Low Contrast Detectability for CT (LCD-CT) Toolbox provides a common interface to evaluate the low contrast detectability (LCD) performance of advanced nonlinear CT image reconstruction and denoising algorithms. The toolbox uses model observers (MO) to evaluate the LCD of targets with known locations in test images obtained with the MITA-LCD phantom. The model observer detection accuracy is measured by the area under the receiver operating characteristic curve (AUC) and the detectability signal-to-noise ratio (d’_{snr}). The LCD-CT toolbox can be used by CT developers to perform initial evaluation on image quality improvement or dose reduction potential of their reconstruction and denoising algorithms.
- Regulatory Science Tool: Check the FDA website for a description of the LCD-CT toolbox in the Regulatory Science Tool Catalog
- Creating digital replica of the background and signal modules of the MITA-LCD phantom.
- Simuating sinogram and generate fan-beam CT scans of the digital phantoms based on the publicly available Michigan Image Reconstruction Tolbox (MIRT).
- Estimating low contrast detectability performance from the MITA-LCD phantom CT images using channelized Hoteling model observer with Laguerre-Gauss (LG) channels and two options of Difference-of-Gaussian (DOG) channels and Gabor channels.
Requirements
- Python (>= 3.8) with packages listed in pyproject.toml (numpy, scipy, scikit-image, etc.)
- OR
- Matlab (version > R2016a) or Octave (version > 4.4)
- If the above Matlab or Octave requirements are not met, then conda is required to install Octave using the installation instructions.
If required versions of Matlab or Octave are not available on your system (see how to get matlab version or octave version) then see installation for how to setup an Octave environment to run LCD-CT.
Installation
- Git clone the LCD-CT Toolbox repository:
git clone https://github.com/DIDSR/LCD_CT
cd LCD_CTPython Installation:
Create a conda environment and install the package:
conda env create --file environment.yml conda activate LCD_CT pip install -e .Expected run time: 2-5 min
MATLAB/Octave Installation (Legacy):
*If neither Matlab or Octave are installed or do not meet the version requirements, you can source install.sh to prepare a conda environment. Note: this can take about 10 minutes to complete.
source install.shExpected run time: 10-30 min
- Test the installation
Python: Run the tests using pytest:
pytest tests/test_lcd.py
From the bash command line octave test.m or matlab -batch test.m
From the Matlab or Octave interactive prompt
>> testExpected run time (Octave): 1 min 30 s
- RST Reference Number: RST24MD08.01
- Date of Publication: 09/24/2023
- Recommended Citation: U.S. Food and Drug Administration. (2023). LCD-CT: Low-contrast Detectability (LCD) Test for Assessing Advanced Nonlinear CT Image Reconstruction and Denoising Methods (RST24MD08.01). https://cdrh-rst.fda.gov/lcd-ct-low-contrast-detectability-lcd-test-assessing-advanced-nonlinear-ct-image-reconstruction-and
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About the Catalog of Regulatory Science Tools The enclosed tool is part of the Catalog of Regulatory Science Tools, which provides a peer-reviewed resource for stakeholders to use where standards and qualified Medical Device Development Tools (MDDTs) do not yet exist. These tools do not replace FDA-recognized standards or MDDTs. This catalog collates a variety of regulatory science tools that the FDA's Center for Devices and Radiological Health's (CDRH) Office of Science and Engineering Labs (OSEL) developed. These tools use the most innovative science to support medical device development and patient access to safe and effective medical devices. If you are considering using a tool from this catalog in your marketing submissions, note that these tools have not been qualified as Medical Device Development Tools and the FDA has not evaluated the suitability of these tools within any specific context of use. You may request feedback or meetings for medical device submissions as part of the Q-Submission Program. For more information about the Catalog of Regulatory Science Tools, OSEL_CDRH@fda.hhs.gov.