Matches in SemOpenAlex for { <https://semopenalex.org/work/W3190689644> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W3190689644 endingPage "573" @default.
- W3190689644 startingPage "564" @default.
- W3190689644 abstract "Positron emission tomography (PET) imaging is used to track biochemical processes in the human body. PET image quality is limited by noise and several methods have been implemented to improve the quality. Kernel-based image reconstruction is among the methods implemented to increase PET image quality and commonly uses a Gaussian kernel to include spatial correlations from image priors into the forward projection model of PET. Unfortunately, the Gaussian kernel tends to smooth details in the reconstructed image. To reduce noise without losing contrast details, a different kernel is needed. Wavelet kernels can be more efficient than the Gaussian kernel in reducing noise while keeping contrast details by better separating signal from noise and thus, it does not over smooth peak values in the final reconstructed images. In this work, we evaluate a wavelet kernel for kernel-based PET image reconstruction. For this goal, a wavelet kernel approach has been tested using simulated brain data, physical phantom data, and patient data. Reconstruction results are presented and discussed in detail comparing the wavelet kernel method with the Gaussian kernel method. Our results suggest that a wavelet kernel performs better in contrast recovery for phantoms and also results in higher signal-to-noise ratio (SNR) for real patient data." @default.
- W3190689644 created "2021-08-16" @default.
- W3190689644 creator A5001546768 @default.
- W3190689644 creator A5036637940 @default.
- W3190689644 creator A5057150859 @default.
- W3190689644 creator A5060704251 @default.
- W3190689644 date "2022-05-01" @default.
- W3190689644 modified "2023-09-23" @default.
- W3190689644 title "Evaluation of Wavelet Kernel-Based PET Image Reconstruction" @default.
- W3190689644 cites W2014351445 @default.
- W3190689644 cites W2021594562 @default.
- W3190689644 cites W2024668293 @default.
- W3190689644 cites W2042031583 @default.
- W3190689644 cites W2045112400 @default.
- W3190689644 cites W2060845446 @default.
- W3190689644 cites W2069629287 @default.
- W3190689644 cites W2096957883 @default.
- W3190689644 cites W2112155856 @default.
- W3190689644 cites W2129683196 @default.
- W3190689644 cites W2148956829 @default.
- W3190689644 cites W2149400409 @default.
- W3190689644 cites W2154744699 @default.
- W3190689644 cites W2167072605 @default.
- W3190689644 cites W2404583896 @default.
- W3190689644 cites W2517088289 @default.
- W3190689644 cites W2767848063 @default.
- W3190689644 cites W2770686150 @default.
- W3190689644 cites W2807474247 @default.
- W3190689644 cites W2906587342 @default.
- W3190689644 cites W2952506191 @default.
- W3190689644 cites W2979642984 @default.
- W3190689644 cites W3015651420 @default.
- W3190689644 cites W3162118180 @default.
- W3190689644 cites W2543859967 @default.
- W3190689644 doi "https://doi.org/10.1109/trpms.2021.3103104" @default.
- W3190689644 hasPublicationYear "2022" @default.
- W3190689644 type Work @default.
- W3190689644 sameAs 3190689644 @default.
- W3190689644 citedByCount "2" @default.
- W3190689644 countsByYear W31906896442022 @default.
- W3190689644 countsByYear W31906896442023 @default.
- W3190689644 crossrefType "journal-article" @default.
- W3190689644 hasAuthorship W3190689644A5001546768 @default.
- W3190689644 hasAuthorship W3190689644A5036637940 @default.
- W3190689644 hasAuthorship W3190689644A5057150859 @default.
- W3190689644 hasAuthorship W3190689644A5060704251 @default.
- W3190689644 hasConcept C114614502 @default.
- W3190689644 hasConcept C115961682 @default.
- W3190689644 hasConcept C121332964 @default.
- W3190689644 hasConcept C153180895 @default.
- W3190689644 hasConcept C154945302 @default.
- W3190689644 hasConcept C163716315 @default.
- W3190689644 hasConcept C31972630 @default.
- W3190689644 hasConcept C33923547 @default.
- W3190689644 hasConcept C41008148 @default.
- W3190689644 hasConcept C47432892 @default.
- W3190689644 hasConcept C62520636 @default.
- W3190689644 hasConcept C7218915 @default.
- W3190689644 hasConcept C74193536 @default.
- W3190689644 hasConcept C99498987 @default.
- W3190689644 hasConceptScore W3190689644C114614502 @default.
- W3190689644 hasConceptScore W3190689644C115961682 @default.
- W3190689644 hasConceptScore W3190689644C121332964 @default.
- W3190689644 hasConceptScore W3190689644C153180895 @default.
- W3190689644 hasConceptScore W3190689644C154945302 @default.
- W3190689644 hasConceptScore W3190689644C163716315 @default.
- W3190689644 hasConceptScore W3190689644C31972630 @default.
- W3190689644 hasConceptScore W3190689644C33923547 @default.
- W3190689644 hasConceptScore W3190689644C41008148 @default.
- W3190689644 hasConceptScore W3190689644C47432892 @default.
- W3190689644 hasConceptScore W3190689644C62520636 @default.
- W3190689644 hasConceptScore W3190689644C7218915 @default.
- W3190689644 hasConceptScore W3190689644C74193536 @default.
- W3190689644 hasConceptScore W3190689644C99498987 @default.
- W3190689644 hasFunder F4320334593 @default.
- W3190689644 hasIssue "5" @default.
- W3190689644 hasLocation W31906896441 @default.
- W3190689644 hasOpenAccess W3190689644 @default.
- W3190689644 hasPrimaryLocation W31906896441 @default.
- W3190689644 hasRelatedWork W1970633265 @default.
- W3190689644 hasRelatedWork W1982584880 @default.
- W3190689644 hasRelatedWork W2044965577 @default.
- W3190689644 hasRelatedWork W2060018053 @default.
- W3190689644 hasRelatedWork W2110459882 @default.
- W3190689644 hasRelatedWork W2142459683 @default.
- W3190689644 hasRelatedWork W2148116311 @default.
- W3190689644 hasRelatedWork W2382457678 @default.
- W3190689644 hasRelatedWork W2541950815 @default.
- W3190689644 hasRelatedWork W3143795982 @default.
- W3190689644 hasVolume "6" @default.
- W3190689644 isParatext "false" @default.
- W3190689644 isRetracted "false" @default.
- W3190689644 magId "3190689644" @default.
- W3190689644 workType "article" @default.