Machine learning to extract quantitative biomarkers from RF spectrums
The present disclosure provides for ultrasound systems and methods to pre-process ultrasound data to distinguish abnormal tissue from normal tissue. An exemplary method can include receiving a set of ultrasound data and partitioning the set into a set of windows. The method can then provide for processing the set of windows to determine a power spectrum for each window. The power spectrum for each window can be processed to determine a normalized power spectrum for each window. This normalized power spectrum can be processed for each window with a machine learning model. The method can then provide for displaying an image where each window of the set of windows is displayed using a unique identifier based on the output of the machine learning model.
1. An ultrasound system comprising:
a transducer configured to output a single static set of raw ultrasound radio frequency (RF) data forming a single frame of a tissue area;
a memory containing machine readable medium comprising machine executable code having stored thereon instructions;
a signal processing unit comprising one or more processors coupled to the memory, the one or more processors configured to execute the machine executable code to the cause the one or more processors to:
receive the single static set of raw ultrasound RF data;
process the single static set of raw RF ultrasound data with a machine learning model; and
output classification of the raw RF ultrasound data from an output of the machine learning model.
2. The ultrasound system of claim 1 , wherein the machine learning model is a k-means model or a model from a deep learning network comprising but not limited to one or a combination to the algorithm types of CNN (Convolutional Neural Network), RBM (Restricted Boltzmann Machine), LSTM (Long Short Term Memory) or CapsNet (Capsule Networks).
3. The ultrasound system of claim 1 , wherein the machine learning model is trained using ultrasound images labeled by a radiologist.
4. The ultrasound system of claim 1 , wherein the machine learning model is an unsupervised model.
5. The system of claim 1 , wherein the processor is operable to display an image representing the set of ultrasound data using a unique identifier based on the output of the machine learning model.
6. A method comprising:
receiving a single static set of raw ultrasound radio frequency (RF) data output from at least one ultrasound transducer, the single static set of raw ultrasound RF data forming a single frame representing a tissue area of a patient;
processing the single static set of raw ultrasound RF data set of windows to determine a power spectrum;
processing the determined power spectrum as an input into a machine learning model; and
outputting a classification of the tissue area from the machine learning model.
7. The method of claim 6 , wherein the classification is a cancer status of the tissue.
8. The method of claim 6 , wherein the method further comprises processing the power spectrum to determine a normalized power spectrum.
9. The method of claim 6 , wherein the power spectrum is taken using a continuous Fast Fourier Transform (FFT).
10. The method of claim 6 , wherein the power spectrum is taken using a discrete FFT.
11. A method comprising:
receiving a single static set of raw ultrasound radio frequency (RF) data output from at least one ultrasound transducer forming a single frame representing a tissue area of a patient;
processing the single static set of ultrasound RF data as an input to a machine learning model; and
outputting a classification of the tissue area from the machine learning model.
12. The method of claim 11 further comprising:
partitioning the single static set of raw ultrasound RF data into a set of windows or bounding boxes; and
processing the set of windows to output a power spectrum for each window of the set of windows.
13. The method of claim 12 , wherein the step of processing the set of windows comprises processing the set of windows to output a time frequency domain processing technique.
14. The method of claim 11 , wherein the machine learning model is a k-means model or a model from a deep learning network comprising one or a combination to the algorithm types of CNN (Convolutional Neural Network), RBM (Restricted Boltzmann Machine), LSTM (Long Short Term Memory) or CapsNet (Capsule Networks).
15. The method of claim 11 , wherein the machine learning model is trained using ultrasound images labeled by a radiologist.
16. The method of claim 11 , wherein the machine learning model is an unsupervised model.
17. The method of claim 11 , wherein the classification is a cancer status of the tissue.
18. The method of claim 11 , further comprising outputting a specific multi-parametric biomarker from the machine learning model.
19. An ultrasound system comprising:
a transducer configured to output a single static set of raw ultrasound radio frequency (RF) data forming a single frame of a tissue area; and
a signal processing unit operable to:
receive the single static set of raw ultrasound RF data
process the set of single static set of raw ultrasound RF data to determine a raw power spectrum;
process the raw power spectrum as an input to a machine learning model; and
output a classification of the raw power from an output of the machine learning model.
20. The system of claim 19 , further comprising a display, wherein the signal processing unit is operable to display an image representing the set of power spectrum data using a unique identifier based on the output of the machine learning model on the display.