http://www.k-wave.org/forum/topic/inverse-reconstruction Support for the k-Wave MATLAB toolbox en-US Wed, 13 May 2026 02:31:12 +0000 http://bbpress.org/?v=1.0.2 q http://www.k-wave.org/forum/search.php http://www.k-wave.org/forum/topic/inverse-reconstruction#post-5463 Wed, 13 Apr 2016 20:29:21 +0000 Leyla 5463@http://www.k-wave.org/forum/ <p>Hi Brad,</p> <p>Thank you so much.</p> <p>Best Leyla </p> http://www.k-wave.org/forum/topic/inverse-reconstruction#post-5452 Tue, 05 Apr 2016 09:27:56 +0000 Bradley Treeby 5452@http://www.k-wave.org/forum/ <p>Hi Leyla,</p> <p>The <code>sensor_data</code> 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.</p> <p>Brad. </p> http://www.k-wave.org/forum/topic/inverse-reconstruction#post-5440 Tue, 29 Mar 2016 10:36:47 +0000 Leyla 5440@http://www.k-wave.org/forum/ <p>Hi Ben,</p> <p>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. </p> http://www.k-wave.org/forum/topic/inverse-reconstruction#post-5416 Mon, 14 Mar 2016 10:41:37 +0000 Leyla 5416@http://www.k-wave.org/forum/ <p>Hi Ben,</p> <p>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 </p> <p><a href="http://www.caam.rice.edu/~optimization/L1/RecPF/" rel="nofollow">http://www.caam.rice.edu/~optimization/L1/RecPF/</a></p> <p>Best Leyla </p> http://www.k-wave.org/forum/topic/inverse-reconstruction#post-5400 Wed, 24 Feb 2016 20:03:37 +0000 bencox 5400@http://www.k-wave.org/forum/ <p>Hi Leyla, </p> <p>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'?</p> <p>Best wishes,<br /> Ben </p> http://www.k-wave.org/forum/topic/inverse-reconstruction#post-5398 Wed, 24 Feb 2016 01:12:17 +0000 Leyla 5398@http://www.k-wave.org/forum/ <p>Hi,Ben </p> <p>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? </p> <p>Best,Leyla </p> http://www.k-wave.org/forum/topic/inverse-reconstruction#post-5396 Mon, 22 Feb 2016 11:31:53 +0000 bencox 5396@http://www.k-wave.org/forum/ <p>Hi Leyla, </p> <p>I can't quite follow what you're doing. Can you explain a bit more? </p> <p>Thanks,<br /> Ben </p> http://www.k-wave.org/forum/topic/inverse-reconstruction#post-5392 Sat, 20 Feb 2016 13:56:27 +0000 Leyla 5392@http://www.k-wave.org/forum/ <p>Hi Dr. Treeby and Dr. Cox</p> <p>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. </p> <p>What do you thing where I am wrong? Is not possible to reconstruct initial distribution with using sensor_data?</p> <p>Best Leyla, </p>