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Towards 3D gamma-ray imaging Enzo PARADISO EDUSAFE Final Review 20 June 2016
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Overview Gamma camera prototype recap Panoramic gamma Imaging
Gamma sensor calibration 3-D Gamma imaging Overview June 2016 – p. 2
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EduPIX gamma camera Panoramic Gamma Imaging capabilities Embedded
Cross-Platform oriented Scalable, loose coupling Overview June 2016 – p. 3
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Why an Embedded system? Integration of proof of concept and the gamma camera prototype in a stand-alone embedded system, to increase reliability, scalability, improve performances, and reduce size Why an embedded system – 20 June – p. 4 Odroid-XU3 NVIDIA Jetson TK1 Samsung Exynos5422 Cortex™-A15 2Ghz and Cortex™-A7 Octa core CPUs Mali-T628 MP6(OpenGL ES 3.0/2.0/1.1 and OpenCL 1.1 Full profile) 2Gbyte LPDDR3 RAM PoP stacked eMMC5.0 HS400 Flash Storage 2 x USB 3.0 Host, 1 x USB 2.0 Host Gigabit Ethernet port HDMI 1.4a for display Size : 82 x 58 x 22 mm Tegra K1 SOC NVIDIA Kepler GPU with 192 CUDA cores NVIDIA 4-Plus-1 quad-core ARM Cortex-A15 CPU 2 GB memory RAM 16 GB eMMC, SATA, SD/MMC USB 3.0 & Micro USB-USB, RS232 serial port HDMI 1.4 MiniPCI, GPIOs, and high-bandwidth camera interface
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The Chosen One The ODROID-C2
The most robust board among the boards tested Efficient Heat sink -> Low temperature -> No cooling fan Adequate Performance combined with small size Amlogic ARM® Cortex®-A53(ARMv8) 2Ghz quad core CPUs Mali™-450 GPU (3 Pixel-processors + 2 Vertex shader processors) 2Gbyte DDR3 SDRAM Gigabit Ethernet 40pin GPIOs + 7pin I2S eMMC5.0 HS400 Flash Storage slot / UHS-1 SDR50 MicroSD Card slot USB 2.0 Host x 4, USB OTG x 1 (power + data capable) Infrared(IR) Receiver The One - 20 June – p. 5
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Cross Platform – EduPIX Front-end
HTML 5 JavaScript WebGL/CSS 3D To display panoramic and 3D images directly via the browser, without the need for any plugins Cross platform – EduPIX Front end - 20 June – p. 6
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Cross Platform – EduPIX Back-end
Python Tornado web server Scipy PIL Numpy C++ OpenCV ZeroMQ Distributed Messaging System Scalability Suitable for remote control and networking Loose coupling Efficiency Linux operating system (obviously…) Cross platform – EduPIX Back End - 20 June 2016 – p. 7
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Outline Gamma camera prototype recap Panoramic gamma Imaging
Gamma sensor registration 3-D Gamma imaging Outline - 20 June 2016 – p. 8
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Panoramic Gamma Imaging - Visible Processing
Acquisition Wave correction Retrieve best frame aut. Blend images Registration data Panoramic Gamma Imaging - Visible Processing overview - 20 June – p. 9 Optimize selected image Final Pano Warp images Find features Match features
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Panoramic Gamma Imaging - Testing at ATLAS
Performing measurements with the gamma camera prototype at 2 meters from the ATLAS beam pipe Estimated dose at camera location between 3 and 4 µSv/h, depending on the position and the orientation of the gamma camera Panoramic gamma imaging – Testing - 20 June 2016 – p. 10
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Panoramic Gamma Imaging - Testing at ATLAS
Gamma Panorama Image produced by the EduPIX gamma camera prototype projected in Spherical Mode (or Equirectangular). For illustration purposes, the image is shown in black and white in a 360°x180° format with a width/height ratio of 360/180=2 Panoramic gamma imaging – Results - 20 June – p. 11
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Outline Gamma camera prototype recap Panoramic gamma Imaging
Gamma sensor calibration 3-D Gamma imaging Outline - 20 June 2016 – p. 12
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Key Idea: Coded aperture imaging uses many pinholes
What is the relationships between pixel coordinates in the gamma image and the corresponding camera coordinates? What are the position and orientation of the gamma camera relatively to a world co-ordinate system? Key Idea - 20 June 2016 – p. 13
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Key Idea: Coded aperture imaging uses many pinholes
What is the relationships between pixel coordinates in the gamma image and the corresponding camera coordinates? What are the position and orientation of the gamma camera relatively to a world co-ordinate system? We can calibrate the camera by analyzing the position between image points and the position of corresponding radioactive sources in the world. That is, we compute intrinsic and extrinsic parameters of the gamma camera Gamma camera calibration - 20 June 2016 – p. 14 To calibrate a camera means to be able to relate its image points with the homogeneous world coordinates
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Radioactive source distance retrieval
Once calibrated, we can combine the gamma camera with a depth sensor (multi-sensors calibration) in order to retrieve the distance of a source Multi-camera calibration - 20 June 2016 – p. 15
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Depth retrieval techniques overview
Several techniques to retrieve distance of points in a scene Advantages and disadvantages for each method Outline distance sensors - 20 June 2016 – p. 16
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Depth sensor chosen for experiments
Asus Xtion Pro Compact and light Does not require power supply except USB Depth map size (640 px x 480 px) FOV: 58° H, 45° V, 70° D Depth range: up to 5 meters Accuracy of measurements: inversely proportional to distance, from 1 mm to 5 cm The Asus Xtion Pro - 20 June 2016 – p. 17
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Proposed Calibration Technique - Results
Examples and proof-of-concept - 20 June 2016 – p. 18 Mean reprojection Error per each gamma image, calculated using MATLAB and resulting from the proposed calibration technique
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Proposed Calibration Technique - Results
Examples and proof-of-concept - 20 June 2016 – p. 19
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Stereo Gamma Imaging Prototype
Examples and proof-of-concept - 20 June 2016 – p. 20
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Proposed Calibration Technique - Results
Examples and proof-of-concept - 20 June 2016 – p. 21 Distance automatically retrieved of an 241Am source with 360 MBq estimated activity.
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Proposed Calibration Technique - Results
Examples and proof-of-concept - 20 June 2016 – p. 22 Distance automatically retrieved of an 137Cs source with 263 MBq estimated activity.
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Outline Gamma camera prototype recap Panoramic gamma Imaging
Gamma sensor calibration 3-D Gamma imaging Outline - 20 June 2016 – p. 23
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Challenges Selection, testing and integration of appropriate methodology for data range estimation Embedded / power efficient Rapid Acceptable tolerance Retrieve distance automatically Compute 3D position of radioactive sources Design and development of appropriate software /hardware architecture solution for the prototype 3D gamma reconstruction: Reconstruction of 3D model of the physical scene Tracking in a continuous manner the 6 DoF pose of the sensor (location and orientation) Volumetric integration of visible and gamma information Challenges – 20 June 2016 – p. 24
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3D Gamma Imaging Algorithm - Main phases - 1
3D scan of the environment generating a point cloud of the geometric entities within the scene Localize and process radioactive hotspots in 2D using coded-aperture imaging techniques from one or more different perspectives 3D Gamma Imaging - Main phases – 20 June – p. 25 Example of 3-D point cloud data visualization – Laboratory in Canberra Montigny-le-Bretonneux
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3D Gamma Imaging Algorithm - Main phases - 2
6 DoF pose tracking of the camera of the sensor (location and orientation) Alignment (in this phase the gamma camera location and orientation are estimated) Occluding material/object 3D Gamma Imaging - Main phases – 20 June 2016 – p. 26 Measurement from Pose1 Occluded radioactive material/object
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3D Gamma Imaging Algorithm - Main phases - 3
Tracking of the 6 DoF pose of the sensor (location and orientation) Alignment (in this phase the gamma camera location and orientation are estimated) Occluding material/object 3D Gamma Imaging - Main phases – 20 June 2016 – p. 27 Measurement from Pose2 Measurement from Pose1 Occluded radioactive material/object
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3D Gamma Imaging Algorithm - Main phases - 4
Add depth information to the hotspot profile Voxelized model 3D Gamma Imaging - Main phases – 20 June 2016 – p. 28 Dynamic voxelization for 3D imaging
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3D Gamma Imaging Algorithm - Main phases - 5
Volumetric integration: the 3D volume is dynamically merged with the textured 3D mesh and the information of the radioactive source, if any Visualization Constrain the 3D gamma-ray image space to the surfaces of physical objects. 3D Gamma Imaging - Main phases – 20 June 2016 – p. 29
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Thank you!
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