VICTRE PIPELINE DOCUMENTATION

Author: Miguel A. Lago

Documentation

The Victre automated pipeline currently supports the following steps:

To start a new pipeline object use this function:

import Victre
pline = Victre.Pipeline(ip={"cpu": CPU_IP, "gpu": GPU_IP},
                        phantom_file=PHANTOM_FILE)

ip: dictionary with the following two fields (use localhost if running in local machine)
- gpu: IP address for the GPU enabled node that will run the projection
- cpu: IP address for a computing node
phantom_file: path to a previously generated voxelized phantom in raw format

Optional parameters:

seed: random seed used for the lesion insertion and ROI extraction
results_folder: Path to folder to be used when saving the results
spectrum_file: path to a previously generated spectrum file in raw format
lesion_file Path to file containing the lesion to be inserted (in HDF5 format)
materials Dictionary including the materials to be used during projection (see Constants for an example of this dictionary)
arguments_mcgpu Arguments to be overridden for the projection in MCGPU
arguments_recon Arguments to be overridden for the reconstruction algorithm
flatfield_file Path to the flatfield file for projection

Create the region of interest dictionary for the lesions you want to insert:

roi_sizes = {Constants.VICTRE_SPICULATED: [109, 109, 9],
             Constants.VICTRE_CLUSTERCALC: [65, 65, 5]}

Call the insert_lesions method:

pline.insert_lesions(lesion_type=Constants.VICTRE_SPICULATED,
                     n=2,
                     roi_sizes=roi_sizes)

lesion_type: see Constants for a list of available lesion types
n: number of lesions to be inserted
roi_sizes: dictionary with the size of the regions of interest for each lesion

If the number of lesions is too high or the lesion size is too large, the insertion might not be successful.

Simply call the following methods:

pline.project()
pline.reconstruct()

The process will take several minutes, depending on the speed and number of GPUs available.

If you want to extract regions of interest without any lesion, call the add_absent_ROIs method:

pline.add_absent_ROIs(lesion_type=Constants.VICTRE_SPICULATED,
                      n=2,
                      roi_sizes=roi_sizes)

Parameters and conditions are the same as for insert_lesions.

To extract the ROIs and save them, use the method save_ROIs with the dictionary for the roi_sizes to be used for each type of lesion inserted.

save_ROIs(roi_sizes=roi_sizes)

The Victre pipeline saves the results of each step this way:

Insertion: Results from the lesion insertion will be saved in the phantom subfolder as:
- One gzip compressed raw file containing the voxelized phantom
- One text file (extension .loc) with the locations coordinates in the original phantom for each lesion and the lesion type (negative lesion types are for absent regions of interest)
Projection: Results from the projection will be saved in the results folder in their corresponding seed folder:
- A number of projection raw files from projection_0001.raw to projection_0025.raw (or the number of projections requested)
- One projection corresponding to the digital mammography projection projection_0000.raw
- The input and output file for the MCGPU software input_mcgpu.in and output_mcgpu.out
Reconstruction: Results from the reconstruction will be saved along the results from the projection:
- One file reconstruction.raw with the results from the reconstruction software for the DBT image
- One file projection_3000x1500pixels_25proj.raw that is all the stacked projection slices
- The input and output file for the reconstruction software input_recon.in and output_recon.out
ROI: Results from the ROI extraction will be saved in two different ways along with the other result files:
- All the ROIs together saved as an HDF5 file ROIs.h5
- Each ROI saved in raw format inside the ROIs subfolder