Dataset Information

Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification.

ABSTRACT: The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N?=?34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (¹⁸F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global ¹⁸F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVr_CT, SUVr_MRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVr_CT and SUVr_MRI global uptake scores showed negligible differences (mean?±?sd 0.01?±?0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1?mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine analysis.

SUBMITTER: Presotto L

PROVIDER: S-EPMC6072675 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification.

Presotto Luca L Iaccarino Leonardo L Sala Arianna A Vanoli Emilia G EG Muscio Cristina C Nigri Anna A Bruzzone Maria Grazia MG Tagliavini Fabrizio F Gianolli Luigi L Perani Daniela D Bettinardi Valentino V

NeuroImage. Clinical 20180719

The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures ...[more]

PMID: 30094164

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Project description:Structural MR images concomitantly acquired with PET images can provide crucial anatomic information for precise quantitative analysis. However, in the clinical setting, not all the subjects have corresponding MR images. Here, we developed a model to generate structural MR images from amyloid PET using deep generative networks. We applied our model to quantification of cortical amyloid load without structural MR. Methods: We used florbetapir PET and structural MR data from the Alzheimer Disease Neuroimaging Initiative database. The generative network was trained to generate realistic structural MR images from florbetapir PET images. After the training, the model was applied to the quantification of cortical amyloid load. PET images were spatially normalized to the template space using the generated MR, and then SUV ratio (SUVR) of the target regions was measured by predefined regions of interest. A real MR-based quantification was used as the gold standard to measure the accuracy of our approach. Other MR-less methods-a normal PET template-based, a multiatlas PET template-based, and a PET segmentation-based normalization/quantification-were also tested. We compared the performance of quantification methods using generated MR with that of MR-based and MR-less quantification methods. Results: Generated MR images from florbetapir PET showed signal patterns that were visually similar to the real MR. The structural similarity index between real and generated MR was 0.91 ± 0.04. The mean absolute error of SUVR of cortical composite regions estimated by the generated MR-based method was 0.04 ± 0.03, which was significantly smaller than other MR-less methods (0.29 ± 0.12 for the normal PET template, 0.12 ± 0.07 for the multiatlas PET template, and 0.08 ± 0.06 for the PET segmentation-based methods). Bland-Altman plots revealed that the generated MR-based SUVR quantification was the closest to the SUVRs estimated by the real MR-based method. Conclusion: Structural MR images were successfully generated from amyloid PET images using deep generative networks. Generated MR images could be used as templates for accurate and precise amyloid quantification. This generative method might be used to generate multimodal images of various organs for further quantitative analyses.

Project description:PurposeFor pediatric lymphoma, quantitative FDG PET/CT imaging features such as metabolic tumor volume (MTV) are important for prognosis and risk stratification strategies. However, feature extraction is difficult and time-consuming in cases of high disease burden. The purpose of this study was to fully automate the measurement of PET imaging features in PET/CT images of pediatric lymphoma.Methods18F-FDG PET/CT baseline images of 100 pediatric Hodgkin lymphoma patients were retrospectively analyzed. Two nuclear medicine physicians identified and segmented FDG avid disease using PET thresholding methods. Both PET and CT images were used as inputs to a three-dimensional patch-based, multi-resolution pathway convolutional neural network architecture, DeepMedic. The model was trained to replicate physician segmentations using an ensemble of three networks trained with 5-fold cross-validation. The maximum SUV (SUVmax), MTV, total lesion glycolysis (TLG), surface-area-to-volume ratio (SA/MTV), and a measure of disease spread (Dmaxpatient) were extracted from the model output. Pearson's correlation coefficient and relative percent differences were calculated between automated and physician-extracted features.ResultsMedian Dice similarity coefficient of patient contours between automated and physician contours was 0.86 (IQR 0.78-0.91). Automated SUVmax values matched exactly the physician determined values in 81/100 cases, with Pearson's correlation coefficient (R) of 0.95. Automated MTV was strongly correlated with physician MTV (R = 0.88), though it was slightly underestimated with a median (IQR) relative difference of - 4.3% (- 10.0-5.7%). Agreement of TLG was excellent (R = 0.94), with median (IQR) relative difference of - 0.4% (- 5.2-7.0%). Median relative percent differences were 6.8% (R = 0.91; IQR 1.6-4.3%) for SA/MTV, and 4.5% (R = 0.51; IQR - 7.5-40.9%) for Dmaxpatient, which was the most difficult feature to quantify automatically.ConclusionsAn automated method using an ensemble of multi-resolution pathway 3D CNNs was able to quantify PET imaging features of lymphoma on baseline FDG PET/CT images with excellent agreement to reference physician PET segmentation. Automated methods with faster throughput for PET quantitation, such as MTV and TLG, show promise in more accessible clinical and research applications.

Project description:Backgroundamyloid-PET reading has been classically implemented as a binary assessment, although the clinical experience has shown that the number of borderline cases is non negligible not only in epidemiological studies of asymptomatic subjects but also in naturalistic groups of symptomatic patients attending memory clinics. In this work we develop a model to compare and integrate visual reading with two independent semi-quantification methods in order to obtain a tracer-independent multi-parametric evaluation.MethodsWe retrospectively enrolled three cohorts of cognitively impaired patients submitted to 18F-florbetaben (53 subjects), 18F-flutemetamol (62 subjects), 18F-florbetapir (60 subjects) PET/CT respectively, in 6 European centres belonging to the EADC. The 175 scans were visually classified as positive/negative following approved criteria and further classified with a 5-step grading as negative, mild negative, borderline, mild positive, positive by 5 independent readers, blind to clinical data. Scan quality was also visually assessed and recorded. Semi-quantification was based on two quantifiers: the standardized uptake value (SUVr) and the ELBA method. We used a sigmoid model to relate the grading with the quantifiers. We measured the readers accord and inconsistencies in the visual assessment as well as the relationship between discrepancies on the grading and semi-quantifications.ConclusionIt is possible to construct a map between different tracers and different quantification methods without resorting to ad-hoc acquired cases. We used a 5-level visual scale which, together with a mathematical model, delivered cut-offs and transition regions on tracers that are (largely) independent from the population. All fluorinated tracers appeared to have the same contrast and discrimination ability with respect to the negative-to-positive grading. We validated the integration of both visual reading and different quantifiers in a more robust framework thus bridging the gap between a binary and a user-independent continuous scale.

Project description:Dedicated breast PET/CT is expected to have utility in local staging, surgical planning, monitoring of therapy response, and detection of residual disease for breast cancer. Quantitative metrics will be integral to several such applications. The authors present a validation of fully 3D data correction schemes for a custom built dedicated breast PET/CT (DbPET/CT) scanner via (18)F-FDG phantom scans.A component-based normalization was implemented, live-time was estimated with a multicomponent model, and a variance reduced randoms estimate was computed from delayed coincidences. Attenuation factors were calculated by using a CT based segmentation scheme while scatter was computed using a Monte Carlo (MC) simulation method. As no performance standard currently exists for breast PET systems, custom performance tests were created based on prior patient imaging results. Count-rate linearity for live-time and randoms corrections was measured with a decay experiment for a solid polyethylene cylinder phantom with an offset line source. A MC simulation was used to validate attenuation correction, a multicompartment phantom with asymmetric activity distribution provided an assessment of scatter correction, and image uniformity after geometric and detector normalization was measured from a high count scan of a uniform cylinder phantom. Raw data were reconstructed with filtered back projection (FBP) after Fourier rebinning. To quantify performance absolute activity concentrations, contrast recovery coefficients and image uniformity were calculated through region of interest analysis.The most significant source of error was attributed to mispositioning of events due to pile-up, presenting in count-related axial and transaxial nonuniformities that were not corrected for with the normalization method used here. Within the range of singles counts observed during clinical trials residual error after applying all corrections was comparable to that of a commercial whole body PET/CT system.The results suggest that DbPET/CT is capable of producing quantitative images under the operating conditions expected during patient imaging.

Dataset Information

Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification.

Publications

Low-dose CT for the spatial normalization of PET images: A validation procedure for amyloid-PET semi-quantification.

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