MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units

Juttukonda, Meher R.; Mersereau, Bryant G.; Chen, Yasheng; Su, Yi; Rubin, Brian G.; Benzinger, Tammie L.S.; Lalush, David S.; An, Hongyu

doi:10.1016/j.neuroimage.2015.03.009

Cited by 78 publications

(109 citation statements)

References 28 publications

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“…In our study, the relative error and absolute relative error of ZTE-AC across all 670 VOIs were 20.09% 6 2.26% and 1.77% 6 1.41%, respectively, which are generally comparable to other studies (9,(22)(23)(24). Previously reported absolute relative percentage errors of PET images range from 1.38% 6 4.52% to 2.55% 6 0.86% (9,(22)(23)(24), though care should be taken when comparing these studies and the present study, because of analysis variations.…”

Section: Discussionsupporting

confidence: 90%

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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: Discussionsupporting

confidence: 90%

“…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%

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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“…Results similar to ours were also presented by Burgos et al (2014) and Izquierdo-Garcia et al (2014) using atlas-based approaches that predict CT HU from T1w images, Johansson et al (2014) with a machine learning method based on a mixture of Gaussians using two pairs of UTE images, Navalpakkam et al (2013) with a support vector regression method using Dixon image and a pair of UTE images, and Juttukonda et al (2015) using the R * 2 signal to model the individual bone density. The mean error (ME) or mean absolute error (MAE) reported for the full brain region on PET is for Burgos: 0.2%/2.9% (ME/MAE, 41 subjects), Izquierdo-Garcia: −1.2% (ME, 15 subjects), Johansson: 1.9% (ME, 8 subjects) (Larsson et al 2013), Navalpakkam: 2.4% (MAE, 5 subjects), and Juttukonda: 2.6% (MAE, 98 subjects).…”

Section: Discussionsupporting

confidence: 83%

“…We use a low threshold for included bone (R * 2 > 100 s −1 ), which is lower than that in the literature (Keereman et al 2010, Juttukonda et al 2015. We chose to use the lower threshold to capture the full width of the bone, and avoid discontinuities in the skull, as opposed to existing UTE based segmentation methods (Dickson et al 2014, Juttukonda et al 2015. The low threshold might introduce a bias, as the amount of included bone can be locally overestimated, especially in areas of complex air, bone, and tissue combinations.…”

Section: Introductionmentioning

confidence: 99%

Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging

et al. 2015

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The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data.A total of 164 [ 18 F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R * 2 values to CT Hounsfield Units (HU) to measure the density in bone voxels.

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“…The creation of such parametric images can be divided into [208], (E) Anazodo et al [209], (F) Izquierdo-Garcia et al [210], (G) Burgos et al [211], (H) Merida et al [212]; MLAA-based methods: (I) Benoit et al [213]; Segmentation-based methods: (J) Cabello et al [214], (K) Juttukonda et al [215], (L) Ladefoged et al [216]. Graphical analyses methods include the Gjedde-Patlak equation [251,252], which describes irreversibly bound tracers, such as [ 18 F]FDG and the Logan-plot, which can be used to describe the reversibly bound tracers [253].…”

Section: Kinetic Modeling and Image Derived Input Function (Idif)mentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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State-of-the-art patient management frequently requires the use of non-invasive imaging methods to assess the anatomy, function or molecular-biological conditions of patients or study subjects. Such imaging methods can be singular, providing either anatomical or molecular information, or they can be combined, thus, providing "anato-metabolic" information. Hybrid imaging denotes image acquisitions on systems that physically combine complementary imaging modalities for an improved diagnostic accuracy and confidence as well as for increased patient comfort. The physical combination of formerly independent imaging modalities was driven by leading innovators in the field of clinical research and benefited from technological advances that permitted the operation of PET and MR in close physical proximity, for example. This review covers milestones of the development of various hybrid imaging systems for use in clinical practice and small-animal research. Special attention is given to technological advances that helped the adoption of hybrid imaging, as well as to introducing methodological concepts that benefit from the availability of complementary anatomical and biological information, such as new types of image reconstruction and data correction schemes. The ultimate goal of hybrid imaging is to provide useful, complementary and quantitative information during patient work-up. Hybrid imaging also opens the door to multi-parametric assessment of diseases, which will help us better understand the causes of various diseases that currently contribute to a large fraction of healthcare costs.

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MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units

Cited by 78 publications

References 28 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

Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging

Hybrid Imaging: Instrumentation and Data Processing

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MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units

Cited by 78 publications

References 28 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

Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging

Hybrid Imaging: Instrumentation and Data Processing

Contact Info

Product

Resources

About

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