Matches in SemOpenAlex for { <https://semopenalex.org/work/W2792691864> ?p ?o ?g. }
Showing items 1 to 97 of
97
with 100 items per page.
- W2792691864 abstract "Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 ± 7.28% for B50f data set, 10.82 ± 6.71% for the B30f data set, and 8.87 ± 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a potential to improve the reliability of imaging biomarker, especially in evaluating the longitudinal changes of EI even when the patient CT scans were performed with different kernels." @default.
- W2792691864 created "2018-03-29" @default.
- W2792691864 creator A5008452010 @default.
- W2792691864 creator A5014835747 @default.
- W2792691864 creator A5015642585 @default.
- W2792691864 date "2018-02-27" @default.
- W2792691864 modified "2023-09-22" @default.
- W2792691864 title "Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema" @default.
- W2792691864 cites W1488392151 @default.
- W2792691864 cites W1490844403 @default.
- W2792691864 cites W1540856690 @default.
- W2792691864 cites W1979889457 @default.
- W2792691864 cites W1990658058 @default.
- W2792691864 cites W1998415587 @default.
- W2792691864 cites W2045735961 @default.
- W2792691864 cites W2592929672 @default.
- W2792691864 cites W4240772283 @default.
- W2792691864 doi "https://doi.org/10.1117/12.2295010" @default.
- W2792691864 hasPublicationYear "2018" @default.
- W2792691864 type Work @default.
- W2792691864 sameAs 2792691864 @default.
- W2792691864 citedByCount "2" @default.
- W2792691864 countsByYear W27926918642019 @default.
- W2792691864 countsByYear W27926918642020 @default.
- W2792691864 crossrefType "proceedings-article" @default.
- W2792691864 hasAuthorship W2792691864A5008452010 @default.
- W2792691864 hasAuthorship W2792691864A5014835747 @default.
- W2792691864 hasAuthorship W2792691864A5015642585 @default.
- W2792691864 hasConcept C105795698 @default.
- W2792691864 hasConcept C108583219 @default.
- W2792691864 hasConcept C114614502 @default.
- W2792691864 hasConcept C126838900 @default.
- W2792691864 hasConcept C136886441 @default.
- W2792691864 hasConcept C143409427 @default.
- W2792691864 hasConcept C144024400 @default.
- W2792691864 hasConcept C151730666 @default.
- W2792691864 hasConcept C153180895 @default.
- W2792691864 hasConcept C154945302 @default.
- W2792691864 hasConcept C19165224 @default.
- W2792691864 hasConcept C22679943 @default.
- W2792691864 hasConcept C2779343474 @default.
- W2792691864 hasConcept C2989005 @default.
- W2792691864 hasConcept C33923547 @default.
- W2792691864 hasConcept C41008148 @default.
- W2792691864 hasConcept C45664433 @default.
- W2792691864 hasConcept C58489278 @default.
- W2792691864 hasConcept C71924100 @default.
- W2792691864 hasConcept C74193536 @default.
- W2792691864 hasConcept C86803240 @default.
- W2792691864 hasConceptScore W2792691864C105795698 @default.
- W2792691864 hasConceptScore W2792691864C108583219 @default.
- W2792691864 hasConceptScore W2792691864C114614502 @default.
- W2792691864 hasConceptScore W2792691864C126838900 @default.
- W2792691864 hasConceptScore W2792691864C136886441 @default.
- W2792691864 hasConceptScore W2792691864C143409427 @default.
- W2792691864 hasConceptScore W2792691864C144024400 @default.
- W2792691864 hasConceptScore W2792691864C151730666 @default.
- W2792691864 hasConceptScore W2792691864C153180895 @default.
- W2792691864 hasConceptScore W2792691864C154945302 @default.
- W2792691864 hasConceptScore W2792691864C19165224 @default.
- W2792691864 hasConceptScore W2792691864C22679943 @default.
- W2792691864 hasConceptScore W2792691864C2779343474 @default.
- W2792691864 hasConceptScore W2792691864C2989005 @default.
- W2792691864 hasConceptScore W2792691864C33923547 @default.
- W2792691864 hasConceptScore W2792691864C41008148 @default.
- W2792691864 hasConceptScore W2792691864C45664433 @default.
- W2792691864 hasConceptScore W2792691864C58489278 @default.
- W2792691864 hasConceptScore W2792691864C71924100 @default.
- W2792691864 hasConceptScore W2792691864C74193536 @default.
- W2792691864 hasConceptScore W2792691864C86803240 @default.
- W2792691864 hasLocation W27926918641 @default.
- W2792691864 hasOpenAccess W2792691864 @default.
- W2792691864 hasPrimaryLocation W27926918641 @default.
- W2792691864 hasRelatedWork W2040994895 @default.
- W2792691864 hasRelatedWork W2488470449 @default.
- W2792691864 hasRelatedWork W2496058887 @default.
- W2792691864 hasRelatedWork W2789563975 @default.
- W2792691864 hasRelatedWork W2907856352 @default.
- W2792691864 hasRelatedWork W2908319771 @default.
- W2792691864 hasRelatedWork W2924878444 @default.
- W2792691864 hasRelatedWork W2949739829 @default.
- W2792691864 hasRelatedWork W2965875357 @default.
- W2792691864 hasRelatedWork W2967815262 @default.
- W2792691864 hasRelatedWork W2969616175 @default.
- W2792691864 hasRelatedWork W3007045034 @default.
- W2792691864 hasRelatedWork W3027359695 @default.
- W2792691864 hasRelatedWork W3041033411 @default.
- W2792691864 hasRelatedWork W3084293538 @default.
- W2792691864 hasRelatedWork W3106791009 @default.
- W2792691864 hasRelatedWork W3120023931 @default.
- W2792691864 hasRelatedWork W3193585445 @default.
- W2792691864 hasRelatedWork W3205167446 @default.
- W2792691864 hasRelatedWork W946570252 @default.
- W2792691864 isParatext "false" @default.
- W2792691864 isRetracted "false" @default.
- W2792691864 magId "2792691864" @default.
- W2792691864 workType "article" @default.