Magnetic Resonance Fingerprinting Exams With Optimized Sound
US-2015070012-A1 · Mar 12, 2015 · US
US2016349341A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2016349341-A1 |
| Application number | US-201515116999-A |
| Country | US |
| Kind code | A1 |
| Filing date | Jan 20, 2015 |
| Priority date | Feb 11, 2014 |
| Publication date | Dec 1, 2016 |
| Grant date | — |
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Systems and methods for estimating quantitative parameters of a subject from data acquired using a magnetic resonance imaging (MRI) system. MR data acquired with an MRI system is provided, which represents a plurality of different signal evolutions acquired using different acquisition parameter settings. An initial dictionary comprising a plurality of signal templates is generated that coarsely sample acquisition parameters used when acquiring the provided MR data. The MR data is compared with the initial dictionary. The quantitative parameters associated with an entry in the initial dictionary are stored as the estimated quantitative parameters when the comparison satisfies a threshold criterion and the initial dictionary is updated when the comparison does not satisfy the threshold criterion.
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1 . A method for estimating quantitative parameters of a subject using a magnetic resonance imaging (MRI) system, the steps of the method comprising: (a) providing MR data acquired with an MRI system, the acquired MR data representing a plurality of different signal evolutions acquired using different acquisition parameter settings; (b) generating an initial dictionary comprising a plurality of signal templates that coarsely sample acquisition parameters used when acquiring the provided MR data; (c) comparing the provided MR data with the initial dictionary and: (i) storing the quantitative parameters associated with an entry in the initial dictionary as the estimated quantitative parameters when the comparison satisfies a threshold criterion; and (ii) updating the initial dictionary when the comparison does not satisfy the threshold criterion, and wherein step (c) is repeated using the updated dictionary. 2 . The method as recited in claim 1 , wherein updating the initial dictionary in step (c)(ii) includes performing a global optimization that searches a parameter space encompassing potential quantitative parameters, and added entries to the initial dictionary based on the global optimization. 3 . The method as recited in claim 2 , wherein performing the global optimization includes identifying a seed entry based on the comparison performed in step (c). 4 . The method as recited in claim 3 , wherein the seed entry is identified as an entry in the initial dictionary that when compared to the provided MR data yields a similarity value closest to the threshold criterion. S. The method as recited in claim 1 , further comprising generating a quantitative parameter map using the estimated quantitative parameters. 6 . The method as recited in claim 1 , wherein the different acquisition parameter settings includes at least one of an echo time, a repetition time, a flip angle, a radio frequency (RF) pulse phase, or a k-space sampling pattern. 7 . The method as recited in claim 1 , wherein the quantitative parameters include at least one of a longitudinal relaxation time, a transverse relaxation time, a longitudinal magnetization, a field map, or a proton density. 8 . A method for estimating quantitative parameters of a subject using a magnetic resonance imaging (MRI) system, the steps of the method comprising: (a) providing MR data acquired with an MRI system, the provided MR data representing a plurality of different signal evolutions acquired using different acquisition parameter settings; (b) generating an initial dictionary comprising a plurality of signal templates that coarsely sample acquisition parameters used when acquiring the provided MR data; (c) comparing the provided MR data with the initial dictionary and: (i) storing the quantitative parameters associated with an entry in the initial dictionary as the estimated quantitative parameters when the comparison satisfies a threshold criterion; and (ii) updating the threshold criterion when the comparison does not satisfy the threshold criterion, and wherein step (c) is repeated using the updated threshold criterion. 9 . The method as recited in claim 8 , wherein updating the threshold criterion in step (c)(ii) includes performing a global optimization that searches a parameter space encompassing potential quantitative parameters and uses entries in the initial dictionary as seed entries for new entries based on the global optimization. 10 . The method as recited in claim 9 , wherein the seed entries are identified as an entries in the initial dictionary that, when compared to the provided MR data, yield a similarity value within the updated threshold criterion. 11 . The method as recited in claim 8 , further comprising generating a quantitative parameter map using the estimated quantitative parameters. 12 . The method as recited in claim 8 , wherein the different acquisition parameter settings includes at least one of an echo time, a repetition time, a flip angle, a radio frequency (RF) pulse phase, or a k-space sampling pattern. 13 . The method as recited in claim 8 , wherein the quantitative parameters include at least one of a longitudinal relaxation time, a transverse relaxation time, a longitudinal magnetization, a field map, or a proton density. 14 . A magnetic resonance imaging (MRI) system, comprising: a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject arranged in the MRI system; a magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field; a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject using a coil array; a computer system programmed to: (a) control the magnetic gradient system and RF system to acquire MR data from the subject using different acquisition parameter settings and representing a plurality of different signal evolutions; (b) generate an initial dictionary comprising a plurality of signal templates that coarsely sample acquisition parameters used when acquiring the MR data; (c) compare the MR data with the initial dictionary and: (i) store the quantitative parameters associated with an entry in the initial dictionary as the estimated quantitative parameters when the comparison satisfies a threshold criterion; (ii) update the initial dictionary when the comparison does not satisfy the threshold criterion, and wherein step (c) is repeated using the updated dictionary; and (d) generate a quantitative parameter map of the subject using the estimated quantitative parameters. 15 . The system as recited in claim 14 , wherein the computer system is further configured to perform a global optimization that searches a parameter space encompassing potential quantitative parameters, and add entries to the initial dictionary based on the global optimization to update the initial dictionary. 16 . The system as recited in claim 15 , wherein the computer system is further configured to identify a seed entry based on the comparison performed in step (c) to perform the global optimization. 17 . The system as recited in claim 16 , wherein the computer system is further configured to identify the seed entry as an entry in the initial dictionary that, when compared to the MR data, yields a similarity value closest to the threshold criterion. 18 . The system as recited in claim 14 , wherein the different acquisition parameter settings includes at least one of an echo time, a repetition time, a flip angle, a radio frequency (RF) pulse phase, or a k-space sampling pattern. 19 . The system as recited in claim 14 , wherein the quantitative parameters include at least one of a longitudinal relaxation time, a transverse relaxation time, a longitudinal magnetization, a field map, or a proton density.
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Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T) · CPC title
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