Dataset Information

Resolving phase ambiguity in dual-echo dixon imaging using a projected power method.

ABSTRACT:

Purpose

To develop a fast and robust method to resolve phase ambiguity in dual-echo Dixon imaging.

Methods

A major challenge in dual-echo Dixon imaging is to estimate the phase error resulting from field inhomogeneity. In this work, a binary quadratic optimization program was formulated to resolve the phase ambiguity. A projected power method was developed to efficiently solve the optimization problem. Both the 1-peak fat model and 6-peak fat model were applied to three-dimensional (3D) datasets. Additionally, the proposed method was extended to dynamic magnetic resonance imaging (MRI) applications using the 6-peak fat model. With institutional review board (IRB) approval and patient consent/assent, the proposed method was evaluated and compared with region growing on 29 consecutive 3D high-resolution patient datasets.

Results

Fast and robust water/fat separation was achieved by the proposed method in different representative 3D datasets and dynamic 3D datasets. Superior water/fat separation was achieved using the 6-peak fat model compared with the 1-peak fat model. Compared to region growing, the proposed method reduced water/fat swaps from 76 to 7% of the patient cohort.

Conclusion

SUBMITTER: Zhang T

PROVIDER: S-EPMC5123983 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Publications

Resolving phase ambiguity in dual-echo dixon imaging using a projected power method.

Zhang Tao T Chen Yuxin Y Bao Shanshan S Alley Marcus T MT Pauly John M JM Hargreaves Brian A BA Vasanawala Shreyas S SS

Magnetic resonance in medicine 20160525 5

<h4>Purpose</h4>To develop a fast and robust method to resolve phase ambiguity in dual-echo Dixon imaging.<h4>Methods</h4>A major challenge in dual-echo Dixon imaging is to estimate the phase error resulting from field inhomogeneity. In this work, a binary quadratic optimization program was formulated to resolve the phase ambiguity. A projected power method was developed to efficiently solve the optimization problem. Both the 1-peak fat model and 6-peak fat model were applied to three-dimensiona ...[more]

PMID: 27221766

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Project description:PurposePET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging.MethodsThirty-five subjects that underwent both 11C-PiB and 18F-MK6240 scans were included in this study. The proposed method was compared with Dixon-based atlas method as well as magnetization-prepared rapid acquisition with gradient echo (MPRAGE)- or Dixon-based deep learning methods. The Dice coefficient and validation loss of the generated pseudo-CT images were used for comparison. PET error images regarding standardized uptake value ratio (SUVR) were quantified through regional and surface analysis to evaluate the final AC accuracy.ResultsThe Dice coefficients of the deep learning methods based on MPRAGE, Dixon, and mUTE images were 0.84 (0.91), 0.84 (0.92), and 0.87 (0.94) for the whole-brain (above-eye) bone regions, respectively, higher than the atlas method of 0.52 (0.64). The regional SUVR error for the atlas method was around 6%, higher than the regional SUV error. The regional SUV and SUVR errors for all deep learning methods were below 2%, with mUTE-based deep learning method performing the best. As for the surface analysis, the atlas method showed the largest error (> 10%) near vertices inside superior frontal, lateral occipital, superior parietal, and inferior temporal cortices. The mUTE-based deep learning method resulted in the least number of regions with error higher than 1%, with the largest error (> 5%) showing up near the inferior temporal and medial orbitofrontal cortices.ConclusionDeep learning with mUTE can generate accurate AC for amyloid and tau imaging in PET/MR.

Dataset Information

Resolving phase ambiguity in dual-echo dixon imaging using a projected power method.

Purpose

Methods

Results

Conclusion

Publications

Resolving phase ambiguity in dual-echo dixon imaging using a projected power method.

Similar Datasets

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