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1 Reconfigurable FPGAs for USCT N.V. Ruiter, M. Balzer, M. Hübner, Institut für Prozessdatenverarbeitung und Elektronik, FZK Institut für Technik der Informationsverarbeitung, Uni Ka Placeholder for Pictures
2 Ultrasound Computer Tomography Basic idea: Surround object with (unfocussed) transducers in fixed setup Research for nearly 30 years Long term goal: Early breast cancer diagnosis Most frequent cancer for women High death toll Early detection = good survival probability Possible advantages USCT: Higher image quality, volume images Non ionizing for screening Reproducible More than one image modality Transmission Example data recording (in 2D) Breast imaging in fixed setup Reflection 2
3 Specification of Our System Surround object in 3D 3D unfocussed transducers Advantages: Optimal focus in 3D Fast data acquisition possible Challenges: Low Signal to Noise Ratio (SNR) Limited number of transducers (sparse aperture) Very demanding reconstruction 3D USCT 3
4 Currents Systems 3D USCT I Cylindrical aperture 1920 transducers ( pos) 3.5 Mio A-Scans ~ 20 GB 3D USCT II Optimized semi-ellipsoidal aperture Rotation and Translation Powerful DAQ hardware: 20s -2.5 min Proof of concept with static phantoms Imaging of living tissue Visit USCT Lab 4
5 Digital Signal and Image Processing Central problems: low SNR, 20 GB raw data, millions of voxels, changing algos Signal preprocessing Averaging Digital Bandpass Matched filter, e.g. Chirps Current Signal processing Signal detection Detection of transmission peak Log spectrum analysis Envelope, optimal Pulse, Image reconstruction Inverse Radon Transform SAFT SAFT with speed of sound (SOS) Adapted SAFT Inverse Born New Algorithms USCT DAQ System DAQ PC Reconstruction PC(s) Averaging Peak detection R A M R A M R A M SAFT Inv. Radon SOS projection Sig. Processing 5
6 Digital Signal and Image Processing Central problems: low SNR, 20 GB raw data, millions of voxels, changing algos Signal processing Image reconstruction Averaging Digital Bandpass Matched filter, e.g. Chirps Vision USCT DAQ System Signal preprocessing Sig.Pre- Processing Sig.Processing Image recon. Signal detection Detection of transmission peak Log spectrum analysis Envelope, optimal Pulse, R A M Inverse Radon Transformation SAFT SAFT with speed of sound (SOS) Adapted SAFT Inverse Born Reconstruction PC(s) R A M S Image recon. New Algorithms 6
7 USCT Data Acquisition System 19 rack: 1 Second Level DAQ Board 20 First Level DAQ Board Sensor excitation control 480 Analogue receive channels 480 x Digital data processing Raw data rate 14 GB/s Data storage 20 x 2.4 GB Data readout 20 MB/s 7
8 Second Level DAQ Board Measurement control & data readout Processor PMC Module Intel CPU 1GHz, 256 MB SDRAM PCI Master Interface 64/66 2 x Fast Ethernet (100 Mb) 2 x SATA, 4 x USB Linux 2.6 Communication to Host (DAQ PC) FPGA PCI Slave Interface 64/66 Master Backplane Bus Measurement Flow Control Coded excitation for emitters and emitter selection 8
9 First Level DAQ Board Signal Pre Processing Analog Card Variable Gain Amplifier MHz Filtering and Decimation to 10 MHz Averaging (up to 128 A-Scan) Data Storage (2.4 GB on Board) Signal Processing Transmission peak detection Online calculation Speed of Sound 9
10 USCT Data Acquisition System Sensor Electronic amplitude shape 1 x SL DAQ DAC Sys Cntrl FPGA PMC PC Ethernet Send 24 channels 20 x FL DAQ 8xADC FPGA DAQ FPGA CTRL Record RAM RAM RAM RAM 10
11 Next Generation Data Acquisition System Reconfigurable Computing Device: Embedded PC with Fast Communication FPGA GPU Cell Processor N x ADC FPGA RAM Record N x channels 11
12 Conclusion and Discussion Signal- and image processing for our USCT needs significant acceleration to be clinical relevant Parallel processing in data acquisition hardware is possible Main challenges: Comfortable introduction of new algorithms Reuse of resources for image processing ( reconfiguration) Find optimal hardware configuration for tasks 12
13 Acknowledgements Algorithms and Imaging M. Zapf, M. Hardt, and students Grid Computing M. Hardt, R. Stotzka, T. Jejkal, et. al. Sensors G. Göbel, et. al. DAQ and Hardware D. Tcherniakhovski, S. Menshikov, L. Berger, M. Schleicher, M. Ritter et. al. Cooperations FZK, IMF III: W. Bauer, S. Busch, J. Lorenz, et. al. KIT: ITIV, ISAS Uniklinikum Jena: J. Reichenbach, P. Balzer, W. Kaiser, et. al. University Brno, Tschechien: R. Jirik, J. Jan, et. al. University Delft, NL: K. van Dongen, et. al. 13
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