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A review of 3D first-pass, whole-heart, myocardial perfusion cardiovascular magnetic resonance.

Fair MJ, Gatehouse PD, DiBella EV, Firmin DN - J Cardiovasc Magn Reson (2015)

Bottom Line: The mechanisms include rapid sequences, non-Cartesian k-space trajectories, reduced k-space acquisitions, parallel imaging reconstructions and compressed sensing.An attempt is made to explain, rather than simply state, the varying methods with the hope that it will give an appreciation of the different components making up a 3D FPP protocol.Basic estimates demonstrating the required total acceleration factors in typical 3D FPP cases are included, providing context for the extent that each acceleration method can contribute to the required imaging speed, as well as potential limitations in present 3D FPP literature.

View Article: PubMed Central - PubMed

Affiliation: National Heart & Lung Institute, Imperial College London, London, UK. M.Fair@rbht.nhs.uk.

ABSTRACT
A comprehensive review is undertaken of the methods available for 3D whole-heart first-pass perfusion (FPP) and their application to date, with particular focus on possible acceleration techniques. Following a summary of the parameters typically desired of 3D FPP methods, the review explains the mechanisms of key acceleration techniques and their potential use in FPP for attaining 3D acquisitions. The mechanisms include rapid sequences, non-Cartesian k-space trajectories, reduced k-space acquisitions, parallel imaging reconstructions and compressed sensing. An attempt is made to explain, rather than simply state, the varying methods with the hope that it will give an appreciation of the different components making up a 3D FPP protocol. Basic estimates demonstrating the required total acceleration factors in typical 3D FPP cases are included, providing context for the extent that each acceleration method can contribute to the required imaging speed, as well as potential limitations in present 3D FPP literature. Although many 3D FPP methods are too early in development for the type of clinical trials required to show any clear benefit over current 2D FPP methods, the review includes the small but growing quantity of clinical research work already using 3D FPP, alongside the more technical work. Broader challenges concerning FPP such as quantitative analysis are not covered, but challenges with particular impact on 3D FPP methods, particularly with regards to motion effects, are discussed along with anticipated future work in the field.

No MeSH data available.


Related in: MedlinePlus

Breath-held and free-breathing sparsity in different domains. The image (a), x-t (b), x-f (c), and x-KLT (d) domains of simulated breath-held (top) and free-breathing (bottom) datasets. The x-f domain (c) is seen to be far more sparse when the patient is breath-holding than when the patient is allowed to breathe. (d) shows the potential for alternate domains to increase sparsity, with improvements in both cases, but of particular importance in the case of free-breathing. KLT stands for Karhunen-Loève transform and is not discussed further - more information can be found in the references in the text. Reproduced from [100]
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Fig8: Breath-held and free-breathing sparsity in different domains. The image (a), x-t (b), x-f (c), and x-KLT (d) domains of simulated breath-held (top) and free-breathing (bottom) datasets. The x-f domain (c) is seen to be far more sparse when the patient is breath-holding than when the patient is allowed to breathe. (d) shows the potential for alternate domains to increase sparsity, with improvements in both cases, but of particular importance in the case of free-breathing. KLT stands for Karhunen-Loève transform and is not discussed further - more information can be found in the references in the text. Reproduced from [100]

Mentions: With potentially high CS acceleration factors under the ideal conditions of good breath-holding and ECG-triggering, work has gone into modifying the standard CS processes to correct for respiratory motion. A technique utilising the Sparsity and Low-Rank properties of the dynamic datasets termed k-t SLR [100] has shown promise in 2D free-breathing FPP in comparison to other CS reconstructions [101], using a transform that provides greater sparsity even in free breathing (Fig. 8). Usman et al. [102] presented free-breathing 2D FPP with more direct motion compensation, improving on methods that adjust for affine deformations (e.g. [99]), integrating a general motion correction technique directly into the CS algorithm. Block LOw-rank Sparsity with Motion-guidance (BLOSM) [103] is another method for motion correction in CS, designed specifically for FPP, combining similar properties of the above methods, dividing the image into regions that can be tracked over time. This was compared with the previously mentioned CS algorithms in 2D FPP under prominent respiratory motion, as well as recent preliminary work in quantitative 3D FPP [104].Fig. 8


A review of 3D first-pass, whole-heart, myocardial perfusion cardiovascular magnetic resonance.

Fair MJ, Gatehouse PD, DiBella EV, Firmin DN - J Cardiovasc Magn Reson (2015)

Breath-held and free-breathing sparsity in different domains. The image (a), x-t (b), x-f (c), and x-KLT (d) domains of simulated breath-held (top) and free-breathing (bottom) datasets. The x-f domain (c) is seen to be far more sparse when the patient is breath-holding than when the patient is allowed to breathe. (d) shows the potential for alternate domains to increase sparsity, with improvements in both cases, but of particular importance in the case of free-breathing. KLT stands for Karhunen-Loève transform and is not discussed further - more information can be found in the references in the text. Reproduced from [100]
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4522116&req=5

Fig8: Breath-held and free-breathing sparsity in different domains. The image (a), x-t (b), x-f (c), and x-KLT (d) domains of simulated breath-held (top) and free-breathing (bottom) datasets. The x-f domain (c) is seen to be far more sparse when the patient is breath-holding than when the patient is allowed to breathe. (d) shows the potential for alternate domains to increase sparsity, with improvements in both cases, but of particular importance in the case of free-breathing. KLT stands for Karhunen-Loève transform and is not discussed further - more information can be found in the references in the text. Reproduced from [100]
Mentions: With potentially high CS acceleration factors under the ideal conditions of good breath-holding and ECG-triggering, work has gone into modifying the standard CS processes to correct for respiratory motion. A technique utilising the Sparsity and Low-Rank properties of the dynamic datasets termed k-t SLR [100] has shown promise in 2D free-breathing FPP in comparison to other CS reconstructions [101], using a transform that provides greater sparsity even in free breathing (Fig. 8). Usman et al. [102] presented free-breathing 2D FPP with more direct motion compensation, improving on methods that adjust for affine deformations (e.g. [99]), integrating a general motion correction technique directly into the CS algorithm. Block LOw-rank Sparsity with Motion-guidance (BLOSM) [103] is another method for motion correction in CS, designed specifically for FPP, combining similar properties of the above methods, dividing the image into regions that can be tracked over time. This was compared with the previously mentioned CS algorithms in 2D FPP under prominent respiratory motion, as well as recent preliminary work in quantitative 3D FPP [104].Fig. 8

Bottom Line: The mechanisms include rapid sequences, non-Cartesian k-space trajectories, reduced k-space acquisitions, parallel imaging reconstructions and compressed sensing.An attempt is made to explain, rather than simply state, the varying methods with the hope that it will give an appreciation of the different components making up a 3D FPP protocol.Basic estimates demonstrating the required total acceleration factors in typical 3D FPP cases are included, providing context for the extent that each acceleration method can contribute to the required imaging speed, as well as potential limitations in present 3D FPP literature.

View Article: PubMed Central - PubMed

Affiliation: National Heart & Lung Institute, Imperial College London, London, UK. M.Fair@rbht.nhs.uk.

ABSTRACT
A comprehensive review is undertaken of the methods available for 3D whole-heart first-pass perfusion (FPP) and their application to date, with particular focus on possible acceleration techniques. Following a summary of the parameters typically desired of 3D FPP methods, the review explains the mechanisms of key acceleration techniques and their potential use in FPP for attaining 3D acquisitions. The mechanisms include rapid sequences, non-Cartesian k-space trajectories, reduced k-space acquisitions, parallel imaging reconstructions and compressed sensing. An attempt is made to explain, rather than simply state, the varying methods with the hope that it will give an appreciation of the different components making up a 3D FPP protocol. Basic estimates demonstrating the required total acceleration factors in typical 3D FPP cases are included, providing context for the extent that each acceleration method can contribute to the required imaging speed, as well as potential limitations in present 3D FPP literature. Although many 3D FPP methods are too early in development for the type of clinical trials required to show any clear benefit over current 2D FPP methods, the review includes the small but growing quantity of clinical research work already using 3D FPP, alongside the more technical work. Broader challenges concerning FPP such as quantitative analysis are not covered, but challenges with particular impact on 3D FPP methods, particularly with regards to motion effects, are discussed along with anticipated future work in the field.

No MeSH data available.


Related in: MedlinePlus