FALCON V2 (Fast Algorithms for motion correction) is an advanced, fully-automatic tool for motion correction in dynamic total-body or whole-body PET imaging. Designed with flexibility and reliability at its core, it's now even more versatile, capable of operating across various operating systems and architectures. π
If you find FALCON useful, please cite the following publications:
In this analysis, we are examining the mean image of 20 dynamic frames of a 68Ga-PSMA study both before and after motion correction. By comparing the two mean images, we can clearly see the significant improvement that results from motion correction. The mean image after motion correction appears noticeably sharper and more defined than the one before correction.
Creating a virtual environment is highly recommended to avoid any potential conflicts with other Python packages.
Windows:
python -m venv falconz_env
.\falconz_env\Scripts\activate
Linux/Mac:
python3 -m venv falconz_env
source falconz_env/bin/activate
With your virtual environment activated, install FALCON V2 using pip:
pip install falconz # stable recommended version
FALCON supports DICOM, Nifti, Analyze, and Metaimage file formats, whether it's a single 4D image or multiple 3D images. Simply specify the directory where the files are located and indicate the registration type. FALCON will take care of the rest.
To use FALCON, use the following syntax:
falconz -d path_to_4d_images -r <rigid | affine | deformable> -i <number_of_iterations_per_level> -sf <starting_frame_from_which_moco_should_be_performed> -rf <reference_frame>
Here's an example of using FALCON in Pro mode:
falconz -d /Documents/Sub001 -r deformable -i 100x50x25 -sf 0 -rf -1
In this example, FALCON is performing deformable registration with 100, 50, and 25 iterations at each level of the multi-scale registration. The registration starts from the 1st frame and uses the last frame as the reference.
Here's an example of using FALCON in lazy mode:
falconz -d /Documents/Sub001 -r deformable # for whole-body registration
falconz -d /Documents/Sub001 -r rigid # for brain only studies (much faster processing)
We also offer a specialized π Dash mode, engineered for rapid motion correction across total-body datasets. Execute complex whole-body registration tasks at unprecedented speeds β‘ with a simple command! π©βπ»π¨βπ»
falconz -d /Documents/Sub001 -r deformable -m dash # for high-velocity whole-body registration
As shown above, you don't need to specify many additional parameters. The rest of the parameters are either inferred or set automatically based on common standards.
β οΈ Note: If you're not satisfied with the 'inferred' start frame, you can always set it on your own (the internal threshold is set to be quite safe). Refer to the manuscript for more information.
If you need help with FALCON or want to review the command-line options, use:
falconz --help
Please note that the number of iterations is specified as a string of values separated by 'x' in the -i
option. For example, to perform 50 iterations at each level, you would use -i 50x50x50
.
π Required Folder Structure:
FALCON accepts the following file formats for dynamic PET images: .dcm
, .ima
, .nii
, .nii.gz
, and .img/hdr
.
Here are some examples to illustrate the accepted folder structures:
For a bunch of DICOM (.dcm) or IMA (.ima) files:
βββ PET_WB_DYNAMIC_(QC)_0005
βββ XXX_1.dcm
βββ XXX_2.dcm
βββ ...
For a single 4D Nifti (.nii or .nii.gz) file:
βββ PET_WB_DYNAMIC_(QC)_0005
βββ XXX.nii.gz
For a bunch of 3D Nifti (.nii or .nii.gz) files:
βββ PET_WB_DYNAMIC_(QC)_0005
βββ XXX_1.nii.gz
βββ XXX_2.nii.gz
βββ ...
For Analyze (.img/hdr) files:
βββ PET_WB_DYNAMIC_(QC)_0005
βββ XXX.img
βββ XXX.hdr
βββ ...
The main folder, PET_WB_DYNAMIC_(QC)_0005
, should contain the dynamic PET images to be motion corrected.
Upon successful execution, FALCON auto-generates an organized output directory, positioned at the same hierarchical level as your original dynamic PET image folder. This dedicated directory carries a unique naming schema that incorporates 'FALCONZ', the version number, and a timestamp for easy identification.
Here's a snapshot of the output folder structure:
FALCONZ-V02-2023-09-03-17-28-17/ # Automatically generated results folder
βββ Motion-corrected-images # Corrected dynamic PET images
βββ ncc-images # Normalized Cross-Correlation images for start frame identification
βββ Split-Nifti-files # Individual 3D Nifti files
βββ transforms # Transformation data for motion correction
This is where you'll find the final dynamic PET images, now refined through motion correction procedures. π
A collection of Normalized Cross-Correlation imagesβthese serve as essential tools π οΈ for determining the most appropriate start frame for motion correction.
This folder contains individual 3D Nifti files, which are crucial ποΈ for conducting the motion correction operations.
This section archives the warp fields in cases of deformable registration and the transformation matrices π for rigid or affine registrations, allowing for transparency and potential reusability of these parameters.
FALCON doesn't just deliver high-precision motion-corrected images; it also provides a comprehensive, organized output structure ποΈ designed for immediate utility and future analysis. π
Before raising an issue, check out our growing Frequently Asked Questions section.
Lalith Kumar Shiyam Sundar π» π |
Sebastian Gutschmayer π» |
Z.K Li π |
Manuel Pires π» |