Total Body PET

1. High Performance Positron Emission Tomography (PET) Detectors for Imaging and Classification in many areas May 1, 2019 Kétévi A. Assamagan, Physics Department Micro-PET (exploration) PEPT for mineral extraction from powders MinPET Total Body PET MADPET-II https://explorer.ucdavis.edu Leverage technologies related to : Detectors, Tomographic Algorithms, High throughput electronics, High Performance Computing, Big Data, AI, Simulation PET / TXRT for containers Nuclear reactor Ubuntu modeling Medical PET Video SPECT for Landmines Westminster international CSUMechatronics https://www.youtube.com/watch?v=oySvkmezdo0

2. NPP - FY 2020 Draft LDRD Presentation Proposal Title: High Performance Positron Emission Tomography Detectors for Imaging and Classification in many areas Principal Investigator: Kétévi A. Assamagan (BNL) Department/Division:Physics Other Investigators: Prof. Simon H. Connell (University of Johannesburg) as Visiting Scientist at BNL Proposal Term: From - October 2019 To – September2022

3. NPP – FY 2020 Draft LDRD Presentation • Project Description: Develop the detection, imaging and classification systems for highly granular, fast gamma detectors in a variety of scenarios. These include Medical, Mining and Homeland Security applications (Total body PET, PET Video, Container scanning, Landmine detection, diamond in rock discovery by MinPET). • Expected Results: Innovation and technology transfer from HEP to competitive industry in the fields of granular sensors, high throughput electronics, big data, quantitative imaging, high performance computing, artificial intelligence. Training and technological development that brings HEP to the Fourth Industrial Revolution. For Total Body PET e.g., we expect: • 40 x less dose,  increased sensitivity;  • While body functions; • Wider range of diagnostic functional studies for a wider range of pathologies;  • To disrupt the field of Nuclear Medicine.

4. NPP – FY 2020 Draft LDRD Presentation MinPET concept with Activation, Detection, Imaging and Classification stages for the search of diamondiferous rocks amongst barren kimberlite in an on-line run-of-mine scenario • Our proposal is to develop an intelligent, all-machine, • sensor-based-sorting technology for the Detection, • Imaging and Classification stages applicable in • many areas: • MinPET • Medical PET for Total Body Scanning • Medical PET Video • Land Mine Searches • Based on our knowledge and experience with the • ATLAS—We aim to develop a cheap and high • performance PET detectors, and fast high throughput • data processing systems. PET data to be reconstructed to 3D PET images, which are processed by intelligent classifiers

5. NPP FY 2020 LDRD Draft Presentation • Program:Multiprogram : HEP and NP applied to Innovation in Industry • Return on Investment – Aim to attract significant Venture Capital within a few years. Commercialization. • But need support to demonstrate a successful prototype • Is the proposed work strategic for BNL and needed for creating or maintaining a program or as exploratory research? • The proposed work is strategic for BNL and needed for creating or maintaining a program to beneficiate the capacity from basic research • Potential for BNL’s impact in practical applications based on demonstrated significant improvements over what is currently available. • Total Effort (FTE): 1.50 • Scientific:0.25 • Post-doc:1 • Summer student: 0.25

6. NPP FY 2020 LDRD Draft Presentation – Funding Requirements. • Fiscal Year FY 2020 FY 2021 FY 2022 • Total Funds $332,000 $209,000 $213,000 • Labor $84,259 $86,922 $89,390 • MST $164,060 $50,105 $50,151 • Dept. O/H $11,046 $11,395 $11,719 • Lab G&A $60,765$56,958 $58,120 • Mat. Handling $11,870 $3,620 $3,620 Conclusions: • Re-use our knowledge in major experiments like ATLAS to make significant improvements in PET tomography for many applications; • Potential for commercialization; • Project fits in the BNL-South Africa Consortium for joint research collaborations, capacity development and technology transfers; • We do have the knowledge and experience to carry this project through successfully.

7. Addition Info – If necessary

8. Detector System (I) Two Modules of 20 x 20 cm2 each to be used to refine image reconstruction and classification MPU consists of a matrix of 4 x 4 scintillators each segmented into an array of smaller crystals. MPUs organized to form large arrays of any size. MPU uses a fast ethernet line for data transfer and a similar interconnection to an external Trigger unit network. Programmable coincidence pattern with any of the other connected MPUs. Data segmentation and the synchronization of the clocks and time stamps. MPU housed in a ruggedized metallic box with high quality sealed interconnects. The detectors, with fully independent pixels and electronics, would enable more sophisticated parameter-scan studies in terms of the detector set-up and system configuration. The new detectors would also enable more sophisticated benchmarking and hence improvement of our Geant4 model of the full system. These studies would lead to higher quality AI training data generation. We would therefore also be able to do further R&D studies on the classification stage

9. Detector System (II) • Detectors are optimized for the following • Large area, very high activity of the source distribution, high signal rate and large data volume; • Good spatial resolution and time resolution and excellent independent granularity in the electronic chain, with a high dynamic range and low noise; • Special attention is paid to triggering and event building; • They do not require active cooling; • They can be upgraded as technology develops; • Electronic simplicity and modularity, enabling trivial system scaling and ease of maintenance • Optimized for low cost. • To achieve these goals • dedicated electronics developed with the most advanced devices available on the market; • The detector is organized in modular units—the MPU Earlier version of Detector with H8500 Hamamatsu position sensitive photomultiplier tubes The scintillator is segmented BGO with 8x8 crystallite arrays with a pitch of 6.08 mm. Electronics layers for each PMT. FPGA marshalling events from the PMT, representing the principle of at-sensor intelligence. FPGA and Linux PC are on a backplane incorporated into the MPU, which supervises 4x4 PMT units in a single module.

10. Detector (III) Geant4 simulated data after PET image reconstruction for a 100mm rock containing a 5mm diamond at ~1 million LoR. • The backplane of the detector • handles the timing, synchronisation, receives the trigger and builds and publishes the half-events; • Networked computers assemble the LoRs from the data • Lines of Response (LoR) similar to Region of Interest (RoI) in track reconstruction in ATLAS; • LoR can be processed into many PET images similar to the reconstruction of tracks from many RoIs, and Raods • The PET images pass through another layer of parallelization where classifiers are applied for final identification of: • Diamond in rocks • Medical diagnostics • Etc. • The classification is performed by a convolutional neural network • Large data Volume • Total Body PET: expect 16 GB / s • The full MinPET detector system comprises of dual opposing planes of detectors, each with an area of 2 m2. A daunting data processing task due to rate and volume considerations • Uses the concept of distributed analysis—as in ATLAS—for parallel and fast processing AI classification of several diamond sizes based on simulated data

11. Detector (IV) • Particular interest in the image reconstruction • Use its own preliminary PET reconstructed data to segment the PET images • The segmentation would be based on well selected categories applicable only to the MinPET scenario • In the case of medical PET, one could be assisted by 3D material information provided by NMR or a CAT scan. This is not possible for MinPET • Objective • Implement corrections for attenuation and scattering in the PET reconstruction

12. Prior Activation Necessary • In MinPET, the source activity distribution is created via a prior activation stage where the PET isotopes are produced by photon induced reactions and contrasts manifest as given by the isotopic composition of the rock • In Medical PET, the injected PET activity carried by specific bio-molecules targets specific organs as given by the metabolisms of the bio-system. • For homeland security applications, there must also be an activation stage.

13. Homeland Security The application to find buried landmines is based on creating PET isotopes within the buried landmine in a similar way to MinPET and then imaging the distribution of signature elements and discriminating these from false positives using both imaging and elemental analysis.