deconvolution model Search Results


approximate deconvolution models of turbulence: analysis, phenomenology and numerical analysis  (Verlag GmbH)
90
Verlag GmbH approximate deconvolution models of turbulence: analysis, phenomenology and numerical analysis
Approximate Deconvolution Models Of Turbulence: Analysis, Phenomenology And Numerical Analysis, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deconvolution+model/10__1016_slash_j__camwa__2017__09__035-271-3-16?v=Verlag+GmbH
Average 90 stars, based on 1 article reviews
approximate deconvolution models of turbulence: analysis, phenomenology and numerical analysis - by Bioz Stars, 2026-07
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deconvolution model  (Siemens Healthineers)
86
Siemens Healthineers deconvolution model
A flow chart illustrating the complete noise correction process for blood flow (BF) measurements, including evaluation using digital perfusion phantom (DPP) (starting from the first block) and evaluation using a clinical dataset (starting from the second block). For the DPP analysis, each GTBF value is simulated using two independent sets of 576 noise-impacted TACs, resulting in BF1 and BF2 estimates for random error calculation. This process is repeated for 28 GTBF values, totaling 16,128 TACs. For the clinical dataset, patient BF values calculated using the <t>deconvolution</t> model from Mayer’s study were used as input for the noise-impacted BF maps. BFD represents the noise-impacted BF measurements, which need to be corrected. IRF is the impulse response function, AIF is the arterial input function, TAC represents the tissue attenuation curve, and GTBF is the ground-truth blood flow. BFD corr (i) represents the noise-corrected BF measurement for the i th iteration. The random error and model error calculations are also shown in the flow chart. This iterative process for DPP continues until BFD corr aligns with GTBF or until the error between GTBF and corrected measurements is minimized to an acceptable threshold.
Deconvolution Model, supplied by Siemens Healthineers, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deconvolution+model/pmc12550029-97-10-13?v=Siemens+Healthineers
Average 86 stars, based on 1 article reviews
deconvolution model - by Bioz Stars, 2026-07
86/100 stars
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model-free deconvolution algorithm  (Hellige GMBH)
90
Hellige GMBH model-free deconvolution algorithm
A flow chart illustrating the complete noise correction process for blood flow (BF) measurements, including evaluation using digital perfusion phantom (DPP) (starting from the first block) and evaluation using a clinical dataset (starting from the second block). For the DPP analysis, each GTBF value is simulated using two independent sets of 576 noise-impacted TACs, resulting in BF1 and BF2 estimates for random error calculation. This process is repeated for 28 GTBF values, totaling 16,128 TACs. For the clinical dataset, patient BF values calculated using the <t>deconvolution</t> model from Mayer’s study were used as input for the noise-impacted BF maps. BFD represents the noise-impacted BF measurements, which need to be corrected. IRF is the impulse response function, AIF is the arterial input function, TAC represents the tissue attenuation curve, and GTBF is the ground-truth blood flow. BFD corr (i) represents the noise-corrected BF measurement for the i th iteration. The random error and model error calculations are also shown in the flow chart. This iterative process for DPP continues until BFD corr aligns with GTBF or until the error between GTBF and corrected measurements is minimized to an acceptable threshold.
Model Free Deconvolution Algorithm, supplied by Hellige GMBH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deconvolution+model/pm12033585-427-31-17?v=Hellige+GMBH
Average 90 stars, based on 1 article reviews
model-free deconvolution algorithm - by Bioz Stars, 2026-07
90/100 stars
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maximum-likelihood deconvolution: a journey into model- based signal processing  (Verlag GmbH)
90
Verlag GmbH maximum-likelihood deconvolution: a journey into model- based signal processing
A flow chart illustrating the complete noise correction process for blood flow (BF) measurements, including evaluation using digital perfusion phantom (DPP) (starting from the first block) and evaluation using a clinical dataset (starting from the second block). For the DPP analysis, each GTBF value is simulated using two independent sets of 576 noise-impacted TACs, resulting in BF1 and BF2 estimates for random error calculation. This process is repeated for 28 GTBF values, totaling 16,128 TACs. For the clinical dataset, patient BF values calculated using the <t>deconvolution</t> model from Mayer’s study were used as input for the noise-impacted BF maps. BFD represents the noise-impacted BF measurements, which need to be corrected. IRF is the impulse response function, AIF is the arterial input function, TAC represents the tissue attenuation curve, and GTBF is the ground-truth blood flow. BFD corr (i) represents the noise-corrected BF measurement for the i th iteration. The random error and model error calculations are also shown in the flow chart. This iterative process for DPP continues until BFD corr aligns with GTBF or until the error between GTBF and corrected measurements is minimized to an acceptable threshold.
Maximum Likelihood Deconvolution: A Journey Into Model Based Signal Processing, supplied by Verlag GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deconvolution+model/pm17672707-306-1-12?v=Verlag+GmbH
Average 90 stars, based on 1 article reviews
maximum-likelihood deconvolution: a journey into model- based signal processing - by Bioz Stars, 2026-07
90/100 stars
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deconvolution model  (Chenomx Inc)
86
Chenomx Inc deconvolution model
A flow chart illustrating the complete noise correction process for blood flow (BF) measurements, including evaluation using digital perfusion phantom (DPP) (starting from the first block) and evaluation using a clinical dataset (starting from the second block). For the DPP analysis, each GTBF value is simulated using two independent sets of 576 noise-impacted TACs, resulting in BF1 and BF2 estimates for random error calculation. This process is repeated for 28 GTBF values, totaling 16,128 TACs. For the clinical dataset, patient BF values calculated using the <t>deconvolution</t> model from Mayer’s study were used as input for the noise-impacted BF maps. BFD represents the noise-impacted BF measurements, which need to be corrected. IRF is the impulse response function, AIF is the arterial input function, TAC represents the tissue attenuation curve, and GTBF is the ground-truth blood flow. BFD corr (i) represents the noise-corrected BF measurement for the i th iteration. The random error and model error calculations are also shown in the flow chart. This iterative process for DPP continues until BFD corr aligns with GTBF or until the error between GTBF and corrected measurements is minimized to an acceptable threshold.
Deconvolution Model, supplied by Chenomx Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deconvolution+model/pm42167029-205-17-9?v=Chenomx+Inc
Average 86 stars, based on 1 article reviews
deconvolution model - by Bioz Stars, 2026-07
86/100 stars
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Image Search Results


A flow chart illustrating the complete noise correction process for blood flow (BF) measurements, including evaluation using digital perfusion phantom (DPP) (starting from the first block) and evaluation using a clinical dataset (starting from the second block). For the DPP analysis, each GTBF value is simulated using two independent sets of 576 noise-impacted TACs, resulting in BF1 and BF2 estimates for random error calculation. This process is repeated for 28 GTBF values, totaling 16,128 TACs. For the clinical dataset, patient BF values calculated using the deconvolution model from Mayer’s study were used as input for the noise-impacted BF maps. BFD represents the noise-impacted BF measurements, which need to be corrected. IRF is the impulse response function, AIF is the arterial input function, TAC represents the tissue attenuation curve, and GTBF is the ground-truth blood flow. BFD corr (i) represents the noise-corrected BF measurement for the i th iteration. The random error and model error calculations are also shown in the flow chart. This iterative process for DPP continues until BFD corr aligns with GTBF or until the error between GTBF and corrected measurements is minimized to an acceptable threshold.

Journal: Scientific Reports

Article Title: Model based noise correction enhances the accuracy of pancreatic CT perfusion blood flow measurements

doi: 10.1038/s41598-025-24482-x

Figure Lengend Snippet: A flow chart illustrating the complete noise correction process for blood flow (BF) measurements, including evaluation using digital perfusion phantom (DPP) (starting from the first block) and evaluation using a clinical dataset (starting from the second block). For the DPP analysis, each GTBF value is simulated using two independent sets of 576 noise-impacted TACs, resulting in BF1 and BF2 estimates for random error calculation. This process is repeated for 28 GTBF values, totaling 16,128 TACs. For the clinical dataset, patient BF values calculated using the deconvolution model from Mayer’s study were used as input for the noise-impacted BF maps. BFD represents the noise-impacted BF measurements, which need to be corrected. IRF is the impulse response function, AIF is the arterial input function, TAC represents the tissue attenuation curve, and GTBF is the ground-truth blood flow. BFD corr (i) represents the noise-corrected BF measurement for the i th iteration. The random error and model error calculations are also shown in the flow chart. This iterative process for DPP continues until BFD corr aligns with GTBF or until the error between GTBF and corrected measurements is minimized to an acceptable threshold.

Article Snippet: All evaluations in this study were performed using a commercial deconvolution model (syngo.via, Siemens Healthineers) with fixed reconstruction parameters such as slice thickness, reconstruction kernel, and matrix size selected to reflect standard clinical CTp practice.

Techniques: Blocking Assay