Hi Dr. Treeby and Dr. Cox
I am new to K-wave.I am trying to reconstruct 2D initial photoacoustic pressure distribution with using sensor_data. At first, I calculate partial Fourier of sensor_data and then I used it in total variation minimization algorithm to reconstruct initial distribution. However, I get smoothed sensor_data not initial pressure distribution.
What do you thing where I am wrong? Is not possible to reconstruct initial distribution with using sensor_data?
Best Leyla,
Hi Leyla,
I can't quite follow what you're doing. Can you explain a bit more?
Thanks,
Ben
Hi,Ben
Actually, in your toolbox, you generate sensor data p(t, y) like (time_step,sensor_position).Then you turn it (sensor_position,depth) and finally you get initial distribution in (x,y) plane. For CT image reconstruction, with sensor mask and partial fourier data, I can reconstruct CT image. My question is, can I reconstruct photoacoustic sensor_data via this way or I have to use your projection method?
Best,Leyla
Hi Leyla,
What sort of CT are you doing? The photoacoustic inverse problem can be written as a spherical mean Radon transform, which is different from the more common x-ray CT Radon transform. Does that answer your question? If not, what do you mean by 'partial fourier data'?
Best wishes,
Ben
Hi Ben,
I want to ask that sensor_data that your toolbox is generated with different parameters is a spherical mean value of photoacoustic measurements. Actually, I could not understand the base of your sensor data coefficients. I mean, for example in MRI image reconstruction the base is Fourier coefficients. The optimization problem is defined as in
http://www.caam.rice.edu/~optimization/L1/RecPF/
Best Leyla
Hi Ben,
I want to ask a new question that is related to before. You generated sensor_data with using four parameter. How could you decide dimensions of sensor_data. When I want to reconstruct initial source, I always suffer from dimension problem. Could you explain me how you decide dimensions of sensor_data.
Hi Leyla,
The sensor_data variable in k-Wave is always 2D, indexed as (sensor_index, time_index). The sensor data itself is just a recording of the acoustic pressure time series at each of the defined sensor positions. There is more information in Secs. 3.4 and 3.5 in the k-Wave manual.
Brad.
Hi Brad,
Thank you so much.
Best Leyla
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