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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↗