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ABSTRACT: In MRI, the suppression of fat signal is very important for many applications. Multipoint Dixon based water-fat separation methods are commonly used due to its robustness to B(0) homogeneity compared with other fat suppression methods, such as spectral fat saturation. The traditional Cartesian k-space trajectory based multipoint Dixon technique is sensitive to motion, such as pulsatile blood flow, resulting in artifacts that compromise image quality. This work presents a three-point Dixon water-fat separation method using undersampled BLADE (aka PROPELLER) for motion robustness and speed. A regularized iterative reconstruction method is then proposed for reducing the streaking artifacts coming from undersampling. In this study, the performance of the regularized iterative reconstruction method is first tested by simulations and on MR phantoms. The performance of the proposed technique is then evaluated in vivo by comparing it with conventional fat suppression methods on the human brain and knee. Experiments show that the presented method delivers reliable water-fat separation results. The reconstruction method suppresses streaking artifacts typical for undersampled BLADE acquisition schemes without missing fine structures in the image.
Magnetic Resonance in Medicine 02/2011; 65(5):1314-25. · 2.96 Impact Factor
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ABSTRACT: A model-based proton resonance frequency shift (PRFS) thermometry method was developed to significantly reduce the temperature quantification errors encountered in the conventional phase mapping method and the spatiotemporal limitations of the spectroscopic thermometry method. Spectral data acquired using multi-echo gradient echo (GRE) is fit into a two-component signal model containing temperature information and fat is used as the internal reference. The noniterative extended Prony algorithm is used for the signal fitting and frequency estimate. Monte Carlo simulations demonstrate the advantages of the method for optimal water-fat separation and temperature estimation accuracy. Phantom experiments demonstrate that the model-based method effectively reduces the interscan motion effects and frequency disturbances due to the main field drift. The thermometry result of ex vivo goose liver experiment with high intensity focused ultrasound (HIFU) heating was also presented in the paper to indicate the feasibility of the model-based method in real tissue.
Magnetic Resonance Imaging 04/2010; 28(3):418-26. · 1.99 Impact Factor
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ABSTRACT: The conventional phase difference method for MR thermometry suffers from disturbances caused by the presence of lipid protons, motion-induced error, and field drift. A signal model is presented with multi-echo gradient echo (GRE) sequence using a fat signal as an internal reference to overcome these problems. The internal reference signal model is fit to the water and fat signals by the extended Prony algorithm and the Levenberg-Marquardt algorithm to estimate the chemical shifts between water and fat which contain temperature information. A noise analysis of the signal model was conducted using the Cramer-Rao lower bound to evaluate the noise performance of various algorithms, the effects of imaging parameters, and the influence of the water:fat signal ratio in a sample on the temperature estimate. Comparison of the calculated temperature map and thermocouple temperature measurements shows that the maximum temperature estimation error is 0.614 degrees C, with a standard deviation of 0.06 degrees C, confirming the feasibility of this model-based temperature mapping method. The influence of sample water:fat signal ratio on the accuracy of the temperature estimate is evaluated in a water-fat mixed phantom experiment with an optimal ratio of approximately 0.66:1.
Magnetic Resonance in Medicine 09/2009; 62(5):1251-60. · 2.96 Impact Factor
