Current status in spatiotemporal analysis of contrast‐based perfusion MRI

In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions‐of‐interest as isolated systems supplied by a single global source. This simplificat...

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Vydáno v:Magnetic resonance in medicine Ročník 91; číslo 3; s. 1136 - 1148
Hlavní autoři: Shalom, Eve S., Khan, Amirul, Van Loo, Sven, Sourbron, Steven P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Wiley Subscription Services, Inc 01.03.2024
John Wiley and Sons Inc
Témata:
Computer Processing and Modeling
Contrast Media
DCE‐MRI
DSC‐MRI
Image contrast
Inverse problems
Machine learning
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Nomenclature
Perfusion
Review
Spatio-Temporal Analysis
spatiotemporal modeling
Systematic errors
tracer kinetics
DCE-MRI
perfusion
DSC-MRI
spatiotemporal modeling
tracer kinetics
ISSN:0740-3194, 1522-2594, 1522-2594
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Abstract In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions‐of‐interest as isolated systems supplied by a single global source. This simplification not only leads to long‐recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between‐voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state‐of‐the‐art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.
AbstractList In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions‐of‐interest as isolated systems supplied by a single global source. This simplification not only leads to long‐recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between‐voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state‐of‐the‐art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.
In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.
Author Shalom, Eve S.
Van Loo, Sven
Sourbron, Steven P.
Khan, Amirul
AuthorAffiliation 1 School of Physics and Astronomy University of Leeds Leeds UK
4 Department of Applied Physics Ghent University Ghent Belgium
3 School of Civil Engineering University of Leeds Leeds UK
2 Division of Clinical Medicine University of Sheffield Sheffield UK
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Issue 3
Keywords DCE-MRI
perfusion
DSC-MRI
spatiotemporal modeling
tracer kinetics
Language English
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Snippet In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm...
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SubjectTerms Computer Processing and Modeling
Contrast Media
DCE‐MRI
DSC‐MRI
Image contrast
Inverse problems
Machine learning
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Nomenclature
Perfusion
Review
Spatio-Temporal Analysis
spatiotemporal modeling
Systematic errors
tracer kinetics
Title Current status in spatiotemporal analysis of contrast‐based perfusion MRI
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fmrm.29906
https://www.ncbi.nlm.nih.gov/pubmed/37929645
https://www.proquest.com/docview/2904035871
https://www.proquest.com/docview/2886598923
https://pubmed.ncbi.nlm.nih.gov/PMC10962600
Volume 91
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