Regularized Field Map Estimation in MRI

Funai, Amanda K.; Fessler, Jeffrey A.; Yeo, Desmond; Olafsson, Valur; Noll, Douglas C.

doi:10.1109/tmi.2008.923956

Cited by 104 publications

(145 citation statements)

References 40 publications

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“…Note that additional regularization terms could be easily appended to Eq. ͑3͒ to incorporate other a priori information about or c. One notable example would be a quadratic roughness penalty 22 to penalize rapid variations in , which would reflect one's expectation that the heated voxels will be largely contiguous with each other, and that the phase change map will not contain sharp edges in most treatment scenarios. This penalty may also improve the accuracy of estimates in regions of low image magnitude, or where temperature-induced changes in T 1 and T 2 reduce image magnitude, and hence degrade phase SNR inside a hot spot.…”

Section: Discussionmentioning

confidence: 99%

“…͑A1͔͒. Optimization transfer 22 is used to ensure that the maximum likelihood term in Eq. ͑3͒ is decreased monotonically with respect to and c. The ᐉ 0 penalty is implemented using an iteratively reweighted least-squares strategy.…”

Section: ͑3͒mentioning

confidence: 99%

“…Prior to estimation, in order to reduce variations in the optimal choice of , we normalize the treatment image y and the baseline library images ͕x b ͖ b=1 N b by the mean across baseline library images of the median image magnitudes. 22 The hybrid model and numerical methods can be easily extended to multicoil image reception, by jointly estimating baseline coefficients w across coils, independent smooth phase shift coefficients c for each coil, and a common heat phase shift . The cost function in this scenario is given by…”

Section: ͑3͒mentioning

confidence: 99%

“…Following an analogous derivation to Ref. 22 leads to the following iteration that is guaranteed to minimize Eq. ͑3͒ ͑after the substitution ͓Eq.…”

Section: ͑A1͒mentioning

confidence: 99%

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Hybrid referenceless and multibaseline subtraction MR thermometry for monitoring thermal therapies in moving organs

et al. 2010

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Purpose: Magnetic resonance thermometry using the proton resonance frequency ͑PRF͒ shift is a promising technique for guiding thermal ablation. For temperature monitoring in moving organs, such as the liver and the heart, problems with motion must be addressed. Multi-baseline subtraction techniques have been proposed, which use a library of baseline images covering the respiratory and cardiac cycle. However, main field shifts due to lung and diaphragm motion can cause large inaccuracies in multi-baseline subtraction. Referenceless thermometry methods based on polynomial phase regression are immune to motion and susceptibility shifts. While referenceless methods can accurately estimate temperature within the organ, in general, the background phase at organ/ tissue interfaces requires large polynomial orders to fit, leading to increased danger that the heated region itself will be fitted by the polynomial and thermal dose will be underestimated. In this paper, a hybrid method for PRF thermometry in moving organs is presented that combines the strengths of referenceless and multi-baseline thermometry. Methods: The hybrid image model assumes that three sources contribute to image phase during thermal treatment: Background anatomical phase, spatially smooth phase deviations, and focal, heat-induced phase shifts. The new model and temperature estimation algorithm were tested in the heart and liver of normal volunteers, in a moving phantom HIFU heating experiment, and in numerical simulations of thermal ablation. The results were compared to multi-baseline and referenceless methods alone. Results: The hybrid method allows for in vivo temperature estimation in the liver and the heart with lower temperature uncertainty compared to multi-baseline and referenceless methods. The moving phantom HIFU experiment showed that the method accurately estimates temperature during motion in the presence of smooth main field shifts. Numerical simulations illustrated the method's sensitivity to algorithm parameters and hot spot features. Conclusions: This new hybrid method for MR thermometry in moving organs combines the strengths of both multi-baseline subtraction and referenceless thermometry and overcomes their fundamental weaknesses.

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Section: Discussionmentioning

confidence: 99%

Section: ͑3͒mentioning

confidence: 99%

Section: ͑3͒mentioning

confidence: 99%

“…Following an analogous derivation to Ref. 22 leads to the following iteration that is guaranteed to minimize Eq. ͑3͒ ͑after the substitution ͓Eq.…”

Section: ͑A1͒mentioning

confidence: 99%

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Hybrid referenceless and multibaseline subtraction MR thermometry for monitoring thermal therapies in moving organs

et al. 2010

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“…They can be interpreted in a straightforward manner by people working in this interdisciplinary field with different background, such as engineers, MRI experts and medical doctors. There are more specialized tests available, such as acquisition and analysis of B 0 and B 1 field maps [53][54][55][56]. In case that suspicious spatial distortion is observed in visual inspection or image subtraction in the proposed test procedures, gradient linearity tests using special phantoms are suggested in [57].…”

Section: Device Functioningmentioning

confidence: 99%

Mutual interferences and design principles for mechatronic devices in magnetic resonance imaging

Gassert

Riener

2010

Int J CARS

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Viscoelasticity of subcortical gray matter structures

Johnson

Schwarb

McGarry

et al. 2016

Human Brain Mapping

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Viscoelastic mechanical properties of the brain assessed with magnetic resonance elastography (MRE) are sensitive measures of microstructural tissue health in neurodegenerative conditions. Recent efforts have targeted measurements localized to specific neuroanatomical regions differentially affected in disease. In this work, we present a method for measuring the viscoelasticity in subcortical gray matter (SGM) structures, including the amygdala, hippocampus, caudate, putamen, pallidum, and thalamus. The method is based on incorporating high spatial resolution MRE imaging (1.6 mm isotropic voxels) with a mechanical inversion scheme designed to improve local measures in pre-defined regions (soft prior regularization; SPR). We find that in twenty-one healthy, young volunteers SGM structures differ from each other in viscoelasticity, quantified as the shear stiffness and damping ratio, but also differ from the global viscoelasticity of the cerebrum. Through repeated examinations on a single volunteer, we estimate the uncertainty to be between 3–7% for each SGM measure. Further, we demonstrate that the use of specific methodological considerations – higher spatial resolution and SPR – both decrease uncertainty and increase sensitivity of the SGM measures. The proposed method allows for reliable MRE measures of SGM viscoelasticity for future studies of neurodegenerative conditions.

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Regularized Field Map Estimation in MRI

Cited by 104 publications

References 40 publications

Hybrid referenceless and multibaseline subtraction MR thermometry for monitoring thermal therapies in moving organs

Hybrid referenceless and multibaseline subtraction MR thermometry for monitoring thermal therapies in moving organs

Mutual interferences and design principles for mechatronic devices in magnetic resonance imaging

Viscoelasticity of subcortical gray matter structures

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