CW-BOS imaging of high intensity ultrasonic pressure fields.

In this study, there are three parts to quantitatively map focused-ultrasound (FUS) pressure fields based on CW-BOS imaging.

CW-BOS simulations: Perform numerical FUS beam simulations and generate the training data.

demo_Simulations.m
- Workflow to generate simulated data, perform SVD on the dictionary, and construct training set.
wave_prop_simu.m
- Generate simulated pressure of different levels and transducers. "ablvec.p", "march_asr.p" "precalculate_abl", "precalculate_ad.p" and "precalculate_mas.p" need to be called.
forward_model.m
- Calculate the projected pressure and displacements in both two dimensions on the background pattern.
accum_d.m
- Calculate displacements of each slice, need to be called in forward_model_dxdz.m

testacq
- Workflow to acquire FUS and non-FUS photos.
- Need to call "BOSTomoController.py" to communicate with iPad.
BOSTomoController.py
- On the experiment computer to switch the background pattern.
BOSTomoDisplay_app_IPad.py
- On an APP named Pythonista of the iPad to display background patterns and IP address of iPad.
ShutterController_Arduino.ino
- Code to be uploaded to the Arduino board, which allows Arduino to control waveform generator and DLSR camera.
Modified_camera_shutter_design.zip
- Modified camera shutter with an analog switch.

Reconstruction: Train deep neural network, process the acquired actual photo by CW-BOS system and reconstruct root-mean-square(RMS) projected pressure by pre-trained neural network.

demo_trainingdata_writer.py
- Write training data with HDF5 format.
svd_trainDNN.py
- Train a multi-layer deep neural network.
process_photo.m
- Segment actual photos acquired by DSLR camera in to small patches of rectangular histograms.
demo_predict.py
- Reconstruct RMS projected pressure from actual photos.
"model116.h5" and "model225.h5" are pre-trained model for two transducers (1.16MHz and 2.25MHz).

Zebrography