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Siemens AG medium soft-tissue kernel siemens b30f
Medium Soft Tissue Kernel Siemens B30f, supplied by Siemens AG, 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/soft+tissue+reconstruction+algorithm+(b30f+kernel)/b30f/pmc04985462-73-7-9
Average 90 stars, based on 1 article reviews
medium soft-tissue kernel siemens b30f - by Bioz Stars, 2026-11
90/100 stars

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Article Title: Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma
Article Snippet: CT imaging protocols Hospital CT system Tube voltage a Pixel size Slice thickness Reconstruction kernel Centre A LightSpeed, GE Medical Systems 120 kVp 0.65-0.78 2.5 mm STANDARD LightSpeed Ultra, GE Medical Systems 120 kVp 0.64-0.76 1.25 mm, 2.5 mm STANDARD LightSpeed VCT, GE Medical Systems 120 kVp 0.60-0.85 1.25 mm, 2.5 mm STANDARD Brilliance iCT, Philips 120 kVp 0.68-0.70 1.25 mm B iCT 256, Philips 120 kVp 0.67-0.87 1.25 mm B, C Biograph 16, Siemens 120 kVp 0.65-0.85 1 mm B31f Sensation 16, Siemens 120 kVp 0.66-0.85 1 mm, 1.5 mm, 2 mm B31f Centre B SOMATOM Definition Flash, Siemens 120 kVp 0.52-0.93 1 mm B31f, B70f SOMATOM Definition Force, Siemens 90 kVp, 100 kVp, 110 kVp 0.52-0.71 1 mm Br40d SOMATOM Perspective, Siemens 110 kVp, 130 kVp 0.48-0.82 1 mm B31s SOMATOM Sensation 64, Siemens 120 kVp 0.57-0.80 1 mm B30f a All CT scans were performed with automatic tube current modulation.

Article Title: Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?
Article Snippet: Additionally, due to limited image availability, another limitation is the fact that we were only able to analyze the effect of two reconstruction kernels from a single vendor (Siemens).

Article Title: Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.
Article Snippet: Manufacturer Kernel pair No. of training pairs No. of training slices for the given pair SIEMENS (B30f, B50f) 100 16 343 PHILIPS (C, D) 100 19 359 TOSHIBA (FC10, FC51) 100 19 746

Article Title: A numerical study on airflow and particle transport characteristics of subjects with cement dust exposure
Article Snippet: Exposure to cement dust can cause respiratory problems and structural changes in respiratory airways.. Computed tomography (CT) has shown that the airways of individuals with cement dust exposure (CDE) exhibit narrowing, wall thickening, and altered bifurcation angles.. This study aimed to investigate the differences in airflow structure and particle transport between individuals with CDE and those with non-cement dust exposure (NCDE).

Article Title: MSO-GP: 3-D segmentation of large and complex conjoined tree structures.
Article Snippet: Contents lists available at ScienceDirect Methods journal homepage: www.elsevier.com/locate/ymeth MSO-GP: 3-D segmentation of large and complex conjoined tree structures Arijit De a,1, Nirmal Das b,c,1, Punam K. Saha d, Alejandro Comellas e, Eric Hoffman e, Subhadip Basu b,∗,2, Tapabrata Chakraborti f ,g,∗∗,2 a Department of Electronics & Telecommunication Engineering, Jadavpur University, Kolkata, India b Department of Computer Science and Engineering (AIML), Institute of Engineering and Management, Kolkata, India c Department of Computer Science and Engineering, Jadavpur University, Kolkata, India d Department of Electrical and Computer Engineering & Department of Radiology, University of Iowa, Iowa City, IA 52242, USA e Department of Internal Medicine, University of Iowa, Iowa City, USA f Health Sciences Programme, The Alan Turing Institute, London, UK g Department of Medical Physics and Biomedical Engineering, University College London, London, UK A R T I C L E I N F O A B S T R A C T

Article Title: Human-airway surface mesh smoothing based on graph convolutional neural networks.
Article Snippet: Background and Objective: A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD).. The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect.. Hence, an advanced surface smoothing is necessary.

Article Title: Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.
Article Snippet: We identified all such pairs but only considered the following five representative pairs: Siemens B30f (soft) and B50f (hard); Philips C (soft) and D (hard); Toshiba FC10 (soft) and FC51 (hard); GE STD (soft) and BONE (hard); and GE STD (soft) and LUNG (hard).

Article Title: Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.
Article Snippet: In a Siemens vendor, B50f represents a hard kernel and B30f represents a soft kernel image.



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