Improved Data Acquisition System for Brain PET Using GAPD Arrays Wei Hu, Yong Choi*, Key Jo Hong, Jihoon Kang, Youn Suk Huh, Hyun Keong Lim, Sang Su Kim, Ji Woong Jung, Kyu Bom Kim, Byung-Tae Kim Abstract– We have previously reported that a brain PET using GAPD arrays was successfully developed. The brain PET consisted of 72 4 x 4 GAPD arrays combined with LYSO crystals (single pixel size: 3 mm x 3 mm). Each 4 GAPD arrays’ output signals were sent to a 64:1 position decoder circuit (PDC) which detects the fastest gamma signal of 64 input channels. To further improve the PET system performance, several modifications were performed on the DAQ system: PET data from 3 DAQ cards were transferred and saved on one SDRAM module by rapid channel communication; parallel processing and multiplexing based FPGA algorithm was developed to detect true PET signals by real time; a more user-friendly GUI DAQ control program was developed to control 3 DAQ cards simultaneously; an accurate and fast coincidence sorting method containing 3 discrimination approaches (time, energy and line of response discriminations) was developed to improve image quality. To evaluate the improved DAQ system, several experiments were performed such as sensitivity measurement using a 25 µci Na-22 point source, spatial resolution measurement using ten F-18 line sources with different source-to-center distances (-8 cm, -6 cm, -4 cm, -2 cm, 0, 2 cm, 4 cm, 6 cm, 8 cm and 10 cm), PET images acquisition of hot rod phantom and Hoffman brain phantom. Experimental results showed that PET sensitivity of 2594 cps/ MBq at 30% energy window (350–650 kev) was achieved. Spatial resolution from 2.9 mm (center) to 5 mm (25 cm off-center) was acquired for ten different source-to-center distances. PET images of hot rod phantom and Hoffman brain phantom were successfully acquired with improved image quality. The DAQ system developed in this study allows to acquiring high quality PET images using GAPD arrays.
Manuscript received November 11, 2010. This study was supported by a grant of the Converging Research Center Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2009-0081935), and by a grant of the Industrial Source Technology Development Programs, the Ministry of Knowledge Economy (10030029), Republic of Korea. Wei Hu, Yong Choi, Key Jo Hong, Jihoon Kang, Yoonsuk Huh, Hyun Keong Lim, Sangsu Kim, Ji Woong Jung and Kyu Bom Kim are with the Department of Electronic Engineering, Sogang University, 1 Shinsu-Dong, Mapo-Gu, Seoul, 121-742, Republic of Korea (telephone: +82-2-705-8910, email:
[email protected],
[email protected]). Byung-Tae Kim is with Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 IlwonDong, Gangnam-Gu, Seoul 135-710, Republic of Korea
I. INTRODUCTION
Abrain PET consisted of 72 4 x 4 GAPD arrays was developed and high quality PET images of various phantoms were successfully acquired [1]. Fig. 1 shows system architecture of the brain PET. To reduce readout channels of the brain PET, a 64:1 position decoder circuit (PDC) was developed to detect the fastest gamma ray signal of 64 input channels from 4 GAPD arrays [2]. One analog output and 6 bits digital position information from each PDC were sent to the data acquisition (DAQ) system. The DAQ system consisted of 3 DAQ cards on 8-channel free-running analog-to-digital converters (ADCs) and fieldprogrammable gate array (FPGA).
Fig. 1. Architecture of the brain PET system: GAPD-LYSO detector, preamp, position decoder circuit, DAQ cards and host computer. Analog coincidence detection units such as constant fraction discriminator (CFD) and time-to-digital converter (TDC) were replaced by FPGA-based digital signal processing method which was more flexible and efficient [3-8]. In this study, to further improve the performance of the brain PET, ameliorations on DAQ system architecture, PET signal acquisition algorithm, DAQ control program and coincidence sorting program were performed. II. MATERIALS AND METHODS A. DAQ system architecture New architecture for DAQ system was designed to save all data packages from 3 DAQ cards to a single SDRAM. Two
rapid channel cables (male and female connectors with Samtec micro-coaxial wires) were used to transfer data among three DAQ cards. As shown in Fig. 2, data packages from DAQ card 1 and DAQ card 2 were transferred to DAQ card 3 through rapid channels and finally multiplexed to save to the SDRAM.
Fig. 2. Rapid channel based data transmission scheme for DAQ system To acquire accurate time information for PET signals from different PDCs, three DAQ cards have to be synchronized. As shown in Fig. 3, a 100 MHz external clock and an external trigger were sent to three multi-channel DAQ cards through splitters to synchronize clock and start time for each multichannel DAQ card. Before each data acquisition, host computer sends counter initialization signals to three multichannel DAQ cards to change all the three counters’ value to 0.
In DAQ card 2 and DAQ card 3, the same PET signal acquisition algorithm as DAQ card 1 was performed. DAQ card 3 finally got all packaged data for 18 PDC and multiplexed them to save to 2 GB SDRAM.
Fig. 4. Parallel processing and multiplexing based PET signal acquisition algorithm The PET signal acquisition algorithm was finally implemented to hardware using Matlab/Simulink block diagram based FPGA code design as shown in Fig. 5.
Fig. 3. Synchronization scheme of three DAQ cards B. Parallel processing and multiplexing based PET signal detection algorithm For PET system, accurate timing, energy and position information for each gamma ray should be acquired. Recently, a simple and improved digital timing method was developed for PET [9]. Based on this digital timing method, FPGA-based PET signal acquisition algorithm was developed as shown in Fig. 4. Firstly, parallel processing consisted of PDC ID generation, coarse arrival time detection, initial rise line detection, pulse energy calculation and PDC channel ID generation was performed for accurate gamma signal acquisition. After parallel processing for each ADC channel, 6 channel packaged data containing pulse arrival time, pulse energy and position were multiplexed in DAQ card 1 and then sent to DAQ card 2 through rapid channel.
Fig. 5. Matlab/Simulink block diagram based FPGA code for PET signal acquisition algorithm. C. Graphical user interface (GUI) DAQ control program A user-friendly GUI DAQ control program was developed for the brain PET as shown in Fig. 6. Three cards can be simultaneously controlled for data acquisition by selecting external clock source. Global energy windows can be defined for all 72 GAPD arrays by setting custom register values. User can define total acquisition counts and maximum 2 GB data can be saved by real time for each acquisition.
Several experiments were performed and PET images were reconstructed by 2-D ordered subset expectation maximization (OSEM) method. A 25 µci Na-22 point source was placed in the center of the PET scanner to measure system sensitivity. Spatial resolution was measured using ten F-18 line sources with different source-to-center distances (-8 cm, -6 cm, -4 cm, -2 cm, 0, 2 cm, 4 cm, 6 cm, 8 cm and 10 cm). PET image of hot rod phantom with activity of 1 mCi was acquired for 15 minutes and PET image of Hoffman brain phantom with activity of 1 mCi was acquired for 30 minutes. III. RESULTS AND DISCUSSIONS A. Sensitivity measurement
Fig. 6. GUI DAQ control program for the brain PET D. Coincidence sorting program In the past, coincidence sorting was performed by Matlab whose sorting rate was very slow. Recently, a C# based coincidence sorting program was developed as shown in Fig. 7. User can define diameter of PET scanner, global or individual energy windows, coincidence timing window and field of view (FOV) in this program. Delayed window based random correction mode can be selected. The final output can contain total coincidence pairs, random coincidence pairs, energy spectra for all 1152 channels and timing spectra for each pair of coincidence GAPD arrays. This program can accurately sort true coincidence pairs by rejecting random and scatter events. The sorting rate was about 1,900,000 single PET events/sec which was 10 times faster than Matlab- based sorting program.
PET sensitivity of 0.26% was acquired when employing a 30% energy window (350 – 650 kev). Comparing to simulation result (0.6%) reported in previous study [1], the acquired sensitivity was reduced because of high threshold setting in the PDC and 1.44 µs delay between PDC and DAQ card. System sensitivity can be improved by using lower threshold in PDC and reducing multiplexing ratio inside FPGA which requires more DAQ cards to be used. B. Spatial resolution measurement Fig. 8 shows acquired images for ten F-18 line sources. PET system spatial resolution of 3.1 mm (center) to 6.6 mm (10 cm off-center) was acquired which was similar to the simulation results of previous study [1].
Fig. 8. PET images of ten F-18 line sources
Fig. 7. Coincidence sorting program E. Performance evaluation of improved DAQ system
C. PET image acquisition of hot rod phantom and Hoffman brain phantom PET images of hot rod phantom and Hoffman brain phantom were successfully acquired as shown in Fig. 9 and Fig. 10. The rods down to a diameter of 3.5 mm can be resolved for hot rod phantom and image quality of Hoffman brain phantom was improved comparing to previous work [10].
Fig. 9. PET image of hot rod phantom
Fig. 10. PET image of Hoffman brain phantom
IV. CONCLUSION Ameliorations on DAQ system architecture, PET signal acquisition algorithm, DAQ control program and coincidence sorting program were successfully performed for brain PET using GAPD arrays. FPGA-based PET signal acquisition algorithm developed in this study was proved to be useful for the development of high performance PET. User friendly DAQ control program and coincidence sorting program were developed to optimize the PET data processing for the brain PET. High quality PET phantom images were successfully acquired which indicates that the DAQ system developed in this study allows acquiring high quality PET images using GAPD arrays.
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