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osem 3d algorithm  (Philips Healthcare)

 
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    Philips Healthcare osem 3d algorithm
    Osem 3d Algorithm, supplied by Philips Healthcare, 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/osem3d+algorithm/3d+algorithm+osem/pmc08698938-59-68-59
    Average 86 stars, based on 1 article reviews
    osem 3d algorithm - by Bioz Stars, 2026-10
    86/100 stars

    Images

    Related Articles

    Positron Emission Tomography:

    Article Title: Relevance of Dynamic 18 F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes
    Article Snippet: .. The PET images were obtained from four different imaging systems using locally optimized reconstruction parameters: (I) Biograph 6 True Point PET/CT (Siemens Healthineers ® , Erlangen, Germany) with an OSEM 2D algorithm (2 iterations, 21 subsets, 256 × 256 × 148 voxels of 2.7 × 2.7 × 3.0 mm 3 ) and 4-mm Gaussian post-reconstruction filter, (II) Vereos PET/CT (Philips Healthcare ® , Eindhoven, The Netherlands) with an OSEM 3D algorithm (3 iterations, 15 subsets, 128 × 128 × 82 voxels of 2 × 2 × 2 mm 3 ) and no post-filtering, (III) Biograph mCT Flow PET/CT (Siemens Healthineers ® ) with an OSEM 3D algorithm (8 iterations, 21 subsets, 400 × 400 × 109 voxels of 1.0 × 1.0 × 2.0 mm 3 ) and no post-filtering, (IV) Signa 3T PET/MR (GE Healthcare ® , Chicago, IL, USA) with an OSEM 3D algorithm (8 iterations, 28 subsets, 256 × 256 × 89 voxels of 1.2 × 1.2 × 2.8 mm 3 ) and no post-filtering. ..

    Imaging:

    Article Title: Relevance of Dynamic 18 F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes
    Article Snippet: .. The PET images were obtained from four different imaging systems using locally optimized reconstruction parameters: (I) Biograph 6 True Point PET/CT (Siemens Healthineers ® , Erlangen, Germany) with an OSEM 2D algorithm (2 iterations, 21 subsets, 256 × 256 × 148 voxels of 2.7 × 2.7 × 3.0 mm 3 ) and 4-mm Gaussian post-reconstruction filter, (II) Vereos PET/CT (Philips Healthcare ® , Eindhoven, The Netherlands) with an OSEM 3D algorithm (3 iterations, 15 subsets, 128 × 128 × 82 voxels of 2 × 2 × 2 mm 3 ) and no post-filtering, (III) Biograph mCT Flow PET/CT (Siemens Healthineers ® ) with an OSEM 3D algorithm (8 iterations, 21 subsets, 400 × 400 × 109 voxels of 1.0 × 1.0 × 2.0 mm 3 ) and no post-filtering, (IV) Signa 3T PET/MR (GE Healthcare ® , Chicago, IL, USA) with an OSEM 3D algorithm (8 iterations, 28 subsets, 256 × 256 × 89 voxels of 1.2 × 1.2 × 2.8 mm 3 ) and no post-filtering. ..

    Positron Emission Tomography-Computed Tomography:

    Article Title: Relevance of Dynamic 18 F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes
    Article Snippet: .. The PET images were obtained from four different imaging systems using locally optimized reconstruction parameters: (I) Biograph 6 True Point PET/CT (Siemens Healthineers ® , Erlangen, Germany) with an OSEM 2D algorithm (2 iterations, 21 subsets, 256 × 256 × 148 voxels of 2.7 × 2.7 × 3.0 mm 3 ) and 4-mm Gaussian post-reconstruction filter, (II) Vereos PET/CT (Philips Healthcare ® , Eindhoven, The Netherlands) with an OSEM 3D algorithm (3 iterations, 15 subsets, 128 × 128 × 82 voxels of 2 × 2 × 2 mm 3 ) and no post-filtering, (III) Biograph mCT Flow PET/CT (Siemens Healthineers ® ) with an OSEM 3D algorithm (8 iterations, 21 subsets, 400 × 400 × 109 voxels of 1.0 × 1.0 × 2.0 mm 3 ) and no post-filtering, (IV) Signa 3T PET/MR (GE Healthcare ® , Chicago, IL, USA) with an OSEM 3D algorithm (8 iterations, 28 subsets, 256 × 256 × 89 voxels of 1.2 × 1.2 × 2.8 mm 3 ) and no post-filtering. ..



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    SNRliver ( A ), SUVmean liver ( B ) and SD liver ( C ) versus body weight (kg) for OSEM3D (grey) and BSREM (black) reconstruction algorithms. The trend lines ( A ) were obtained by simple linear regression. The dashed lines represent the 95% confidence of intervals of the slope of regression lines

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    Article Title: Impact of patient size on image quality of OSEM3D and BSREM reconstructions in [ 68 Ga]Ga-DOTA-TATE PET/MR

    doi: 10.1186/s40658-025-00777-w

    Figure Lengend Snippet: SNRliver ( A ), SUVmean liver ( B ) and SD liver ( C ) versus body weight (kg) for OSEM3D (grey) and BSREM (black) reconstruction algorithms. The trend lines ( A ) were obtained by simple linear regression. The dashed lines represent the 95% confidence of intervals of the slope of regression lines

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    Transversal patient PETMR images and SNRliver, SUVmean liver and SD liver of OSEM3D and BSREM reconstruction algorithms using a dosage regimen with a prolonged acquisition time for heavier patients. OSEM3D images show a comparable image quality between patients whereas image quality increases in heavier patients for BSREM

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    doi: 10.1186/s40658-025-00777-w

    Figure Lengend Snippet: Transversal patient PETMR images and SNRliver, SUVmean liver and SD liver of OSEM3D and BSREM reconstruction algorithms using a dosage regimen with a prolonged acquisition time for heavier patients. OSEM3D images show a comparable image quality between patients whereas image quality increases in heavier patients for BSREM

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    Linear fits of SNRliver,norm [(MBq·min) −1/2 ] of the OSEM3D (gray) and BSREM (black) reconstructions versus body weight ( A ), height ( B ), body weight/height ( C ), BMI ( D ), and LBM ( E ). The additional BSREM fixed (blue) fit is presented in fig. A. The dashed lines represent the 95% confidence intervals of the fits

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    Figure Lengend Snippet: Linear fits of SNRliver,norm [(MBq·min) −1/2 ] of the OSEM3D (gray) and BSREM (black) reconstructions versus body weight ( A ), height ( B ), body weight/height ( C ), BMI ( D ), and LBM ( E ). The additional BSREM fixed (blue) fit is presented in fig. A. The dashed lines represent the 95% confidence intervals of the fits

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