Program Information
A Fully-Automated Pipeline for Generating CT Images Across a Range of Doses and Reconstruction Methods
S Young*1, P Lo1, J Hoffman1, M Wahi-Anwar1, F Noo2, M Brown1, M McNitt-Gray1, (1) UCLA School of Medicine, Los Angeles, CA, (2) University of Utah, Salt Lake City, UT
Presentations
TH-AB-207A-5 (Thursday, August 4, 2016) 7:30 AM - 9:30 AM Room: 207A
Purpose: To evaluate the robustness of CAD or Quantitative Imaging methods, they should be tested on a variety of cases and under a variety of image acquisition and reconstruction conditions that represent the heterogeneity encountered in clinical practice. The purpose of this work was to develop a fully-automated pipeline for generating CT images that represent a wide range of dose and reconstruction conditions.
Methods: The pipeline consists of three main modules: reduced-dose simulation, image reconstruction, and quantitative analysis. The first two modules of the pipeline can be operated in a completely automated fashion, using configuration files and running the modules in a batch queue. The input to the pipeline is raw projection CT data; this data is used to simulate different levels of dose reduction using a previously-published algorithm. Filtered-backprojection reconstructions are then performed using FreeCT_wFBP, a freely-available reconstruction software for helical CT. We also added support for an in-house, model-based iterative reconstruction algorithm using iterative coordinate-descent optimization, which may be run in tandem with the more conventional recon methods. The reduced-dose simulations and image reconstructions are controlled automatically by a single script, and they can be run in parallel on our research cluster. The pipeline was tested on phantom and lung screening datasets from a clinical scanner (Definition AS, Siemens Healthcare).
Results: The images generated from our test datasets appeared to represent a realistic range of acquisition and reconstruction conditions that we would expect to find clinically. The time to generate images was approximately 30 minutes per dose/reconstruction combination on a hybrid CPU/GPU architecture.
Conclusion: The automated research pipeline promises to be a useful tool for either training or evaluating performance of quantitative imaging software such as classifiers and CAD algorithms across the range of acquisition and reconstruction parameters present in the clinical environment.
Funding Support, Disclosures, and Conflict of Interest: Funding support: NIH U01 CA181156 Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare Past recipient, research grant support, Siemens Healthcare Consultant, Toshiba America Medical Systems Consultant, Samsung Electronics
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