Review



deconvolution model  (Siemens Healthineers)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 86
      Buy from Supplier

    Structured Review

    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/deconvolution+model/pmc12550029-97-10-13
    Average 86 stars, based on 1 article reviews
    deconvolution model - by Bioz Stars, 2026-09
    86/100 stars

    Images

    1) Product Images from "Model based noise correction enhances the accuracy of pancreatic CT perfusion blood flow measurements"

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

    Journal: Scientific Reports

    doi: 10.1038/s41598-025-24482-x

    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.
    Figure Legend 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.

    Techniques Used: Blocking Assay

    Related Articles

    Blocking Assay:

    Article Title: Model based noise correction enhances the accuracy of pancreatic CT perfusion blood flow measurements
    Article Snippet: Step-6 Processing of Digital Images Digital images were processed using a deconvolution model on a CTp workstation (syngo.via Body Perfusion; Siemens Healthineers) to generate BF maps, which were exported in DICOM format for further analysis.



    Similar Products

    deconvolution model  (Chenomx Inc)
    86
    Chenomx Inc deconvolution model
    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/deconvolution+model/pm42167029-205-17-9
    Average 86 stars, based on 1 article reviews
    deconvolution model - by Bioz Stars, 2026-09
    86/100 stars
    		  Buy from Supplier
    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/deconvolution+model/pmc12550029-97-10-13
    Average 86 stars, based on 1 article reviews
    deconvolution model - by Bioz Stars, 2026-09
    86/100 stars
    		  Buy from Supplier
    blood volume using the deconvolution model bv  (Decon Laboratories)
    90
    Decon Laboratories blood volume using the deconvolution model bv
    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.
    Blood Volume Using The Deconvolution Model Bv, supplied by Decon Laboratories, 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/blood+volume+using+the+deconvolution+model+bv/pmc11320886-132-7-14
    Average 90 stars, based on 1 article reviews
    blood volume using the deconvolution model bv - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    spatial transcriptomics deconvolution by topic model (stride)  (Spatial Transcriptomics Inc)
    90
    Spatial Transcriptomics Inc spatial transcriptomics deconvolution by topic model (stride)
    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.
    Spatial Transcriptomics Deconvolution By Topic Model (Stride), supplied by Spatial Transcriptomics Inc, 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/spatial+transcriptomics+deconvolution+by+topic+modeling++stride++method/pm38267084-114-4-0
    Average 90 stars, based on 1 article reviews
    spatial transcriptomics deconvolution by topic model (stride) - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    spatial transcriptomics deconvolution by topic modeling (stride) method  (Spatial Transcriptomics Inc)
    90
    Spatial Transcriptomics Inc spatial transcriptomics deconvolution by topic modeling (stride) method
    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.
    Spatial Transcriptomics Deconvolution By Topic Modeling (Stride) Method, supplied by Spatial Transcriptomics Inc, 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/spatial+transcriptomics+deconvolution+by+topic+modeling++stride++method/pm37919720-115-26-20
    Average 90 stars, based on 1 article reviews
    spatial transcriptomics deconvolution by topic modeling (stride) method - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    simca model with 2 nd derivative data deconvolution on pirouette software  (Infometrix Inc)
    90
    Infometrix Inc simca model with 2 nd derivative data deconvolution on pirouette software
    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.
    Simca Model With 2 Nd Derivative Data Deconvolution On Pirouette Software, supplied by Infometrix Inc, 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/simca+model+with+2+nd+derivative+data+deconvolution+on+pirouette+software/pmc10657999-110-18-21
    Average 90 stars, based on 1 article reviews
    simca model with 2 nd derivative data deconvolution on pirouette software - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    implementation of model-free deconvolution  (MathWorks Inc)
    90
    MathWorks Inc implementation of model-free deconvolution
    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.
    Implementation Of Model Free Deconvolution, supplied by MathWorks Inc, 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/pmc09884306-126-43-39
    Average 90 stars, based on 1 article reviews
    implementation of model-free deconvolution - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    deltavision spectristm model dv4tm deconvolution microscope  (Applied Precision Inc)
    90
    Applied Precision Inc deltavision spectristm model dv4tm deconvolution microscope
    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.
    Deltavision Spectristm Model Dv4tm Deconvolution Microscope, supplied by Applied Precision Inc, 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/deltavision+microscope/us11260089-460-9-15
    Average 90 stars, based on 1 article reviews
    deltavision spectristm model dv4tm deconvolution microscope - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

    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