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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-E-QI-03: Compartment Modeling of Dynamic Brain PET - The Effect of Scatter and Random Corrections On Parameter Errors

Purpose: To investigate the effects of corrections for random and scattered coincidences on kinetic parameters in brain tumors, by using ten Monte Carlo (MC) simulated dynamic FLT-PET brain scans. Methods: The GATE MC software was used to simulate ten repetitions of a 1 hour dynamic FLT-PET scan of a voxelized head phantom. The phantom comprised six normal head tissues, plus inserted regions for blood and tumor tissue. Different time-activity-curves (TACs) for all eight tissue types were used in the simulation and were generated in Matlab using a 2-tissue model with preset parameter values (K1,k2,k3,k4,Va,Ki). The PET data was reconstructed into 28 frames by both ordered-subset expectation maximization (OSEM) and 3D filtered back-projection (3DFBP). Five image sets were reconstructed, all with normalization and different additional corrections C (A=attenuation, R=random, S=scatter): Trues (AC), trues+randoms (ARC), trues+scatters (ASC), total counts (ARSC) and total counts (AC). Corrections for randoms and scatters were based on real random and scatter sinograms that were back-projected, blurred and then forward projected and scaled to match the real counts. Weighted non-linearleast- squares fitting of TACs from the blood and tumor regions was used to obtain parameter estimates. Results: The bias was not significantly different for trues (AC), trues+randoms (ARC), trues+scatters (ASC) and total counts (ARSC) for either 3DFBP or OSEM (p<0.05). Total counts with only AC stood out however, with an up to 160% larger bias. In general, there was no difference in bias found between 3DFBP and OSEM, except in parameter Va and Ki. Conclusion: According to our results, the methodology of correcting the PET data for randoms and scatters performed well for the dynamic images where frames have much lower counts compared to static images. Generally, no bias was introduced by the corrections and their importance was emphasized since omitting them increased bias extensively.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Correction of quantification errors in pelvic and spinal lesions caused by ignoring higher photon attenuation of bone in [{sup 18}F]NaF PET/MR

Purpose: MR-based attenuation correction (MRAC) in routine clinical whole-body positron emission tomography and magnetic resonance imaging (PET/MRI) is based on tissue type segmentation. Due to lack of MR signal in cortical bone and the varying signal of spongeous bone, standard whole-body segmentation-based MRAC ignores the higher attenuation of bone compared to the one of soft tissue (MRAC{sub nobone}). The authors aim to quantify and reduce the bias introduced by MRAC{sub nobone} in the standard uptake value (SUV) of spinal and pelvic lesions in 20 PET/MRI examinations with [{sup 18}F]NaF. Methods: The authors reconstructed 20 PET/MR [{sup 18}F]NaF patient data sets acquired with a Philips Ingenuity TF PET/MRI. The PET raw data were reconstructed with two different attenuation images. First, the authors used the vendor-provided MRAC algorithm that ignores the higher attenuation of bone to reconstruct PET{sub nobone}. Second, the authors used a threshold-based algorithm developed in their group to automatically segment bone structures in the [{sup 18}F]NaF PET images. Subsequently, an attenuation coefficient of 0.11 cm{sup −1} was assigned to the segmented bone regions in the MRI-based attenuation image (MRAC{sub bone}) which was used to reconstruct PET{sub bone}. The automatic bone segmentation algorithm was validated in six PET/CT [{sup 18}F]NaF examinations. Relative SUV{sub mean} and SUV{sub max} differences between PET{sub bone} and PET{sub nobone} of 8 pelvic and 41 spinal lesions, and of other regions such as lung, liver, and bladder, were calculated. By varying the assigned bone attenuation coefficient from 0.11 to 0.13 cm{sup −1}, the authors investigated its influence on the reconstructed SUVs of the lesions. Results: The comparison of [{sup 18}F]NaF-based and CT-based bone segmentation in the six PET/CT patients showed a Dice similarity of 0.7 with a true positive rate of 0.72 and a false discovery rate of 0.33. The [{sup 18}F]NaF-based bone segmentation worked well in the pelvis and spine. However, it showed artifacts in the skull and in the extremities. The analysis of the 20 [{sup 18}F]NaF PET/MRI examinations revealed relative SUV{sub max} differences between PET{sub nobone} and PET{sub bone} of (−8.8% ± 2.7%, p = 0.01) and (−8.1% ± 1.9%, p = 2.4 × 10{sup −8}) in pelvic and spinal lesions, respectively. A maximum SUV{sub max} underestimation of −13.7% was found in lesion in the third cervical spine. The averaged SUV{sub mean} differences in volumes of interests in lung, liver, and bladder were below 3%. The average SUV{sub max} differences in pelvic and spinal lesions increased from −9% to −18% and −8% to −17%, respectively, when increasing the assigned bone attenuation coefficient from 0.11 to 0.13 cm{sup −1}. Conclusions: The developed automatic [{sup 18}F]NaF PET-based bone segmentation allows to include higher bone attenuation in whole-body MRAC and thus improves quantification accuracy for pelvic and spinal lesions in [{sup 18}F]NaF PET/MRI examinations. In nonbone structures (e.g., lung, liver, and bladder), MRAC{sub nobone} yields clinically acceptable accuracy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

A GATE evaluation of the sources of error in quantitative {sup 90}Y PET

Purpose: Accurate reconstruction of the dose delivered by {sup 90}Y microspheres using a postembolization PET scan would permit the establishment of more accurate dose–response relationships for treatment of hepatocellular carcinoma with {sup 90}Y. However, the quality of the PET data obtained is compromised by several factors, including poor count statistics and a very high random fraction. This work uses Monte Carlo simulations to investigate what impact factors other than low count statistics have on the quantification of {sup 90}Y PET. Methods: PET acquisitions of two phantoms—a NEMA PET phantom and the NEMA IEC PET body phantom-containing either {sup 90}Y or {sup 18}F were simulated using GATE. Simulated projections were created with subsets of the simulation data allowing the contributions of random, scatter, and LSO background to be independently evaluated. The simulated projections were reconstructed using the commercial software for the simulated scanner, and the quantitative accuracy of the reconstruction and the contrast recovery of the reconstructed images were evaluated. Results: The quantitative accuracy of the {sup 90}Y reconstructions were not strongly influenced by the high random fraction present in the projection data, and the activity concentration was recovered to within 5% of the known value. The contrast recovery measured for simulated {sup 90}Y data was slightly poorer than that for simulated {sup 18}F data with similar count statistics. However, the degradation was not strongly linked to any particular factor. Using a more restricted energy range to reduce the random fraction in the projections had no significant effect. Conclusions: Simulations of {sup 90}Y PET confirm that quantitative {sup 90}Y is achievable with the same approach as that used for {sup 18}F, and that there is likely very little margin for improvement by attempting to model aspects unique to {sup 90}Y, such as the much higher random fraction or the presence of bremsstrahlung in the singles data.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP

Remote access to large-scale scientific datasets, like those generated by combustion simulations or other high-performance computing (HPC) applications, presents a significant challenge. Downloading entire datasets is often impractical due to their size and the bandwidth limitations of typical networks. To address this challenge, we propose a novel approach that enables efficient remote access to large datasets distributed across multiple facilities. Our method enables technologies to download only the data values of a select variable, in a select region of interest, to a user-defined accuracy. For this purpose, we extended the ADIOS IO library to provide read functions with user-defined accuracy, a remote data server that understands multidimensional selections of specific variables, steps and accuracy from an ADIOS dataset, and which uses lossy compression on the remote site to reduce the data to be transferred back to the client. In addition, our extension of the ADIOS library collects metadata from multiple datasets in small files called Campaign Archives, which can be shared among project participants on any HPC, cloud or laptop, and which can easily facilitate the discovery of content and pointers to the data location as well as remote access to the data by local tools as if data was local. This feature called Campaign Management, enables a group of scientists to manage related datasets stored in multiple files, across multiple facilities as if it was in a single file/database. We demonstrate the effectiveness of our approach using a 1.5 TB dataset from the S3D combustion simulation on Frontier at the Oak Ridge Leadership Facility. Even a single variable from this dataset, at 64 GB, is too large to be processed on a standard laptop. We show two different reading patterns for 2D plots and 3D visualization, with careful settings that a scientist studying combustion data would do and show that running the same Python scripts on Frontier directly takes comparable time than running them on the local laptop with remote access to the data on Frontier.

Podhorszki, Norbert [ORNL] (ORCID:000000019647542X↗