Matches in SemOpenAlex for { <https://semopenalex.org/work/W4288805228> ?p ?o ?g. }
Showing items 1 to 69 of
69
with 100 items per page.
- W4288805228 abstract "Mapping computed tomography (CT) number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, magnetic resonance imaging (MRI), and advanced dual-energy CT (DECT) to derive accurate patient mass density maps. Seven tissue substitute MRI phantoms were used for PDMI-based material calibration. The training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold and tin filters. The feasibility investigation included an empirical DECT correlation and four residual networks (ResNet) derived from different training inputs and strategies by the PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet trained with and without a physics constraint using MRI and DECT images. PRN-DE and RN-DE represent ResNet trained with and without a physics constraint using DECT-only images. For the tissue surrogate study, PRN-MR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: -0.72%, 2.62%, -3.58% for adipose; -0.03%, -0.61%, and -0.18% for muscle; -0.58%, -1.36%, and -4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations. The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The physics-constrained training can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the PDMI framework has the potential to improve proton range uncertainty with accurate patient mass density maps." @default.
- W4288805228 created "2022-07-30" @default.
- W4288805228 creator A5002507883 @default.
- W4288805228 creator A5005154623 @default.
- W4288805228 creator A5010012421 @default.
- W4288805228 creator A5022112257 @default.
- W4288805228 creator A5026797400 @default.
- W4288805228 creator A5030054597 @default.
- W4288805228 creator A5050909682 @default.
- W4288805228 creator A5060384504 @default.
- W4288805228 creator A5062404217 @default.
- W4288805228 creator A5089103033 @default.
- W4288805228 date "2022-07-26" @default.
- W4288805228 modified "2023-09-27" @default.
- W4288805228 title "Multimodal Imaging-based Material Mass Density Estimation for Proton Therapy Using Physics-Constrained Deep Learning" @default.
- W4288805228 doi "https://doi.org/10.48550/arxiv.2207.13150" @default.
- W4288805228 hasPublicationYear "2022" @default.
- W4288805228 type Work @default.
- W4288805228 citedByCount "0" @default.
- W4288805228 crossrefType "posted-content" @default.
- W4288805228 hasAuthorship W4288805228A5002507883 @default.
- W4288805228 hasAuthorship W4288805228A5005154623 @default.
- W4288805228 hasAuthorship W4288805228A5010012421 @default.
- W4288805228 hasAuthorship W4288805228A5022112257 @default.
- W4288805228 hasAuthorship W4288805228A5026797400 @default.
- W4288805228 hasAuthorship W4288805228A5030054597 @default.
- W4288805228 hasAuthorship W4288805228A5050909682 @default.
- W4288805228 hasAuthorship W4288805228A5060384504 @default.
- W4288805228 hasAuthorship W4288805228A5062404217 @default.
- W4288805228 hasAuthorship W4288805228A5089103033 @default.
- W4288805228 hasBestOaLocation W42888052281 @default.
- W4288805228 hasConcept C108583219 @default.
- W4288805228 hasConcept C121332964 @default.
- W4288805228 hasConcept C126838900 @default.
- W4288805228 hasConcept C143409427 @default.
- W4288805228 hasConcept C150432741 @default.
- W4288805228 hasConcept C154945302 @default.
- W4288805228 hasConcept C2989005 @default.
- W4288805228 hasConcept C41008148 @default.
- W4288805228 hasConcept C555944384 @default.
- W4288805228 hasConcept C71924100 @default.
- W4288805228 hasConcept C76155785 @default.
- W4288805228 hasConceptScore W4288805228C108583219 @default.
- W4288805228 hasConceptScore W4288805228C121332964 @default.
- W4288805228 hasConceptScore W4288805228C126838900 @default.
- W4288805228 hasConceptScore W4288805228C143409427 @default.
- W4288805228 hasConceptScore W4288805228C150432741 @default.
- W4288805228 hasConceptScore W4288805228C154945302 @default.
- W4288805228 hasConceptScore W4288805228C2989005 @default.
- W4288805228 hasConceptScore W4288805228C41008148 @default.
- W4288805228 hasConceptScore W4288805228C555944384 @default.
- W4288805228 hasConceptScore W4288805228C71924100 @default.
- W4288805228 hasConceptScore W4288805228C76155785 @default.
- W4288805228 hasLocation W42888052281 @default.
- W4288805228 hasOpenAccess W4288805228 @default.
- W4288805228 hasPrimaryLocation W42888052281 @default.
- W4288805228 hasRelatedWork W10202958 @default.
- W4288805228 hasRelatedWork W2526871 @default.
- W4288805228 hasRelatedWork W2679414 @default.
- W4288805228 hasRelatedWork W4500673 @default.
- W4288805228 hasRelatedWork W5046839 @default.
- W4288805228 hasRelatedWork W6468916 @default.
- W4288805228 hasRelatedWork W6572092 @default.
- W4288805228 hasRelatedWork W7303821 @default.
- W4288805228 hasRelatedWork W9190101 @default.
- W4288805228 hasRelatedWork W9325159 @default.
- W4288805228 isParatext "false" @default.
- W4288805228 isRetracted "false" @default.
- W4288805228 workType "article" @default.