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 set of ultrasound data;
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 set of ultrasound data and partition the set of ultrasound data into a set of windows;
process the set of windows to determine a power spectrum for each window of the set of windows;
process the power spectrum for each window of the set of windows to determine a normalized power spectrum for each window of the set of windows; and
process the normalized power spectrum for each window of the set of windows with a machine learning model; and
display 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.
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