Low Contrast Detectability for CT Toolbox

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 standard uniform-background MITA-LCD phantom or a nonuniform-background Liver-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

Features

1. Digital phantom and CT simulaiton:

• Creating digital replica of the background and signal modules of the MITA-LCD phantom and a digital Liver-LCD phantom that contains low-contrast disks in a non-uniform, anatomical background.

• Simuating sinogram and generate fan-beam CT scans of the digital phantoms based on the publicly available Michigan Image Reconstruction Tolbox (MIRT). Note that the MIRT package is compatible with Linux and mac systems but may not be fully compatible with Windows (see MIRT Readme ).

  (This feature runs best in MATLAB.)

2. LCD test:

• Estimating low contrast detectability performance from the MITA-LCD or Liver-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.

  (This feature runs in both MATLAB and python.)

3. Dose reduction estimation:

• • Estimating the dose reduction percentages of an evaluated nonlinear reconstruction method relative to a reference method (e.g., filtered back projection method) using the AUC results of the evaluated and the reference reconstruction methods across multiple dose levels obtained from a LCD test.

  (This feature runs only in python.)

Start Here

Requirements

• Python (>= 3.8) with packages listed in pyproject.toml (numpy, scipy, scikit-image, etc.)
• Matlab (version > R2016a)

Installation

1. Git clone the LCD-CT Toolbox repository:

git clone https://github.com/DIDSR/LCD_CT
cd LCD_CT

2. Python:

• Create a conda environment and install the package:

  conda create -n LCD_CT python=3.8 pip -y
  conda activate LCD_CT
  conda install -c conda-forge -c defaults octave cxx-compiler pandas tomli numpy oct2py pytest simpleitk scikit-image scikit-learn scipy matplotlib sphinx-tabs pandoc ipykernel git -y
  pip install "git+https://github.com/DIDSR/pediatricIQphantoms" sphinxcontrib-svg2pdfconverter nbsphinx
  pip install -e .

  Expected run time: 2-5 min

• Test the python installation

  Run the following tests in the conda LCD-CT virtual environment:

  pytest tests/test_lcd.py
  python demo_analyze_dose_reduction.py

3. MATLAB:

   MATLAB version information is available from MATLAB. See how to get matlab version on your system.

   From the bash command line

   matlab -batch "test"

   Or, from the Matlab prompt

   >> test

   Expected run time: 2 min 30 s

4. (Optional) GNU OCTAVE:

   If Matlab is not available, GNU Octave (version > 4.4) can be installed using source install.sh to prepare a conda environment.

   Please note that the MATLAB part of this software has been fully developed and tested using MATLAB. GNU Octave may also be used to run the software; however, Octave compatibility has not been fully tested or validated. Although Octave is largely compatible with MATLAB, differences exist in the availability and behavior of certain functions and toolboxes. Users who choose to run this software with GNU Octave may need to install additional Octave packages and modify or replace MATLAB-specific functions with their Octave-compatible equivalents. Users are responsible for making any necessary adaptations for their specific Octave environment and for verifying that the resulting outputs are consistent with their expected use.

• Create a Conda Octave environment and install the package:

  source install.sh

  Expected run time: 10-30 min

• Test Octave

  In the Conda Octave virtual environment, run:

  octave test.m

  *Expected run time: 2 min.

  Note: The Liver-LCD Phantom Creation code (makeCT_LiverLCD.m in folder "LCD Phantom creation") is not compatible with Octave, because it calls mex-functions in MIRT that are not compiled for Octave (check MIRT webpage ). For this reason, makeCT_LiverLCD is excluded from being tested in Octave. Use MATLAB to run makeCT_LiverLCD.m.

Tool Reference

• 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

Disclaimer

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, email RST_CDRH@fda.hhs.gov.