SIIM 2016 Scientific Session Research & Knowledge Management Wednesday, June 299:45 am – 10:45 am Open Source Platform for Virtual Clinical Trials

Cedric F. Marchessoux, PhD, Barco; Ali Avanaki, PhD; Kathryn S. Espig, MS (Presenter)

Background

Developing medical imaging systems is very expensive, especially during the clinical validation phase. For over 10 years, Barco has been developing a simulation platform for facilitating the development and pre-clinical validation of imaging systems by modeling different components of an imaging chain (namely acquisition, display and observer) [1]. In 2009, the term Virtual Clinical Trial (VCT) was introduced by researchers at University of Pennsylvania and Barco for such simulations. VCT has at least two significant roles: quantitative and objective assessment of system performance in the design of novel imaging features or methods; and, validation of clinical trial designs prior to execution of the real clinical trials. There is some open source available on the internet corresponding to some components of the virtual medical imaging chain but not to the full chain ([2], [3]). For a realistic simulation, every component must be considered from the image acquisition, to its visualization, and to anthropomorphic observation. This chain has been successfully used at the academia and the industry. The first and only FDA approved medical display for Digital Breast Tomosynthesis has been developed, optimized and validated by using VCT [4].

Case Presentation

A complete VCT pipeline has been constructed by combining the breast anatomy and image acquisition simulation pipeline developed at the University of Pennsylvania, and the MeVIC image display and observation pipeline developed by researchers at Barco, Inc [1]. The aforementioned chain is modeled as a cascade of three main modules: the virtual image capture, the virtual display and the virtual observer. To facilitate the development and the sharing of the VCT, the source code has been released on the internet with a dedicated license for its use and for potential contributions (https://github.com/Barco-VCT/VirtualClinicalTrials/blob/master/license.txt). The chain is cross platform and developed in C++. The platform has three components: the core (https://github.com/Barco-VCT/VirtualClinicalTrials/tree/master/vct/src/Container), the modules (https://github.com/Barco-VCT/VirtualClinicalTrials/tree/master/vct/src/Modules) and the dependencies (https://github.com/Barco-VCT/VirtualClinicalTrials/tree/master/vct/src/Dependencies). There are internal and external dependencies all compatible with the license above. External dependencies include Boost, openCV and OpenCL allowing GPU-accelerated processing. Attention has been given to the design pattern of the program in C++ with the use by instance of modules as follows. In order to run the chain without recompiling the whole platform each time, a factory is used with an automatic run. An ini file (in xml) is used for defining the modules of the pipeline with parameter names and values. Each new module inherits from the class Module and is therefore constrained to have the same data structure and methods. In order to help the development of new modules but also the maintenance of the platform with nightly builds, numerous test units have been developed. The test units are organized using xml ini file, and consist of input and output files.

Github is selected to host the repository because of its popularity and allowing for easy inspection and maintenance of code.

Outcome

All source code is located at github (https://github.com/Barco-VCT/VirtualClinicalTrials). The installation is straightforward once the source code has been checked out. A CMake file is used for generating a project under Visual Studio. A total of seventeen modules have been released, one for the Virtual Image Capture part, five for the virtual display, two for the virtual observer and the others as conversion functionalities. A tutorial for usage and contribution is provided at https://github.com/Barco-VCT/VirtualClinicalTrials/blob/master/doc/VCT_training_presentation_v.8.5.ppt). The test units are also available (https://github.com/Barco-VCT/VirtualClinicalTrials/tree/master/test_units). These test units are documented in latex, the source files are available and the corresponding pdf file is available on https://github.com/Barco-VCT/VirtualClinicalTrials/blob/master/doc/VCT_simulation.pdf. For simulating the display and converting RGB input pixel data into XYZ data in cd/m² (intensity coming out of the display), there are two implemented models. There is a rather simple one: sRGB with the module name SRgbDisplayModule and there is a more elaborated and very accurate one using a combination of two modules: DisplayModule and Rgb2XYZDisplayModule based on the masking model from Tamura [5]. Both do have a dedicated test unit for which it is possible to apply 3 independent Look-Up-Tables on each channel. A rather elaborate test unit simulates a pre-clinical study and is described below. The goal of this simulation is to run a virtual clinical trial with breast images that were previously used in the study described in [6]. The breast images were generated using Bakic’s anthropomorphic breast phantom developed at the University of Pennsylvania. In this virtual clinical trial breast images with and without signals are used as input. Half of the images contain a signal in the center of the images and half do not. These images are then used for simulating the displayed images represented in the XYZ color space. The simulated display is a grayscale mammographic display with a contrast of 1200:1 and a maximum luminance of 600cd/m². Then the luminance component (Y) is used by a single slice Channelized Hoteling Observer (CHO) with Laguerre-Gauss channels [7, 8]. Finally a Multi-Reader-Multi-Cases study is carried out by training and testing multiple single slice CHO observers using the technique described by Gallas in [9]. This simulation is used for testing and explaining the platform. Since the number of images is rather small, the results are not statistically significant but it is possible and easy to increase the number of images.

Discussion

At this time, this platform includes enough modules to conduct sophisticated VCTs. More modules and features will be added in future.

Conclusion

A VCT platform is available on the internet and can be used by the different research groups over the world as a common backbone platform for testing and validating innovation of the future. The platform can be used for adding new modules for each part of the chain by academic and industrial groups all over the world and has the potential to be used for standardization and also for comparing different medical systems.

This work is supported in part by the US National Institutes of Health (grant 1R01CA154444).

References

1. C. Marchessoux, T. Kimpe, and T. Bert. A Virtual Image Chain for Perceived and Clinical Image Quality of Medical Display. IEEE Journal of Display Technology, VOL 4, N 4, 2008

2. http://medicalimaging.medicine.arizona.edu/research/research-labs/cgri/image-quality-toolbox 3. http://medicalimaging.medicine.arizona.edu/research/research-labs/cgri/gpu-accelerated-data-

reconstruction-fastspect-ii 4. https://www.barco.com/en/Products/Displays-monitors-workstations/Medical-displays/Mammography-

displays/5-MegaPixel-display-system-for-digital-breast-imaging-including-breast-tomosynthesis.aspx 5. N. Tamura, N. Tsumura and Y. Miyake. Masking model for accurate colorimetric characterization of lcd.

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luminance, contrast and temporal compensation on cho detection performance at various browsing speeds and on digital breast tomosynthesis images. IWDM. 2012

7. L. Platisa, B. Goossens, E. Vansteenkiste, A. Badano and W. Philips. Channelized Hotelling Observers for Detection Tasks in Multi-Slice Images, October 2009.

8. K.J. Myers and H.H. Barrett. Addition of a channel mechanism to the ideal-observer model. J. Opt. Soc. Am. A, vol. 4, n12, pp. 2447–2457, 1987

9. B.D. Gallas. One-shot estimate of mrmc variance: Auc. Acad. Radiol., vol. 13, pp. 353–362, 2006 Keywords

Data Sharing, Data Integration, Data Access, Data Publishing, REST