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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.


‘Domains’ in FPP. Sets of raw data acquired through time are said to be in k-t space (a). Through a Fourier transform in the spatial dimensions this can be converted to a set of dynamic images (b), which can be examined for a single line of this data through time (c) known as x-t space. A Fourier transform of (c) in the temporal dimension then yields x-f space (d). Reproduced from [80]
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Fig4: ‘Domains’ in FPP. Sets of raw data acquired through time are said to be in k-t space (a). Through a Fourier transform in the spatial dimensions this can be converted to a set of dynamic images (b), which can be examined for a single line of this data through time (c) known as x-t space. A Fourier transform of (c) in the temporal dimension then yields x-f space (d). Reproduced from [80]

Mentions: The feasibility of true 3D whole-heart FPP with good SNR, spatial and temporal resolution improved with the introduction of new PI methods. These take advantage of the similarity of large portions of the images during FPP, and/or the generally gradual changes in image contrast that occur, known technically as using joint spatiotemporal redundancy in dynamically acquired datasets [71]. These techniques are collectively referred to here as k-t PI techniques, due to the temporally (t) varying k-space (k) sampling pattern used in these methods. An extension to the original PI and temporal PI techniques to make simultaneous use of spatial and temporal redundancy, the roots of k-t PI methods can be traced back to the UNFOLD reconstruction algorithm [72]. Redundancy in CMR datasets across time (i.e. across the temporal dimension) can be translated mathematically as a narrower point spread function (PSF) of the series of images when transformed into representation of the different temporal frequencies in the series. This is known as the x-f domain, where x represents all of the spatial dimensions (as with an image in x-y) and f corresponds to frequency, obtained through a Fourier transform across the image in the time series (Fig. 4). This means, with appropriate sampling patterns and small enough acceleration factors, the leakage of PSF energy due to aliasing can be filtered from the true object signal (Fig. 5), which is then Fourier-transformed back to make unaliased images. The process, in effect applying a temporal filter, does not directly cause SNR degradation of gradual changes in image contrast, and therein lies its potential. However, this also ties into a limitation; that more sudden or dynamic real changes in image contrast can lose SNR locally [73], for example if a GBCA bolus remains very compact on arrival in the myocardium.Fig. 4


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)

‘Domains’ in FPP. Sets of raw data acquired through time are said to be in k-t space (a). Through a Fourier transform in the spatial dimensions this can be converted to a set of dynamic images (b), which can be examined for a single line of this data through time (c) known as x-t space. A Fourier transform of (c) in the temporal dimension then yields x-f space (d). Reproduced from [80]
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: ‘Domains’ in FPP. Sets of raw data acquired through time are said to be in k-t space (a). Through a Fourier transform in the spatial dimensions this can be converted to a set of dynamic images (b), which can be examined for a single line of this data through time (c) known as x-t space. A Fourier transform of (c) in the temporal dimension then yields x-f space (d). Reproduced from [80]
Mentions: The feasibility of true 3D whole-heart FPP with good SNR, spatial and temporal resolution improved with the introduction of new PI methods. These take advantage of the similarity of large portions of the images during FPP, and/or the generally gradual changes in image contrast that occur, known technically as using joint spatiotemporal redundancy in dynamically acquired datasets [71]. These techniques are collectively referred to here as k-t PI techniques, due to the temporally (t) varying k-space (k) sampling pattern used in these methods. An extension to the original PI and temporal PI techniques to make simultaneous use of spatial and temporal redundancy, the roots of k-t PI methods can be traced back to the UNFOLD reconstruction algorithm [72]. Redundancy in CMR datasets across time (i.e. across the temporal dimension) can be translated mathematically as a narrower point spread function (PSF) of the series of images when transformed into representation of the different temporal frequencies in the series. This is known as the x-f domain, where x represents all of the spatial dimensions (as with an image in x-y) and f corresponds to frequency, obtained through a Fourier transform across the image in the time series (Fig. 4). This means, with appropriate sampling patterns and small enough acceleration factors, the leakage of PSF energy due to aliasing can be filtered from the true object signal (Fig. 5), which is then Fourier-transformed back to make unaliased images. The process, in effect applying a temporal filter, does not directly cause SNR degradation of gradual changes in image contrast, and therein lies its potential. However, this also ties into a limitation; that more sudden or dynamic real changes in image contrast can lose SNR locally [73], for example if a GBCA bolus remains very compact on arrival in the myocardium.Fig. 4

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.