Dataset Information

Regional 18F-fluoromisonidazole PET images generated from multiple advanced MR images using neural networks in glioblastoma.

ABSTRACT: Generated 18F-fluoromisonidazole (18F-FMISO) positron emission tomography (PET) images for glioblastoma are highly sought after because 18F-FMISO can be radioactive, and the imaging procedure is not easy. This study aimed to explore the feasibility of using advanced magnetic resonance (MR) images to generate regional 18F-FMISO PET images and its predictive value for survival. Twelve kinds of advanced MR images of 28 patients from The Cancer Imaging Archive were processed. Voxel-by-voxel correlation analysis between 18F-FMISO images and advanced MR images was performed to select the MR images for generating regional 18F-FMISO images. Neural network algorithms provided by the MATLAB toolbox were used to generate regional 18F-FMISO images. The mean square error (MSE) was used to evaluate the regression effect. The prognostic value of generated 18F-FMISO images was evaluated by the Mantel-Cox test. A total of 299 831 voxels were extracted from the segmented regions of all patients. Eleven kinds of advanced MR images were selected to generate 18F-FMISO images. The best neural network algorithm was Bayesian regularization. The MSEs of the training, validation, and testing groups were 2.92E-2, 2.9E-2, and 2.92E-2, respectively. Both the maximum Tissue/Blood ratio (P = .017) and hypoxic volume (P = .023) of the generated images were predictive factors of overall survival, but only hypoxic volume (P = .029) was a predictive factor of progression-free survival. Multiple advanced MR images are feasible to generate qualified regional 18F-FMISO PET images using neural networks. The generated images also have predictive value in the prognostic evaluation of glioblastoma.

SUBMITTER: Qin J

PROVIDER: S-EPMC9333488 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Regional 18F-fluoromisonidazole PET images generated from multiple advanced MR images using neural networks in glioblastoma.

Qin Jianhua J Tang Yu Y Wang Bao B

Medicine 20220729 30

Generated 18F-fluoromisonidazole (18F-FMISO) positron emission tomography (PET) images for glioblastoma are highly sought after because 18F-FMISO can be radioactive, and the imaging procedure is not easy. This study aimed to explore the feasibility of using advanced magnetic resonance (MR) images to generate regional 18F-FMISO PET images and its predictive value for survival. Twelve kinds of advanced MR images of 28 patients from The Cancer Imaging Archive were processed. Voxel-by-voxel correlat ...[more]

PMID: 35905276

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Project description:Positron emission tomography (PET)/computed tomography (CT) using the radiotracer 18F-Fluoromisonidazole (FMISO) has been widely employed to image tumour hypoxia and is of interest to help develop novel hypoxia modifiers and guide radiation treatment planning. Yet, the optimal post-injection (p.i.) timing of hypoxic imaging remains questionable. Therefore, we investigated the correlation between hypoxia-related quantitative values in FMISO-PET acquired at 2 and 4 h p.i. in patients with non-small cell lung cancer (NSCLC). Patients with resectable NSCLC participated in the ATOM clinical trial (NCT02628080) which investigated the hypoxia modifying effects of atovaquone. Two-hour and four-hour FMISO PET/CT images acquired at baseline and pre-surgery visits (n = 58) were compared. Cohort 1 (n = 14) received atovaquone treatment, while cohort 2 (n = 15) did not. Spearman's rank correlation coefficients (ρ) assessed the relationship between hypoxia-related metrics, including standardised uptake value (SUV), tumour-to-blood ratio (TBR), and tumour hypoxic volume (HV) defined by voxels with TBR ≥ 1.4. As the primary imaging-related trial endpoint used to evaluate the action of atovaquone on tumour hypoxia in patients with NSCLC was change in tumour HV from baseline, this was also assessed in patients (n = 20) with sufficient baseline 2- and 4-h scan HV to reliably measure change (predefined as ≥ 1.5 mL). Tumours were divided into four subregions or distance categories: edge, outer, inner, and centre, using MATLAB. In tumours overall, strong correlation (P < 0.001) was observed for SUVmax ρ = 0.87, SUVmean ρ = 0.91, TBRmax ρ = 0.83 and TBRmean ρ = 0.81 between 2- and 4-h scans. Tumour HV was moderately correlated (P < 0.001) with ρ = 0.69 between 2- and 4-h scans. Yet, in tumour subregions, the correlation of HV decreased from the centre ρ = 0.71 to the edge ρ = 0.45 (P < 0.001). SUV, TBR, and HV values were consistently higher on 4-h scans than on 2-h scans, indicating better tracer-to-background contrast. For instance, for TBRmax, the mean, median, and interquartile range were 1.9, 1.7, and 1.6-2.0 2-h p.i., and 2.6, 2.4, and 2.0-3.0 4-h p.i., respectively. Our results support that FMISO-PET scans should be performed at 4 h p.i. to evaluate tumour hypoxia in NSCLC.Trial registration: ClinicalTrials.gov, NCT02628080. Registered 11/12/2015, https://clinicaltrials.gov/ct2/show/NCT02628080 .

Project description:Boron-10-containing positron emission tomography (PET) radio-tracer, 18F-FBPA, has been used to evaluate the feasibility and treatment outcomes of Boron neutron capture therapy (BNCT). The clinical use of PET/MR is increasing and reveals its benefit in certain applications. However, the PET/CT is still the most widely used modality for daily PET practice due to its high quantitative accuracy and relatively low cost. Considering the different attenuation correction maps between PET/CT and PET/MR, comparison of derived image features from these two modalities is critical to identify quantitative imaging biomarkers for diagnosis and prognosis. This study aimed to investigate the comparability of image features extracted from 18F-FBPA PET/CT and PET/MR. A total of 15 patients with malignant brain tumor who underwent 18F-FBPA examinations using both PET/CT and PET/MR on the same day were retrospectively analyzed. Overall, four conventional imaging characteristics and 449 radiomic features were calculated from PET/CT and PET/MR, respectively. A linear regression model and intraclass correlation coefficient (ICC) were estimated to evaluate the comparability of derived features between two modalities. Features were classified into strong, moderate, and weak comparability based on coefficient of determination (r2) and ICC. All of the conventional features, 81.2% of histogram, 37.5% of geometry, 51.5% of texture, and 25% of wavelet-based features, showed strong comparability between PET/CT and PET/MR. With regard to the wavelet filtering, radiomic features without filtering (61.2%) or with low-pass filtering (59.2%) along three axes produced strong comparability between the two modalities. However, only 8.2% of the features with high-pass filtering showed strong comparability. The linear regression models were provided for the features with strong and moderate consensus to interchange the quantitative features between the PET/CT and the PET/MR. All of the conventional and 71% of the radiomic (mostly histogram and texture) features were sufficiently stable and could be interchanged between 18F-FBPA PET with different hybrid modalities using the proposed equations. Our findings suggested that the image features high interchangeability may facilitate future studies in comparing PET/CT and PET/MR.

Project description:Mapping the human brain is one of the great scientific challenges of the 21st century. Brain network analysis is an effective technique based on graph theory that is widely used to investigate network patterns in the human brain. Currently, mapping an individual brain network using a single image has been a hotspot in the field of brain science; techniques, such as the Kullback-Leibler (KL) method, have applications in structural Magnetic Resonance (MR) imaging. However, maintaining an image's intensity, shape, texture and gradient information during feature extraction is very challenging. In this study, we propose a novel method for individual-level network construction based on the high-resolution Brainnetome Atlas, which shows 246 brain regions. Principal components (PCs) were obtained for each brain region using principal component analysis (PCA) for feature extraction. Individual brain networks were followed and used to construct the PC similarity measurement based on the mutual information (MI) method. To evaluate the robustness of the proposed method, three independent experiments were carried out. In the first, 34 healthy subjects underwent two Carbon 11-labeled Pittsburgh compound B Positron emission tomography (11C-PiB PET) scans; in the second, 32 healthy subjects underwent two structural MRI scans; and in the last, 10 Alzheimer's disease (AD) subjects and 10Healthy Control (HC) subjects underwent 11C-PiB PET scans. For each subject, network metrics including clustering coefficient, path length, small-world coefficient, efficiency and node betweenness centrality were calculated. The results suggested that both the individual PET and structural MRI networks exhibited a good small-word property, and the variances within subjects was also quite small in all metrics, The average value of Coefficient of variation (CV) map was 0.33 and 0.32 for PiB PET and MR images respectively, and intra-class correlation coefficients (ICC) range from approximately 0.4 to 0.7, indicating that the new method was well adapted to the subjects. The results of intra-class correlation coefficients from the test-retest experiment were consistent with previous research employing KL divergence, but with low computational complexity. Further, differences between AD subjects and HC subjects can be observed in network metrics. The method proposed herein provides a new perspective for investigating individual brain connectivity; it would enable neuroscientists to further understand the functions of the human brain.

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:The positron emission tomography (PET) tracer [18F]MNI-659, selective for phosphodiesterase 10A (PDE10A), is a promising tool to assess an early biomarker for Huntington's disease (HD). In this study we investigated [18F]MNI-659 uptake in the Q175 mouse model of HD. Given the focal striatal distribution of PDE10A as well as the striatal atrophy occurring in HD, the spatial normalization approach applied during the processing could sensibly affect the accuracy of the regional quantification. We compared the use of a magnetic resonance images (MRI) template based on individual MRI over a PET and CT templates for regional quantification and spatial normalization of [18F]MNI-659 PET images. We performed [18F]MNI-659 PET imaging in six months old heterozygous (HET) Q175 mice and wild-type (WT) littermates, followed by X-ray computed tomography (CT) scan. In the same week, individual T2-weighted MRI were acquired. Spatial normalization and regional quantification of the PET/CT images was performed on MRI, [18F]MNI-659 PET, or CT template and compared to binding potential (BPND) using volumes manually delineated on the individual MR images. Striatal volume was significantly reduced in HET mice (-7.7%, p<0.0001) compared to WT littermates. [18F]MNI-659 BPND in striatum of HET animals was significantly reduced (p<0.0001) when compared to WT littermates using all three templates. However, BPND values were significantly higher for HET mice using the PET template compared to the MRI and CT ones (p<0.0001), with an overestimation at lower activities. On the other hand, the CT template spatial normalization introduced larger variability reducing the effect size. The PET and CT template-based approaches resulted in a lower accuracy in BPND quantification with consequent decrease in the detectability of disease effect. This study demonstrates that for [18F]MNI-659 brain PET imaging in mice the use of an MRI-based spatial normalization is recommended to achieve accurate quantification and fully exploit the detectability of disease effect.

Dataset Information

Regional 18F-fluoromisonidazole PET images generated from multiple advanced MR images using neural networks in glioblastoma.

Publications

Regional 18F-fluoromisonidazole PET images generated from multiple advanced MR images using neural networks in glioblastoma.

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