FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3040887003> ?p ?o ?g. }

• W3040887003 abstract "Computed tomography (CT) simulators are versatile tools for scanning protocol evaluation, optimization of geometrical design parameters, assessment of image reconstruction algorithms, and evaluation of the impact of future innovations attempting to improve the performance of CT scanners. Computational human phantoms (CHPs) play a key role in simulators for the radiation dosimetry and assessment of image quality tasks in the medical x-ray systems. Since the construction of patient-specific CHPs can be both difficult and time-consuming, nominal standard/reference CHPs have been established, yielding significant discrepancies in the special design and optimization demands of patient dose and imaging protocols for most medical applications. Therefore, the aim of this work was to develop a personalized Monte-Carlo (MC) CT simulator equipped with a fast and well-structured tool-kit called DeepSegNet for automatic generation of patient-specific CHPs based on MRI images, working under two principal algorithms. To this end, we first developed a 3D convolutional neural network (3DCNN) for the automated segmentation of 3D MRI images to detect anatomical organs/tissues. Then, a 3D voxel merging (3DVM) algorithm constructing CHPs and making fast MC calculations were developed. The proposed 3DCNN benefits from the main merit of residual networks by designing a 15-layer model. Next, the 3DVM algorithm utilizes the segmented data acquired from the former step, to create realistic and optimized CHPs by material mapping and voxel size manipulating. The performance of our 3DCNN model on 20 patients as test cases was 84.54% and 74.52% in terms of average accuracy and Dice-Coefficient, respectively, outperforming SegNet, as a comparable method by 2%. Finally, we developed an MC CT simulator by implementing a set of our generated CHPs. The efficiency of our 3DVM algorithm in constructing CHPs was assessed in terms of MC execution time and the number of merged voxels representing occupied storage memory and compared to the existing lattice method. Besides, the accuracy of our 3DVM investigated through the estimation of patient dose maps and image reconstruction. Results demonstrated a significant reduction of about 96% in the number of voxels and a 15% reduction in MC execution time for x-ray photon transportation while keeping the same accuracy. Therefore, this software package has a strong potential in the optimization of therapeutic and radiological imaging procedures." @default.
• W3040887003 created "2020-07-16" @default.
• W3040887003 creator A5031517308 @default.
• W3040887003 creator A5040889307 @default.
• W3040887003 creator A5069082023 @default.
• W3040887003 creator A5083961372 @default.
• W3040887003 creator A5085220942 @default.
• W3040887003 date "2020-06-01" @default.
• W3040887003 modified "2023-09-25" @default.
• W3040887003 title "Personalized Computational Human Phantoms via a Hybrid Model-based Deep Learning Method" @default.
• W3040887003 cites W168591761 @default.
• W3040887003 cites W1973457617 @default.
• W3040887003 cites W1978330980 @default.
• W3040887003 cites W1984495711 @default.
• W3040887003 cites W2003324024 @default.
• W3040887003 cites W2013460930 @default.
• W3040887003 cites W2035759004 @default.
• W3040887003 cites W2055894011 @default.
• W3040887003 cites W2063018753 @default.
• W3040887003 cites W2081295822 @default.
• W3040887003 cites W2117867888 @default.
• W3040887003 cites W2138738604 @default.
• W3040887003 cites W2150188850 @default.
• W3040887003 cites W2163239918 @default.
• W3040887003 cites W2169496790 @default.
• W3040887003 cites W2194775991 @default.
• W3040887003 cites W2286434735 @default.
• W3040887003 cites W2301358467 @default.
• W3040887003 cites W2464708700 @default.
• W3040887003 cites W2524496318 @default.
• W3040887003 cites W2575866797 @default.
• W3040887003 cites W2608353599 @default.
• W3040887003 cites W2962914239 @default.
• W3040887003 cites W2963881378 @default.
• W3040887003 cites W2967012607 @default.
• W3040887003 cites W2968771585 @default.
• W3040887003 cites W2968773479 @default.
• W3040887003 cites W3005328939 @default.
• W3040887003 cites W3041141384 @default.
• W3040887003 cites W3041887767 @default.
• W3040887003 cites W369501120 @default.
• W3040887003 doi "https://doi.org/10.1109/memea49120.2020.9137114" @default.
• W3040887003 hasPublicationYear "2020" @default.
• W3040887003 type Work @default.
• W3040887003 sameAs 3040887003 @default.
• W3040887003 citedByCount "2" @default.
• W3040887003 countsByYear W30408870032020 @default.
• W3040887003 countsByYear W30408870032022 @default.
• W3040887003 crossrefType "proceedings-article" @default.
• W3040887003 hasAuthorship W3040887003A5031517308 @default.
• W3040887003 hasAuthorship W3040887003A5040889307 @default.
• W3040887003 hasAuthorship W3040887003A5069082023 @default.
• W3040887003 hasAuthorship W3040887003A5083961372 @default.
• W3040887003 hasAuthorship W3040887003A5085220942 @default.
• W3040887003 hasConcept C105795698 @default.
• W3040887003 hasConcept C108583219 @default.
• W3040887003 hasConcept C115961682 @default.
• W3040887003 hasConcept C141379421 @default.
• W3040887003 hasConcept C142724271 @default.
• W3040887003 hasConcept C154945302 @default.
• W3040887003 hasConcept C19499675 @default.
• W3040887003 hasConcept C204787440 @default.
• W3040887003 hasConcept C2780385302 @default.
• W3040887003 hasConcept C2989005 @default.
• W3040887003 hasConcept C31601959 @default.
• W3040887003 hasConcept C31972630 @default.
• W3040887003 hasConcept C33923547 @default.
• W3040887003 hasConcept C41008148 @default.
• W3040887003 hasConcept C54170458 @default.
• W3040887003 hasConcept C55020928 @default.
• W3040887003 hasConcept C71924100 @default.
• W3040887003 hasConcept C75088862 @default.
• W3040887003 hasConcept C81363708 @default.
• W3040887003 hasConcept C89600930 @default.
• W3040887003 hasConceptScore W3040887003C105795698 @default.
• W3040887003 hasConceptScore W3040887003C108583219 @default.
• W3040887003 hasConceptScore W3040887003C115961682 @default.
• W3040887003 hasConceptScore W3040887003C141379421 @default.
• W3040887003 hasConceptScore W3040887003C142724271 @default.
• W3040887003 hasConceptScore W3040887003C154945302 @default.
• W3040887003 hasConceptScore W3040887003C19499675 @default.
• W3040887003 hasConceptScore W3040887003C204787440 @default.
• W3040887003 hasConceptScore W3040887003C2780385302 @default.
• W3040887003 hasConceptScore W3040887003C2989005 @default.
• W3040887003 hasConceptScore W3040887003C31601959 @default.
• W3040887003 hasConceptScore W3040887003C31972630 @default.
• W3040887003 hasConceptScore W3040887003C33923547 @default.
• W3040887003 hasConceptScore W3040887003C41008148 @default.
• W3040887003 hasConceptScore W3040887003C54170458 @default.
• W3040887003 hasConceptScore W3040887003C55020928 @default.
• W3040887003 hasConceptScore W3040887003C71924100 @default.
• W3040887003 hasConceptScore W3040887003C75088862 @default.
• W3040887003 hasConceptScore W3040887003C81363708 @default.
• W3040887003 hasConceptScore W3040887003C89600930 @default.
• W3040887003 hasLocation W30408870031 @default.
• W3040887003 hasOpenAccess W3040887003 @default.
• W3040887003 hasPrimaryLocation W30408870031 @default.
• W3040887003 hasRelatedWork W12703013 @default.
• W3040887003 hasRelatedWork W12793662 @default.
• W3040887003 hasRelatedWork W14869171 @default.

• next