◾ Regularized Phase Estimation Methods in Interferometry

High-resolution Magnetic Resonance Imaging (MRI) of humans and animals in vivo is routine and non-invasive. Identifying and quantifying chemical composition of tissue from acquired images is a challenge. MR spectroscopy (MRS) may be used to identify chemical components accurately over a finite volume in the tissue. However, the temporal and spatial resolutions are limited. Multi-spectral MRI exploits the multiple modes of MR such as T1, T2 and proton density maps and classifies voxels into different tissue types, but the chemical identity of the tissue remains unknown. Many fat suppression methods were developed because the unwanted fat signal often compromises image interpretability in clinical MRI, but these techniques are sensitive to MR field inhomogeneity. Multi-point Dixon methods separate MR images into water and fat images and are less sensitive to field inhomogeneity [1] and IDEAL-MRI (iterative decomposition of water and fat with echo asymmetry and least-squares estimation) improved upon the Dixon methods by avoiding the problem of phase unwrapping [2]. However, special care has to be taken when estimating the field map to avoid erroneous solutions to the least-squares estimation problem which lead to artifacts such as swapping of water and fat. The use of region growing schemes (with a reliable seed) mitigates this problem as demonstrated in previous studies [3][4]. However, the seed is not always reliable and growing schemes can be sensitive to phase discontinuities. Moreover, although the technology was successfully demonstrated on many clinical scanners, only limited applications were found in preclinical scanners with high MR field where the field inhomogeneity can be far worse [5]. We developed a robust and accurate algorithm to compute water and fat content on an 11.7T small animal scanner by improving upon existing phase estimation methods through multiple starting pixels and consensus-based region growing. The method, after further validation, has the potential of providing a translatable assay to study disease progression and regression related to fat and water contents in various animal models, such as studying atherosclerotic plaque composition.

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New phase estimation methods dedicated to high-frequency voltage injections for sensorless PMSM drives

2011 IEEE International Electric Machines & Drives Conference (IEMDC) ◽

10.1109/iemdc.2011.5994769 ◽

2011 ◽

Cited By ~ 1

Author(s):

Shinji Shinnaka

Keyword(s):

High Frequency ◽

Estimation Methods ◽

Phase Estimation ◽

New Phase

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Comparison of Phase Estimation Methods for Quantitative Susceptibility Mapping Using a Rotating-Tube Phantom

Radiology Research and Practice ◽

10.1155/2021/1898461 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Kathryn E. Keenan ◽

Ben P. Berman ◽

Slávka Rýger ◽

Stephen E. Russek ◽

Wen-Tung Wang ◽

...

Keyword(s):

Maximum Likelihood ◽

Data Acquisition ◽

Relative Error ◽

Region Growing ◽

Susceptibility Mapping ◽

Cumulative Distribution ◽

Likelihood Method ◽

Estimation Methods ◽

Phase Estimation ◽

Quantitative Susceptibility Mapping

Quantitative Susceptibility Mapping (QSM) is an MRI tool with the potential to reveal pathological changes from magnetic susceptibility measurements. Before phase data can be used to recover susceptibility ( Δ χ ), the QSM process begins with two steps: data acquisition and phase estimation. We assess the performance of these steps, when applied without user intervention, on several variations of a phantom imaging task. We used a rotating-tube phantom with five tubes ranging from Δ χ = 0.05 ppm to Δ χ = 0.336 ppm. MRI data was acquired at nine angles of rotation for four different pulse sequences. The images were processed by 10 phase estimation algorithms including Laplacian, region-growing, branch-cut, temporal unwrapping, and maximum-likelihood methods, resulting in approximately 90 different combinations of data acquisition and phase estimation methods. We analyzed errors between measured and expected phases using the probability mass function and Cumulative Distribution Function. Repeatable acquisition and estimation methods were identified based on the probability of relative phase errors. For single-echo GRE and segmented EPI sequences, a region-growing method was most reliable with Pr (relative error <0.1) = 0.95 and 0.90, respectively. For multiecho sequences, a maximum-likelihood method was most reliable with Pr (relative error <0.1) = 0.97. The most repeatable multiecho methods outperformed the most repeatable single-echo methods. We found a wide range of repeatability and reproducibility for off-the-shelf MRI acquisition and phase estimation approaches, and this variability may prevent the techniques from being widely integrated in clinical workflows. The error was dominated in many cases by spatially discontinuous phase unwrapping errors. Any postprocessing applied on erroneous phase estimates, such as QSM’s background field removal and dipole inversion, would suffer from error propagation. Our paradigm identifies methods that yield consistent and accurate phase estimates that would ultimately yield consistent and accurate Δ χ estimates.

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Validation of Data Acquisition and Phase Estimation for Quantitative Susceptibility Mapping with a Rotating-Tube Phantom

10.1101/2021.06.29.450224 ◽

2021 ◽

Author(s):

Kathryn E Keenan ◽

Benjamin Paul Berman ◽

Slavka Carnicka ◽

Stephen E Russek ◽

Wen-Tung Wang ◽

...

Keyword(s):

Maximum Likelihood ◽

Data Acquisition ◽

Relative Error ◽

Region Growing ◽

Susceptibility Mapping ◽

Cumulative Distribution ◽

Likelihood Method ◽

Estimation Methods ◽

Phase Estimation ◽

Quantitative Susceptibility Mapping

Purpose: Quantitative Susceptibility Mapping (QSM) is an MRI tool with the potential to reveal pathological changes from magnetic susceptibility measurements. Before phase data can be used to recover susceptibility (Δχ), the QSM process begins with two steps: data acquisition and phase estimation. We assess the performance of these steps, when applied without user intervention, on several variations of a phantom imaging task. Approach: We used a rotating-tube phantom with five tubes ranging from Δχ=0.05 ppm to Δχ=0.336 ppm. MRI data was acquired at nine angles of rotation for four different pulse sequences. The images were processed by 10 phase estimation algorithms including Laplacian, region-growing, branch-cut, temporal unwrapping and maximum-likelihood methods. We analyzed errors between measured and expected phase using the probability mass function and Cumulative Distribution Function. Results: Repeatable acquisition and estimation methods were identified based on the probability of relative phase errors. For single-echo GRE and segmented EPI sequences, a region-growing method was most reliable with Pr(relative error<0.1)=0.95 and 0.90 respectively. For multi-echo sequences, a Maximum-Likelihood method was most reliable with Pr(relative error<0.1)=0.97. The most repeatable multi-echo methods outperformed the most repeatable single-echo methods. Conclusions: We found a wide range of repeatability and reproducibility for off-the-shelf MRI acquisition and phase estimation approaches. The error was dominated in many cases by spatially discontinuous phase unwrapping errors. Any post-processing applied on erroneous phase estimates, such as QSM's background field removal and dipole inversion, would suffer from error propagation. Our paradigm identifies methods that yield consistent and accurate phase estimates that would ultimately yield consistent and accurate Δ𝜒 estimates.

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