Assuming a linear relation between voltage and pressure amplitude, you should be able to map the input voltage to the input pressure (I think its 1 MPa by default), and then use this as a scaling factor, but it's unclear to me what you mean with voltage at the focused area.

What exactly do you mean by transducer sensitivity? You could run additional simulations where you shift the transducer or slightly change some parameters to get a sense of the sensitivity.

Here is a simple implementation to write an iSEG tissue list file, given a dictionary mapping the label index to a name:
https://github.com/dyollb/s4l-scripts/blob/df8e2241f87ca91d71138e9d2b3d4336dadb82dc/src/anisotropic_conductivity/load_labels.py#L44

Thank you so much you are a godsend 🙏 You saved me a couple days worth of work!

In terms of the material settings in the thermal simulation:
The heat transfer rate defines whether heat removal by perfusion should be considered. In the absence of this term, heat is only removed by thermal conduction (diffusion) and boundary conditions. The options provided for heat transfer rate affect whether perfusion is constant or affected by local thermoregulation (temperature (T) dependent perfusion, e.g., to account for vasodilation). As for the heat transfer rate, the heat generation rate term can be constant or affected by local thermoregulation (T dependent, reflecting increased metabolic activity with increasing temperature). It is also possible to introduce time-dependent heat generation, e.g., to model a heating battery.
Baseline perfusion values (incl. variability information) are available in the IT'IS database and can be automatically assigned from sim4life: (http://www.itis.ethz.ch/virtual-population/tissue-properties/database/database-summary/).
If non-constant perfusion should be applied or not depends on the tissue and temperature increase magnitude (e.g., muscle above 39 starts to have a strong perfusion increase). The conservativeness of a perfusion model choice is application-dependent.

To simulate the heating effect of tissues over time, blood perfusion and heat generation rate (metabolic heat generation) of the tissue would also need to be considered. The perfusion is covered in the option "Heat Transfer Rate". All options that you can enter here are related to heat-transfer based removal of energy from the system. Perfusion can be adjusted by changing the type of hear transfer (None, Constant, Linear (T), Piecewise Linear (T).

The constant term assumes constant perfusion, independent of tissue temperature. It is the default assigned when using the IT'IS tissue database in Sim4Life. Linear (T) or Piecewise Linear (T) assume temperature dependent perfusion. You can add your your transition temperatures using the little "+" icon. Please note that the linear coefficients represent the slope of the linear perfusion. Each transition temperature indicates the change of the perfusion rate.
For example, if you wish to enter constant perfusion up to 37 degrees, linear perfusion of 10 times the "standard" perfusion rate between 37 and 43 degrees, and constant perfusion above 43 degrees, then you would enter:
transition temperatures: [37,44], linear coefficients: [10,0].

To perform such a study, define your region or quantity of interest, then parametrize it. After a quantity has been parametrized, you can run multiple simulations using the built in Sweeper feature or using the Sim4Life Python API.

There is an example in the Sim4Life manual titled 'Parametrized Patch Antenna' that shows how to use the Sweeper feature.

If using the Python API, the easiest way would be to create a baseline simulation by hand, then right-click on the simulation name and select To Python. Then you would find the quantity of interest in the auto-generated script and assign it to a variable instead of the hardcoded value. Using a simple loop in Python, you can create multiple simulation that have identical setup except for parameters of interest.

In your example, multiple simulations would be run with identical setup except for the grid resolution within a region of interest (e.g., a wire block surrounding a region of interest is placed in a manual grid settings folder, then Maximum Step is changed from 0.3 to 1.0 mm).

Then in Analysis you extract a quantity of interest and compare how this value changes as a function of grid resolution.

If the change in the value is small as you increase resolution, you can proceed in future simulations with a coarser resolution to save on run time. The exact convergence of the value needed depends on your application.

@bryn Thank you Bryn.
Managed to find a version that wasn't corrupted!
Hope you have a nice day 🙂

Are you using Sim4Life lite (web version)? I recorded my screen while I voxeled Duke V2.0 and exported the voxels.
There seems to be a glitch in the Reload button when I try to see the data that was saved in the Study: as a workaround the Reload button in the DATA tab works. I voxeled at 15mm, so the skin is not completely closed, but if you choose something like 5-10mm it should be enough to resolve thin layers (like the skin):

BTW. if you are using a desktop version of Sim4Life, the workflow is similar, except that the software uses native Windows file dialogs to save/load files.

Youtube Video

Assuming 'sim' is the acoustic simulation you're interested in and that 'elements' is a list of the elements (gotten from entities = model.AllEntities()) and that 'amp' and 'phase' are numpy arrays with the amplitude, phase values you're interested in assigning:

# Print current simulation name print(sim.Name) # Remove all sources before adding them to_del = [] for s in sim.AllSettings: if isinstance(s,acoustic.SourceSettings): to_del.append(s) for d in to_del: sim.Remove(d) # Add sources with correct element and parameters for source_idx in np.arange(len(elements)): source_settings = acoustic.SourceSettings() source_settings.Amplitude = amp[source_idx] source_settings.Phase = phases[source_idx] sim.Add(source_settings, rings[source_idx]) print(rings[source_idx].Name, amp[source_idx], phases[source_idx])

Sorry I'm not providing a full example. Easiest way to do that would be to manually create the whole simulation without the sources (just make all sources as materials), then right click on simulation and select 'To Python...' and then add the lines above to your code

You could do something like this to get the elements as a list:

src_ents = [] entities = model.AllEntities() for e in entities: if 'Element' in e.Name: src_ents.append(e)

Careful, I have no idea how the elements will be sorted in that case but you could use a fixed size numerical suffix and then sort them.

Hallo,
There should be, in the same folder where the smash project is, a file named as the smash file, but starting with a ".", like ".Name_of_the_File.smash.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx".
Try to drag that file directly into Sim4Life and save it with a different name. From there you should be able to go on with the models/simulations/analysis settings already saved.

I have two versions installed (6.2.2 and 7.00) and I tried opening the file with both. I have now deleted the smash file that wasn't working and started over, but if it happens again, I will try the import option- thanks!

If you don't have a full version, i think (untested) you can still recover the model by importing the smash file in the modeling tab.

Sorry, don't have S4L with neuron available at the moment, but I looked at the tutorial and it seems to have changed. When you create the sensor settings can you try to drag and drop the axon to the sensor setting and then chose the section name? (That was the old way of doing it)

Still, you should be able to run the simulation without a point sensor.. Sorry can't help more

I am using Sim4Life 6.2.1.4972.
It happened once when I've had put a high "maximum number of samples" in the time domain (200 I think) with the acoustic head tutorial that I changed a bit, by putting 2 transducers instead of 1.
Another time it was weird because it happened with a SEFT targeting a square of skin, which was rather simple but it did crash again. My simulation was probably wrong in a way, but it didn't throw and error and just stopped.

But now that I am applying @gbgbha advices, it is fine 🙂 and thanks for the tip ! I'll try for the file that I lost

The TRP is computed in a different way than the input power. Both ways are mathematically correct and would both be "exact" if there were no numerical errors. In any FDTD simulation, however, there are spatial (finite grid) or temporal (finite time step) discretization error.
What you are seeing in this half-wave dipole example, is that those discretization errors are larger than the precision you would require to distinguish TRP from input power (because the radiation efficiency is very high in this case, there is almost no difference between TRP and input power).

To solve this "problem", either you accept that the simulation results are accurate enough for your needs (the warning is a simple consistency check) or you increase the precision of the simulation. You can do so by increasing the grid resolution, the overall convergence level and the resolution of the far-field sensor. This will be computationally expensive, though.

To help you understand, you could try to add some lossy media in your (coarse) simulation (e.g. place the dipole next to a phantom or any other dielectric). The simulation will not be more precise, but the numerical errors will be less "obvious" because they will be dwarfed by the losses occurring in the dielectric. The TRP will be lower than the input power and the warning will not be triggered.

I hope this helps.