iterative bicgstab solver (MathWorks Inc)
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MathWorks Inc
iterative bicgstab solver
Iterative Bicgstab Solver, 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/iterative+bicgstab+solver/pm37285215-298-7-11
Average 90 stars, based on 1 article reviews
Iterative Bicgstab Solver, 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/iterative+bicgstab+solver/pm37285215-298-7-11
Average 90 stars, based on 1 article reviews
iterative bicgstab solver - by Bioz Stars,
2026-10
90/100 stars
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Filtration:Article Title: MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data. Article Snippet: Funding information National Institute on Aging, Grant/Award Numbers: RF1AG070149, RF1AG070149-01S1; National Cancer Institute, Grant/Award Number: U24CA220245; National Institute of Biomedical Imaging and Bioengineering, Grant/Award Numbers: R01EB001838, P41EB028744 Abstract Background: The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers.. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools.These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains.. Purpose: We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. Article Title: MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data Article Snippet: Despite additional overhead associated with the iterative BiCGSTAB solver implemented in Modification:Article Title: MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data. Article Snippet: Funding information National Institute on Aging, Grant/Award Numbers: RF1AG070149, RF1AG070149-01S1; National Cancer Institute, Grant/Award Number: U24CA220245; National Institute of Biomedical Imaging and Bioengineering, Grant/Award Numbers: R01EB001838, P41EB028744 Abstract Background: The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers.. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools.These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains.. Purpose: We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. Article Title: MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data Article Snippet: Despite additional overhead associated with the iterative BiCGSTAB solver implemented in Selection:Article Title: MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data. Article Snippet: Funding information National Institute on Aging, Grant/Award Numbers: RF1AG070149, RF1AG070149-01S1; National Cancer Institute, Grant/Award Number: U24CA220245; National Institute of Biomedical Imaging and Bioengineering, Grant/Award Numbers: R01EB001838, P41EB028744 Abstract Background: The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers.. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools.These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains.. Purpose: We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. Article Title: MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data Article Snippet: Despite additional overhead associated with the iterative BiCGSTAB solver implemented in |