Selective sampling for assessing structural spatial frequencies with specific contrast mechanisms
The disclosed embodiments provide a method for acquiring MR data at resolutions down to tens of microns for application in in vivo diagnosis and monitoring of pathology for which changes in fine tissue textures can be used as markers of disease onset and progression. Bone diseases, tumors, neurologic diseases, and diseases involving fibrotic growth and/or destruction are all target pathologies. Further the technique can be used in any biologic or physical system for which very high-resolution characterization of fine scale morphology is needed. The method provides rapid acquisition of signal at selected values in k-space, with multiple successive acquisitions at individual k-values taken on a time scale on the order of microseconds, within a defined tissue volume, and subsequent combination of the multiple measurements in such a way as to maximize SNR. The reduced acquisition volume, and acquisition of only signal values at select places in k-space, along selected directions, enables much higher in vivo resolution than is obtainable with current MRI techniques.
1. A method for operating a magnetic resonance (MR) machine comprising: exciting a volume of interest (VOI) with the MR machine; encoding a first k-value within a selected gradient pulse to induce phase wrap to create an encode for the first k-value within the VOI with orientation in a direction determined by a vector combination of machine gradients; and recording a plurality of samples of a signal at a first k-value during a single excitation.
2. The method as defined in claim 1 further comprising:
decrementing the set of k-values from the first k-value to obtain a series of k-values; and
recording a plurality of samples of a signal at each of the series of k-values.
3. The method as defined in claim 2 wherein the step of decrementing comprises:
applying a plurality of k-value selection gradient pulses and the step of recording samples comprises:
recording samples of a signal after applying each of the plurality of k-value selection gradient pulses.
4. The method as defined in claim 2 wherein the steps of decrementing and recording are executed in the single excitation.
5. The method as defined in claim 1 further comprising:
incrementing the set of k-values from the first k-value to obtain a series of k-values; and
recording samples of a signal at each of the series of k-values.
6. The method as defined in claim 5 wherein the step of incrementing comprises:
applying a plurality of positive k-value selection gradient pulses and the step of recording samples comprises:
recording samples of a signal after applying each of the plurality of k-value selection gradient pulses.
7. The method as defined in claim 6 wherein the steps of incrementing and recording are executed in the single excitation.
8. A method of processing data output from a magnetic resonance (MR) machine comprising:
receiving a set of sample data output from a magnetic resonance (MR) machine relating to a volume of interest (VOI) including a plurality of samples of a signal recorded at each k-value in a set of k-values;
applying simulated encoding with a selected gradient pulse to induce phase wrap to create an encode for the first k-value within the VOI with orientation in a direction determined by a vector combination of machine gradients; and
processing the set of sample data to produce a texture data set of signal vs k-values for the set of k-values to characterize the textural features of tissue in the VOI.
9. The method as defined in claim 8 wherein the step of processing further comprises:
initiating a series of simulated gradients to produce incremented or decremented k-value encodes for a k-value set, the k-value set being a subset of that required to produce an image of the VOI;
recording multiple sequential samples of simulated NMR RF signal encoded with the k-value set to produce the texture data set.
10. The method as defined in claim 8 further comprising processing the texture data set with an algorithm to output a diagnosis for a patient.
11. The method as defined in claim 10 wherein the algorithm is a machine learning algorithm.
12. The method as defined in claim 11 further comprising repeating the step of receiving a set of sampled data across a high number of VOIs.
13. The method as defined in claim 8 wherein the step of processing further comprises:
initiating a series of simulated gradients to produce incremented or decremented k-value encodes for a k-value set, the k-value set being a subset of that required to produce an image of the VOI;
recording multiple sequential samples of simulated NMR RF signal encoded with the k-value set to produce the texture data set.
14. A method of processing data output from a magnetic resonance (MR) machine comprising: receiving a set of sample data output from a magnetic resonance (MR) machine relating to a volume of interest (VOI) including a plurality of samples of a signal recoded at each k-value in a set of k-values; processing the texture data set with a machine learning algorithm to output a diagnosis for a patient; wherein the machine learning algorithm is a supervised machine learning algorithm; applying the supervised machine learning algorithm to the textual dataset and to comparative datasets to optimize acquisition parameters including VOI dimensions and acquisition direction, using peak contrast of a targeted feature; repeating the step of sample data: processing the texture data set and applying the supervised machine learning algorithm across a high number of VOIs.
15. The method as defined in claim 14 further comprising:
using unsupervised machine learning across the high number of VOIs in the targeted feature with specific disease markers to identify salient features additional to that called out for supervised learning; and
using machine learning algorithms to correlate those salient features with information known regarding disease onset and progression in the tissue samples towards biomarker identification.
16. A method of processing data output from microCT comprising:
receiving a set of sample data output from microCT relating to a volume of interest (VOI) including samples of a signal recorded at a set of k-values; and
processing the set of sample data, by applying a simulated encoding gradient pulse to induce phase wrap to create an encode for a specific k-value and orientation in the set of sample data to produce a texture data set of signal vs k-values for the set of k-values to characterize the textural features of tissue in the VOI.
17. The method as defined in claim 16 wherein the step of processing further comprises:
initiating a series of simulated gradients to produce incremented or decremented k-value encodes for a k-value set, the k-value set being a subset of that required to produce an image of the VOI;
recording multiple sequential samples of simulated NMR RF signal encoded with the k-value set to produce the texture data set.
18. A method of processing data output from histology staining comprising: receiving a set of sample data output from the histology stained images relating to a volume of interest (VOI) including samples of a signal recorded at a set of k-values; and processing the set of sample data by applying a simulated encoding gradient pulse of a magnetic resonance (MR) machine to induce phase wrap to create an encode for a specific k-value and orientation in the set of sample data to produce a texture data set of signal vs k-values for the set of k-values to characterize the textural features of tissue in the VOI.