Computed Tomography shows how to use the Intel® oneAPI Math Kernel Library (oneMKL) DFT functionality to simulate computed tomography (CT) imaging.
| Optimized for | Description |
|---|---|
| OS | Linux* Ubuntu* 18.04 Windows* 10, 11 |
| Hardware | Skylake with Gen9 or newer |
| Software | Intel® oneAPI Math Kernel Library (oneMKL) |
| What you will learn | How to use oneMKL Discrete Fourier Transform (DFT) functionality |
| Time to complete | 15 minutes |
For more information on oneMKL and complete documentation of all oneMKL routines, see https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-documentation.html.
Computed Tomography uses oneMKL discrete Fourier transform (DFT) routines to transform simulated raw CT data (as collected by a CT scanner) into a reconstructed image of the scanned object.
In computed tomography, the raw imaging data is a set of line integrals over the actual object, also known as its Radon transform. From this data, the original image must be recovered by approximately inverting the Radon transform. This sample uses Fourier reconstruction for inverting the Radon transform of a user-provided input image. Using batched 1D real DFT of Radon transform data points, samples of the input image's Fourier spectrum may be estimated on a polar grid. After interpolating the latter onto a Cartesian grid, an inverse 2D real DFT produces a fair reproduction of the original image.
This sample performs its computations on the default SYCL device. You can set the ONEAPI_DEVICE_SELECTOR environment variable to *:cpu or *:gpu to select the device to use.
To use oneMKL DFT routines, the sample creates double-precision real DFT descriptor objects and calls the commit member function with a sycl::queue object to define the device and context. The compute_* routines are then called to perform the actual computation with the appropriate descriptor object and input data.
You can use Visual Studio Code (VS Code) extensions to set your environment, create launch configurations, and browse and download samples.
The basic steps to build and run a sample using VS Code include:
- Download a sample using the extension Code Sample Browser for Intel Software Developer Tools.
- Configure the oneAPI environment with the extension Environment Configurator for Intel Software Developer Tools.
- Open a Terminal in VS Code (Terminal>New Terminal).
- Run the sample in the VS Code terminal using the instructions below.
- (Linux only) Debug your GPU application with GDB for Intel® oneAPI toolkits using the Generate Launch Configurations extension.
To learn more about the extensions, see the Using Visual Studio Code with Intel® oneAPI Toolkits User Guide.
Note: If you have not already done so, set up your CLI environment by sourcing the
setvarsscript located in the root of your oneAPI installation.Linux*:
- For system wide installations:
. /opt/intel/oneapi/setvars.sh- For private installations:
. ~/intel/oneapi/setvars.sh- For non-POSIX shells, like csh, use the following command:
$ bash -c 'source <install-dir>/setvars.sh ; exec csh'Windows*:
C:\"Program Files (x86)"\Intel\oneAPI\setvars.bat- For Windows PowerShell*, use the following command:
cmd.exe "/K" '"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" && powershell'For more information on configuring environment variables, see Use the setvars Script with Linux* or MacOS* or Use the setvars Script with Windows*.
Run make to build and run the sample.
You can remove all generated files with make clean.
Run nmake to build and run the sample. nmake clean removes temporary files.
Warning: On Windows, static linking with oneMKL currently takes a very long time due to a known compiler issue. This will be addressed in an upcoming release.
If everything is working correctly, the example program will start with the 400x400 example image input.bmp then create simulated CT data from it (stored as radon.bmp). It will then reconstruct the original image in grayscale and store it as restored.bmp.
./computed_tomography
Reading original image from input.bmp
Generating Radon transform data from input.bmp
Saving Radon transform data in radon.bmp
Reconstructing image from the Radon projection data
Step 1 - Batch of 400 real 1D in-place forward DFTs of length 400
Step 2 - Interpolating spectrum from polar to cartesian grid
Step 3 - In-place backward real 2D DFT of size 400x400
Saving restored image in restored.bmp
If an error occurs, troubleshoot the problem using the Diagnostics Utility for Intel® oneAPI Toolkits. Learn more.
Code samples are licensed under the MIT license. See License.txt for details.
Third party program Licenses can be found here: third-party-programs.txt.