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Accelerated MR thermometry using the Kalman filter

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Magnetic resonance (MR) imaging plays an important role in monitoring thermal treatment... Therefore, accelerated methods are needed to improve the spatial and temporal resolution in MR thermometry... Multi-channel coils are not widely available for MR-guided FUS systems, so conventional parallel imaging methods cannot be used for acceleration... The Kalman filter (KF) uses prior state information to predict the current state with a dynamic system model: x(k) = x(k-1) + w(k-1) z(k) = U(k) F x(k) + v(k) x(k) is the target image at the kth frame and the first function describes the state transition. z(k) is the corresponding acquired data... F is a Fourier transform operator and U(k) is an undersampling pattern. w and v are the system and measurement noise, assumed to have white Gaussian distributions with covariance matrices estimated by the KF. w models state changes resulting from heating... Figs. 1 and 2 show the simulated spatial and temporal temperature maps... The KF method produced negligible aliasing artifacts in the temperature map (top) and resulted in better approximation of the standard temperature with less error (bottom)... With the first image initialized by view sharing, KF further reduced the error in the first few frames... Fig. 4 shows the temporal profile of the focal spot (3x3 pixels)... The KF method approximated the fully sampled image accurately and provided a temperature map with less error... In conclusion, the KF method can estimate temperature accurately with a speed-up of at least 2X, enabling real-time thermometry with greater spatial coverage.

No MeSH data available.


Experimental accelerated temporal temperature maps. The fully sampled temperature map is shown as reference. The KF method reduced aliasing and error. Initialized by view sharing, KF further reduced the error in the first few frames.
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Figure 3: Experimental accelerated temporal temperature maps. The fully sampled temperature map is shown as reference. The KF method reduced aliasing and error. Initialized by view sharing, KF further reduced the error in the first few frames.

Mentions: Figs. 1 and 2 show the simulated spatial and temporal temperature maps. The KF method produced negligible aliasing artifacts in the temperature map (top) and resulted in better approximation of the standard temperature with less error (bottom). With the first image initialized by view sharing, KF further reduced the error in the first few frames. Fig. 3 shows the experimental temporal temperature maps. Fig. 4 shows the temporal profile of the focal spot (3x3 pixels). The KF method approximated the fully sampled image accurately and provided a temperature map with less error. In conclusion, the KF method can estimate temperature accurately with a speed-up of at least 2X, enabling real-time thermometry with greater spatial coverage.


Accelerated MR thermometry using the Kalman filter
Experimental accelerated temporal temperature maps. The fully sampled temperature map is shown as reference. The KF method reduced aliasing and error. Initialized by view sharing, KF further reduced the error in the first few frames.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4489618&req=5

Figure 3: Experimental accelerated temporal temperature maps. The fully sampled temperature map is shown as reference. The KF method reduced aliasing and error. Initialized by view sharing, KF further reduced the error in the first few frames.
Mentions: Figs. 1 and 2 show the simulated spatial and temporal temperature maps. The KF method produced negligible aliasing artifacts in the temperature map (top) and resulted in better approximation of the standard temperature with less error (bottom). With the first image initialized by view sharing, KF further reduced the error in the first few frames. Fig. 3 shows the experimental temporal temperature maps. Fig. 4 shows the temporal profile of the focal spot (3x3 pixels). The KF method approximated the fully sampled image accurately and provided a temperature map with less error. In conclusion, the KF method can estimate temperature accurately with a speed-up of at least 2X, enabling real-time thermometry with greater spatial coverage.

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

Magnetic resonance (MR) imaging plays an important role in monitoring thermal treatment... Therefore, accelerated methods are needed to improve the spatial and temporal resolution in MR thermometry... Multi-channel coils are not widely available for MR-guided FUS systems, so conventional parallel imaging methods cannot be used for acceleration... The Kalman filter (KF) uses prior state information to predict the current state with a dynamic system model: x(k) = x(k-1) + w(k-1) z(k) = U(k) F x(k) + v(k) x(k) is the target image at the kth frame and the first function describes the state transition. z(k) is the corresponding acquired data... F is a Fourier transform operator and U(k) is an undersampling pattern. w and v are the system and measurement noise, assumed to have white Gaussian distributions with covariance matrices estimated by the KF. w models state changes resulting from heating... Figs. 1 and 2 show the simulated spatial and temporal temperature maps... The KF method produced negligible aliasing artifacts in the temperature map (top) and resulted in better approximation of the standard temperature with less error (bottom)... With the first image initialized by view sharing, KF further reduced the error in the first few frames... Fig. 4 shows the temporal profile of the focal spot (3x3 pixels)... The KF method approximated the fully sampled image accurately and provided a temperature map with less error... In conclusion, the KF method can estimate temperature accurately with a speed-up of at least 2X, enabling real-time thermometry with greater spatial coverage.

No MeSH data available.