Quantitative accuracy of attenuation correction in the Philips Ingenuity TF whole-body PET/MR system: a direct comparison with transmission-based attenuation correction

Schramm, Georg; Langner, Jens; Hofheinz, Frank; Petr, Jan; Beuthien‐Baumann, Bettina; Platzek, Ivan; Steinbach, J.; Kotzerke, J.; Hoff, Joerg van den

doi:10.1007/s10334-012-0328-5

Cited by 59 publications

(36 citation statements)

References 27 publications

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“…[5][6][7][8][9][10][11] Similar errors and issues have been reported for body imaging, with the largest number of errors occurring within and adjacent to bone tissue. [10][11][12][13][14][15] These results are not surprising as at least four tissue types, including bone, are required in order to achieve accurate SUVs, 5,16 whereas current commercial PET/MR attenuation correction (MR-AC) solutions use a three-or four-class segmentation that neglect bone. 13 The three-class segmentation method identifies air (external to the patient) and lungs and classifies the rest of the patient as soft tissue.…”

Section: Introductionmentioning

confidence: 58%

Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering

Stehning

et al. 2015

Medical Physics

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Purpose: MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spread functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2 * images (1/T2 * ) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2 * , were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 µs (max: 1.350 µs; min: 0.180 µs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low-dose CT. Conclusions: The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo-CT generation. C 2015 American Association of Physicists in Medicine.

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Section: Introductionmentioning

confidence: 58%

Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering

Stehning

et al. 2015

Medical Physics

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“…The Philips Ingenuity TF system (7) implemented a MR based approach that segments the MR image into air, lung and soft tissue and do not account for bones. In a comparison study against measured attenuation map, region dependent underestimation of up to 20% can occur for the Philips version of MRAC (36). For the GE Signa PET/MR system (8), we did not find systematic evaluation of MRAC in literature likely due to the short time since its release.…”

Section: Discussionmentioning

confidence: 99%

Impact of MR-Based Attenuation Correction on Neurologic PET Studies

Rubin

McConathy

et al. 2016

J Nucl Med

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Hybrid positron emission tomography (PET) and magnetic resonance (MR) scanners have become a reality in recent years with the benefits of reduced radiation exposure, reduction of imaging time, and potential advantages in quantification. Appropriate attenuation correction remains a challenge. Biases in PET activity measurements were demonstrated using the current MR based attenuation correction technique. We aim to investigate the impact of using standard MRAC technique on the clinical and research utility of PET/MR hybrid scanner for amyloid imaging. Methods Florbetapir scans were obtained on 40 participants on a Biograph mMR hybrid scanner with simultaneous MR acquisition. PET images were reconstructed using both MR and CT derived attenuation map. Quantitative analysis was performed for both datasets to assess the impact of MR based attenuation correction to absolute PET activity measurements as well as target to reference ratio (SUVR). Clinical assessment was also performed by a nuclear medicine physician to determine amyloid status based on the criteria in the FDA prescribing information for florbetapir. Results MR based attenuation correction led to underestimation of PET activity for most part of the brain with a small overestimation for deep brain regions. There is also an overestimation of SUVR values with cerebellar reference. SUVR measurements obtained from the two attenuation correction methods were strongly correlated. Clinical assessment of amyloid status resulted in identical classification as positive or negative regardless of the attenuation correction methods. Conclusions MR based attenuation correction cause biases in quantitative measurements. The biases may be accounted for by a linear model, although the spatial variation cannot be easily modelled. The quantitative differences however did not affect clinical assessment as positive or negative.

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“…HUs were converted into m-values by bilinear transformation (11). Finally, CTAC was smoothed to PET resolution (23). MATLAB, version 2011b (MathWorks Inc.), and SPM8 (Wellcome Trust Centre for Neuroimaging, University College London) were used in image processing.…”

Section: Ctac For Pet/mrmentioning

confidence: 99%

Effect of Attenuation Correction on Regional Quantification Between PET/MR and PET/CT: A Multicenter Study Using a 3-Dimensional Brain Phantom

et al. 2016

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A spatial bias in brain PET/MR exists compared with PET/CT, because of MR-based attenuation correction. We performed an evaluation among 4 institutions, 3 PET/MR systems, and 4 PET/CT systems using an anthropomorphic brain phantom, hypothesizing that the spatial bias would be minimized with CT-based attenuation correction (CTAC). Methods: The evaluation protocol was similar to the quantification of changes in neurologic PET studies. Regional analysis was conducted on 8 anatomic volumes of interest (VOIs) in gray matter on count-normalized, resolution-matched, coregistered data. On PET/MR systems, CTAC was applied as the reference method for attenuation correction. Results: With CTAC, visual and quantitative differences between PET/MR and PET/CT systems were minimized. Intersystem variation between institutions was 13.42% to −3.29% in all VOIs for PET/CT and 12.15% to −4.50% in all VOIs for PET/MR. PET/MR systems differed by 12.34% to −2.21%, 12.04% to −2.08%, and −1.77% to −5.37% when compared with a PET/CT system at each institution, and these differences were not significant (P $ 0.05). Conclusion: Visual and quantitative differences between PET/MR and PET/CT systems can be minimized by an accurate and standardized method of attenuation correction. If a method similar to CTAC can be implemented for brain PET/MRI, there is no reason why PET/MR should not perform as well as PET/CT. However, quantitative accuracy remains inconsistent between PET/MR and PET/CT systems, with MR-based attenuation correction (MRAC) suspected of being the main source of bias (3-5). MRAC remains a challenge, as MR images do not correspond to electron density and cannot be directly translated to m-values (6,7). Currently, conversion to m-values is performed via image segmentation (6-8).Although MRAC is feasible for whole-body imaging, large and spatially varying biases exist in brain PET/MR because of exclusion of bone in MRAC and incorrect segmentation of air cavities (3-5). These biases cannot be minimized merely by image postprocessing and must be compensated accordingly (9). An accurate, standardized attenuation correction method is needed to remove the differences.Currently, CT-based attenuation correction (CTAC) is an established standard (8) and is often considered the gold standard. Measured Hounsfield units (HUs) are converted to m-values by simple bilinear scaling (10-12). Therefore, a multicenter evaluation between PET/MR and PET/CT systems with CTAC and an anthropomorphic phantom would be highly desirable for determining image quantification in a controlled manner.Previous PET/MR investigations have been conducted with National Electrical Manufacturers Association whole-body or Hoffman phantoms, which do not model attenuation realistically and have not specifically addressed brain PET/MR (5,13,14). However, an anatomic brain phantom that has recently been developed models gray matter uptake and has a realistic head contour, including air spaces and the attenuation effect of bone (15,16). In this study, PET/MR and PET/CT syst...

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Quantitative accuracy of attenuation correction in the Philips Ingenuity TF whole-body PET/MR system: a direct comparison with transmission-based attenuation correction

Cited by 59 publications

References 27 publications

Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering

Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering

Impact of MR-Based Attenuation Correction on Neurologic PET Studies

Effect of Attenuation Correction on Regional Quantification Between PET/MR and PET/CT: A Multicenter Study Using a 3-Dimensional Brain Phantom

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