correction

Aasheim, Lars Birger; Karlberg, Anna; Goa, Pål Erik; Håberg, Asta; Sørhaug, Sveinung; Fagerli, Unn-Merete; Eikenes, Live

doi:10.1007/s00259-015-3060-3

Cited by 47 publications

(85 citation statements)

References 24 publications

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“…Second, ZTE requires minimal gradient switching, which can reduce eddy current effect. This might be some advantage when compared with the UTE-AC, which is one of the most popular segmentation-based methods in clinical head PET/MR study (8). The artifact sometimes causes the error in the calculation of T2 relaxation time on UTE-AC because of reduced signal intensities in the first UTE echo just after switching (9,29).…”

Section: Discussionmentioning

confidence: 99%

“…The first family is that of template-/atlas-/model-based approaches (5)(6)(7). The second one is that of segmentation approaches (8)(9)(10)(11)(12). The third one constitutes methods directly estimating attenuation information from emission data (13,14).…”

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confidence: 99%

“…However, these methods are not recommended for brain studies, because neglecting bone introduces a significant bias in the area of the cortex (15). In addition, the mMR has a clinical dual ultrashort echo (UTE)-based segmentation method (8) and a prototype modelbased method (7). In turn, the SIGNA has a clinical atlas-based method and a prototype zero-echo-time (ZTE)-based segmentation method (5,16,17).…”

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Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain ¹⁸F-FDG PET/MRI: Comparison with Atlas Attenuation Correction

et al. 2016

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Accurate attenuation correction (AC) on PET/MR is still challenging. The purpose of this study was to evaluate the clinical feasibility of AC based on fast zero-echo-time (ZTE) MRI by comparing it with the default atlas-based AC on a clinical PET/MR scanner. Methods: We recruited 10 patients with malignant diseases not located on the brain. In all patients, a clinically indicated whole-body 18 F-FDG PET/CT scan was acquired. In addition, a head PET/MR scan was obtained voluntarily. For each patient, 2 AC maps were generated from the MR images. One was atlas-AC, derived from T1-weighted liver acquisition with volume acceleration flex images (clinical standard). The other was ZTE-AC, derived from proton-density-weighted ZTE images by applying tissue segmentation and assigning continuous attenuation values to the bone. The AC map generated by PET/CT was used as a silver standard. On the basis of each AC map, PET images were reconstructed from identical raw data on the PET/MR scanner. All PET images were normalized to the SPM5 PET template. After that, these images were qualified visually and quantified in 67 volumes of interest (VOIs; automated anatomic labeling, atlas). Relative differences and absolute relative differences between PET images based on each AC were calculated. 18 F-FDG uptake in all 670 VOIs and generalized merged VOIs were compared using a paired t test. Results: Qualitative analysis shows that ZTE-AC was robust to patient variability. Nevertheless, misclassification of air and bone in mastoid and nasal areas led to the overestimation of PET in the temporal lobe and cerebellum (%diff of ZTE-AC, 2.46% ± 1.19% and 3.31% ± 1.70%, respectively). The j%diffj of all 670 VOIs on ZTE was improved by approximately 25% compared with atlas-AC (ZTE-AC vs. atlas-AC, 1.77% ± 1.41% vs. 2.44% ± 1.63%, P , 0.01). In 2 of 7 generalized VOIs, j%diffj on ZTE-AC was significantly smaller than atlas-AC (ZTE-AC vs. atlas-AC: insula and cingulate, 1.06% ± 0.67% vs. 2.22% ± 1.10%, P , 0.01; central structure, 1.03% ± 0.99% vs. 2.54% ± 1.20%, P , 0.05). Conclusion: The ZTE-AC could provide more accurate AC than clinical atlas-AC by improving the estimation of head-skull attenuation. The misclassification in mastoid and nasal areas must be addressed to prevent the overestimation of PET in regions near the skull base.

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Section: Discussionmentioning

confidence: 99%

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Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain ¹⁸F-FDG PET/MRI: Comparison with Atlas Attenuation Correction

et al. 2016

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“…The accuracy of the attenuation maps were evaluated using Dice similarity coefficients (40)(41)(42) that measure the overlap of the segmented bone and air regions of MR-AC maps with respect to those of CT-AC maps (1 for perfect overlap and 0 for no overlapping) according to the following equation:…”

Section: Image Analysismentioning

confidence: 99%

MRI-Based Attenuation Correction for PET/MRI Using Multiphase Level-Set Method

Seo

Kang

et al. 2015

J Nucl Med

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Inaccuracy in MR image-based attenuation correction (MR-AC) leads to errors in quantification and the misinterpretation of lesions in brain PET/MRI studies. To resolve this problem, we proposed an improved ultrashort echo time MR-AC method that was based on a multiphase level-set algorithm with main magnetic field (B 0 ) inhomogeneity correction. We also assessed the feasibility of this levelset-based MR-AC method (MR-AC level ), compared with CT-AC and MR-AC provided by the manufacturer of the PET/MRI scanner (MR-AC mMR ). Methods: Ten healthy volunteers and 20 Parkinson disease patients underwent 18 F-FDG and 18 F-fluorinated-N-3-fluoropropyl-2-β-carboxymethoxy-3-β-(4-iodophenyl)nortropane ( 18 F-FP-CIT) PET scans, respectively, using both PET/MRI and PET/CT scanners. The level-set-based segmentation algorithm automatically delimited air, bone, and soft tissue from the ultrashort echo time MR images. For the comparison, MR-AC maps were coregistered to reference CT. PET sinogram data obtained from PET/CT studies were then reconstructed using the CT-AC, MR-AC mMR , and MR-AC level maps. The accuracies of SUV, SUVr (SUV and its ratio to the cerebellum), and specific-to-nonspecific binding ratios obtained using MR-AC level and MR-AC mMR were compared with CT-AC using region-ofinterest-and voxel-based analyses. Results: There was remarkable improvement in the segmentation of air cavities and bones and the quantitative accuracy of PET measurement using the level set. Although the striatal and cerebellar activities in 18 F-FP-CIT PET and frontal activity in 18 F-FDG PET were significantly underestimated by the MR-AC mMR , the MR-AC level provided PET images almost equivalent to the CT-AC images. PET quantification error was reduced by a factor of 3 using MR-AC level ( The development of tomographic imaging technologies has made dramatic progress in recent decades. Among the modern medical imaging systems, PET and MRI have greatly contributed to understanding normal and abnormal brain functions and evaluating various neurologic disorders (1-4). Although PET is the most sensitive medical imaging device, providing both functional and biochemical information, it has limited spatial resolution, signalto-noise ratio, and anatomic information. Conversely, MRI offers detailed anatomic information about the brain along with excellent soft-tissue contrast and various types of hemodynamic information (i.e., perfusion and diffusion). Accordingly, the combination of PET and MRI can provide a 1-stop shop for clinical examination and new methodology for exploring the brain with multiparametric and complementary imaging information (5,6).In addition, fully integrated PET/MRI scanners based on semiconductor photosensors, such as avalanche photodiodes and silicon photomultipliers, allow the simultaneous acquisition of both image datasets, which possess several distinct advantages over the sequential scan in conventional PET/CT examinations (7-11). Accurate spatiotemporal correlation of PET/MRI signals permits the studies to demonstrat...

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“…In brain PET/MRI imaging, ultrashort echo-time (UTE) (Keereman et al, 2010, Catana et al, 2010 and more recently zero echo-time (ZTE) , Delso et al, 2015 MR sequences have been developed to specifically delineate bones and include them in the attenuation maps. Initial UTE-based AC studies have reported quantification errors of less than 5% (Aasheim et al, 2015); however, the inhomogeneous and imprecise classification of bones, particularly in the presence of diamagnetic susceptibility effects at air/bone or air/soft-tissue interfaces (Delso et al, 2014) can give rise to errors in the range 4%-17% in different regions of the brain (Dickson et al, 2014). Recently, Delso et al (2015) demonstrated that ZTE-based bone segmentation outperforms its UTE-based counterpart with reduced segmentation errors.…”

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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PET/MR brain imaging: evaluation of clinical UTE-based attenuation correction

Cited by 47 publications

References 24 publications

Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain ¹⁸F-FDG PET/MRI: Comparison with Atlas Attenuation Correction

Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain ¹⁸F-FDG PET/MRI: Comparison with Atlas Attenuation Correction

MRI-Based Attenuation Correction for PET/MRI Using Multiphase Level-Set Method

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

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PET/MR brain imaging: evaluation of clinical UTE-based attenuation correction

Cited by 47 publications

References 24 publications

Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain 18F-FDG PET/MRI: Comparison with Atlas Attenuation Correction

Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain 18F-FDG PET/MRI: Comparison with Atlas Attenuation Correction

MRI-Based Attenuation Correction for PET/MRI Using Multiphase Level-Set Method

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

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Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain ¹⁸F-FDG PET/MRI: Comparison with Atlas Attenuation Correction

Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain ¹⁸F-FDG PET/MRI: Comparison with Atlas Attenuation Correction