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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2014

SU-E-J-111: The Contouring Error of the Parotids Based On the CT and MRI Images in Radiotherapy Planning for Nasopharyngeal Carcinoma

Purpose: To analyze the variation of sketching the parotid for patients with nasopharyngeal carcinoma who underwent radiotherapy based on computed tomography (CT) and magnetic resonance(MR) images. Methods: 41 nasopharyngeal cancer patients were randomly selected. Each patient underwent MR and CT scanning. The Gross Tumor Volume and Organs at risk were contoured on both contrasted CT and T1-MR images. For each patient, one radiotherapist sketched the parotid on CT and MR images for 10 times, and 10 different radiotherapists were asked to sketching the parotid on CT and MR images only one time. The inter- and intra-observers volumes and outline variations were compared. Results: The volumes of parotid contoured by inter-observer on CT and MR images were 34.6±12.1cm{sup 3}(left),34.3±9.0cm{sup 3}(right) and 24.6±7.6cm{sup 3}(L),23.2±8.1cm{sup 3}(R); In the same way, for intra-observer on CT and MR images the volumes were 28.2±7.6cm{sup 3}(L),29.4±9.4cm{sup 3}(R) and 24.4±7.6cm{sup 3}(L),22.5±7.4cm{sup 3}(R), respectively. The variable ratios of volume on MR images were 4.7±0.7%(L),5.0±0.6%(R) for inter-observer and 2.3±0.4%(L),2.1±0.7%(R) for intra-observer. Similarly, The inter- and intra-observer ratios for contouring on CT images reached 18.0±4.8%(L),17.4±4.6%(R) and 6.3±1.5%(L),6.8±1.5%(R), respectively. Conclusion: Contouring the parotids on MR images was more accurate and reproducible than that on CT images.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-330: To Analyze the Calculation Error of Live Dose-Volume Indices Applying 4D-CT in Radiotherapy for PTVs Within the Liver Completely

Purpose: To study the variation rule of normal liver dose-volume indices calculation for the liver malignancy patients whose plan target volumes were in the liver completely in all breath phases. Methods: Ten patients who accepted radiotherapy for malignant tumor were selected in our study. All patients underwent 4D-CT simulation and 3D-CT simulation in free breathing(FB). 4D-CT was sorted into 10 series CT images according to breath phase, named CT0, CT10 to CT90, respectively. And GTVs were contoured on different CT series, and the individual target volume(ITV) was obtained by merging 10 GTVs from 4D-CT. The PTVs were obtained from ITV applying margins. The PTVs were not beyond the boundary of liver in all breath phase observed by dynamic 4D-CT. The radiotherapy plans were designed and irradiation dose was calculated on 3D-CT images, and the livers were contoured on different series CT images and mapped to 3D-CT images applying rigid registration. To compare the dose-volume difference of livers based on distinct CT images. Results: (1)The liver volumes were similar on 4D-CT and 3D-CT images(CTFB 1485±500cm{sup 3}, CT0 1413±377cm{sup 3}, CT10 1409±396cm{sup 3}, CT20 1419±418cm{sup 3},CT30 1485±500cm{sup 3}, CT40 1438±392cm{sup 3}, CT50 1437±404cm{sup 3}, CT60 1439±409cm{sup 3}, CT70 1408±393cm{sup 3}, CT80 1384±397cm{sup 3}, CT90 1398±397cm{sup 3}; F=0.064,p=1.00) (2) The PTVs volume were 30.17±14.62cm{sup 3};(3) The mean dose and V5 to V10 of liver were similar among 4D-CT different series CT images(p>0.05), and the indices varied less than ±4% refer to liver on CT50. Conclusion: The calculation affection of liver dose-volume indices induced by breath motion were not significant for the PTV within liver completely as estimation before. And more objective prediction indices for radiation induced l.

60 APPLIED LIFE SCIENCES↗
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↗