Jensen, Jørgen Arendt

Stuart, Matthias Bo

Matthias Bo Stuart

Jorgen Arendt Jensen

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An architecture and implementation of real-time synthetic aperture compounding with SARUS

Synthetic aperture and compounding are imaging techniques for increasing the resolution and contrast of ultrasound images. Both techniques are computationally intensive, and combined they require approximately two orders of magnitude more lines to be beamformed per second compared to conventional B-mode imaging with similar frame rates. In this paper, an implementation of a system capable of synthetic aperture compound imaging in real-time producing more than 325 million complex beamformed samples per second is presented. This corresponds to synthetic aperture compound imaging at 13 frames per second with 64 emissions and 3 compound angles with 128 lines each. The beamformer is implemented in the SARUS research scanner which consists of 320 Virtex4 FPGAs and has 1024 independent transmit and receive channels. The beamformer is partitioned across 64 FPGAs and runs at 87.5 MHz while consuming 76% of the available logic resources in each FPGA. The beamformed images have resolution similar to offline processed images

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Downloaded from orbit.dtu.dk on: Dec 18, 2017An Architecture and Implementation of Real-time Synthetic Aperture Compoundingwith SARUSStuart, Matthias Bo; Jensen, Jørgen ArendtPublished in:Proceedings of the 2011 IEEE International Ultrasonics SymposiumLink to article, DOI:10.1109/ULTSYM.2011.0256Publication date:2011Document VersionEarly version, also known as pre-printLink back to DTU OrbitCitation (APA):Stuart, M. B., & Jensen, J. A. (2011). An Architecture and Implementation of Real-time Synthetic ApertureCompounding with SARUS. In Proceedings of the 2011 IEEE International Ultrasonics Symposium (pp. 1044-1047). IEEE. DOI: 10.1109/ULTSYM.2011.0256Paper presented at the IEEE International Ultrasonics Symposium, Orlando Florida, 2011:An Architecture and Implementation of Real-time Synthetic ApertureCompounding with SARUSMatthias Bo Stuart and Jørgen Arendt JensenCenter for Fast Ultrasound Imaging,Biomedical Engineering group, Department of Electrical Engineering, Bldg. 349,Technical University of Denmark, DK-2800 Kgs. Lyngby, DenmarkTo be published in Proceedings of IEEE International Ultrasonics Symposium, Orlando Florida, 2011.An Architecture and Implementation of Real-timeSynthetic Aperture Compounding with SARUSMatthias Bo Stuart and Jørgen Arendt JensenCenter for Fast Ultrasound Imaging, Dept. of Elec. Eng. Bldg. 349,Technical University of Denmark, DK-2800 Lyngby, DenmarkAbstract—Synthetic aperture and compounding are imagingtechniques for increasing the resolution and contrast of ultra-sound images. Both techniques are computationally intensive, andcombined they require approximately two orders of magnitudemore lines to be beamformed per second compared to conven-tional B-mode imaging with similar frame rates. In this paper,an implementation of a system capable of synthetic aperturecompound imaging in real-time producing more than 325 millioncomplex beamformed samples per second is presented. Thiscorresponds to synthetic aperture compound imaging at 13frames per second with 64 emissions and 3 compound angleswith 128 lines each. The beamformer is implemented in theSARUS research scanner which consists of 320 Virtex4 FPGAsand has 1024 independent transmit and receive channels. Thebeamformer is partitioned across 64 FPGAs and runs at 87.5MHz while consuming 76% of the available logic resources ineach FPGA. The beamformed images have resolution similar tooffline processed images.I. INTRODUCTIONSynthetic aperture (SA) imaging [1], [2] makes it possibleto synthesize dynamic transmit and receive focusing in manydirections simultaneously. In transmit, a set of transducerelements is emitting the same waveform with individual delaysto create a virtual transmit point source. On each emission,all image lines are beamformed, using different weights perline and per virtual source, creating a low-resolution image.The final high-resolution image is created by adding the low-resolution images for all the virtual sources. The processis illustrated in Fig. fig:sa. The advantages include higherresolution and potentially faster acquisition times, as feweremissions are required compared to conventional focusedultrasound emissions. The main drawback is that the com-putational complexity increases by two orders of magnitude,as each emission results in a full image rather than a singleor a few lines.Compounding can be done in both transmit [3] and inreceive [4]. In transmit, a region is insonified from multipleangles by electronically steering the beam. In receive, a regionis imaged from multiple angles by beamforming lines indifferent directions. Compounding improves speckle-to-noiseratio at a cost of increased computational complexity, as thenumber of lines to beamform grows linearly with the numberof compounding angles.Synthetic aperture compounding (SAC) is the mixture of SAand compounding, where a full set of image lines for eachcompounding angle is beamformed for each virtual source.Compared to conventional compounding, where the region ofinterest needs to be insonified at different beam angles, SACdoes not suffer from frame-rate reduction, as each emission ofultrasound energy results in image lines for each compoundangle. Furthermore, SAC has the advantage of SA imagingallowing dynamic focusing in every point in the image.Assuming a setup with 64 virtual sources, i.e., 64 emissionsto create a high-resolution image, 3 compounding angles with128 image lines each, and 13 frames per second, 319,488 linesneed to be beamformed per second. A conventional compoundimaging system with one line beamformed per emission,128 image lines, 3 angles, and 13 compounded frames persecond, needs 4,992 lines to be beamformed per second.The computational complexity is, thus, roughly two orders ofmagnitude greater than conventional compound imaging.This contribution of this paper is an architecture and imple-mentation of real-time SAC on the synthetic aperture real-timeultrasound system (SARUS) research scanner.The rest of the paper is structured as follows: In Section II,SARUS is presented. Section III presents the architecture ofthe beamformer and compounding, and Section IV describesthe conducted experiments to evaluate the beamformer. SectionV presents the results, while Section VI concludes the paper.II. SARUSSARUS is a research scanner constructed at the TechnicalUniversity of Denmark [5], [6]. The scanner consists of ananalog front-end with 1024 independent transmit and receivechannels and a digital back-end. The back-end consists of 64digital acquisition and ultrasound processing (DAUP) boards.Each DAUP board has 16 independent transmit and receivechannels that are sampled at 12 bits and 70 MHz.Fig. 2 shows a schematic of a DAUP board. Each DAUPboard hosts five field-programmable gate arrays (FPGAs) forcontrol, transmission, filtering, focusing, and summing ofultrasound data. The FPGAs all have external memory forstorage of parameters and ultrasound data. The control FPGAis a Xilinx Virtex4 FX60, while the remaining four FPGAs areXilinx Virtex4 FX100. These FPGAs contain 42,176 slicesof logic, various input/output elements, and 160 high-speedDSP slices capable of running at 400 MHz giving 64 billionoperations per second maximum multiplication rate per FPGA.The receive FPGA on each board receives sampled datafrom the ADCs for 16 channels. From these, sampled data forall 16 channels can be stored in RAM, while 4 channels canbe selected for real-time processing. The selection of channelsfor processing is fully flexible and can be adjusted for eachemission allowing the use of a sliding aperture.The samples from the selected channels are passed througha programmable filter in the frequency domain and the resultis then sent to the focusing FPGA. Here, the samples arebeamformed using parametrized focusing units that producehigh-resolution tiles (a tile is the contribution to a set of linesfrom the receive channels on a given DAUP board [7]). Thesetiles are then sent to the sum FPGA, where they are summedwith tiles from other DAUP boards to form the final high-resolution image.III. IMPLEMENTATIONThe implementation is based on a delay-and-sum beam-former, but partitioned to minimize the amount of data that istransferred between DAUP boards [7]. Given the coordinatesof the ith virtual source, −→p vs,i, the coordinates of the jthtransducer element, −→p rx, j, and the kth point to beamform onthe lth line in the output image, −→p b f ,k,l , the time of flight iscalculated asti, j,k,l =∣∣−→p b f ,k,l−−→p vs,i∣∣+ ∣∣−→p b f ,k,l−−→p rx, j∣∣c(1)where c is the speed of sound.Given a sampled and filtered signal Si, j for the ith virtualsource on channel j, the focusing index m corresponding tothe calculated time of flight ismi, j,k,l = ti, j,k,l fs (2)where fs is the sampling frequency.The output sample Sk,l of a synthetic aperture beamformerwith N input channels and M virtual sources is then the sumFig. 1. Synthetic aperture imaging: Each emission results in a low-resolutionimage all of which are added to form the final high-resolution image.of the individually delayed signalsSk,l =M∑i=1N∑j=1Si, j(mi, j,k,l) (3)The computation of Sk,l can be partitioned in tiles that matchthe number of channels on a DAUP board. Let NDAUP be thenumber of DAUP cards in the system, Nlocal is the number ofchannels on a DAUP card, and jd is the jth local channel onDAUP card d. (3) can then be rewritten asSk,l =NDAUP∑d=1M∑i=1Nlocal∑j=1Si, jd (mi, jd ,k,l) (4)Apodizations (windowing) are typically used in the beam-forming process to lower side-lobes at a cost in resolution. Letwvs,i, j,k,l be the apodization of virtual source i for channel j,output sample k on line l, and wrx, j,k,l be the apodization ofchannel j, output sample k on line l. Adding apodizations tothe expression for the beamformed sample in (4) givesSk,l =NDAUP∑d=1M∑i=1Nlocal∑j=1Si, jd (mi, jd ,k,l)wvs,i, jd ,k,lwrx, jd ,k,l (5)The apodization curves are procedurally generated as a func-tion of the output sample index k from a description of eightline segments each having a start value, a slope, and a length.The formulation of the synthetic aperture beamformer in(5) allows the inner two sums to be calculated locally oneach DAUP board, minimizing the amount of inter-boardcommunication.The implementation of the inner sums of (5) is illustratedin Fig. 3. The beamformer processes four channels per DAUPboard per virtual source. The updating of virtual source andtransducer coordinates is not shown in the illustration. FiveFig. 2. A schematic of the DAUP board illustrating the major componentsand data paths in the system. The non-real-time data paths are used forconfiguration and reading out sampled and processed data.Fig. 3. The architecture of the beamformer.delay units calculate the four focusing indexes, m. One calcu-lates the distance from the virtual source to the point beingbeamformed, while the other four calculate the distances fromthe four transducer elements to the point being beamformed.The integral part of the focusing index is used to index in thesample memory, while the fractional part is used to performa linear interpolation between two neighbouring samples.Each Si, jd (m) is apodized with the receive apodizationwrx, jd ,k,l , the apodized signals are summed across local chan-nels ( jd) and then apodized with the virtual source apodizationwvs,i, j,k,l . The beamformer sequentially produces each outputsample k on line l for virtual source i and stores the interme-diate valueS ′i,k,l =i∑1Nlocal∑j=1Si, jd (mi, jd ,k,l)wvs,i, jd ,k,lwrx, jd ,k,l (6)where Sk,l = S ′M,k,l .The beamformer is pipelined and has a throughput close toone output sample per clock cycle – it takes as many clockcycles as there are pipeline stages to fill the pipeline. Whenthe pipeline is full, one pixel is output per clock cycle. It takes1,075 clock cycles to beamform a line with 1,024 complexoutput samples.Due to constraints on the number of computational andmemory resources in an FPGA, the beamformer producestiles of eight lines at a time. This gives a good trade-offbetween the utilization of on-chip resources and bandwidthusage to external memory. Multiple passes are made over thebeamforming algorithm to produce multiple tiles.Compounding is performed by beamforming multiple setsof lines at different angular offsets. Fig. 4 illustrates theprinciple in SARUS, when the beamformer is programmedwith 384 lines. Lines 1 to 128 are beamformed at an angle of+30 degrees, lines 129 to 256 at an angle of 0 degrees, andlines 257 to 384 at an angle of -30 degrees.Each sub-image consisting of all lines at a given angle issent to a terminal over a 1 Gbit network connection. Thesub-images are then envelope detected and scanline converted.The scanline converted sub-images are added to form the finalhigh-resolution compounded image, which is logarithmicallycompressed for display.Fig. 4. 384 lines are beamformed. Lines 1 to 128 at an angle of +30 degrees,lines 129 to 256 at 0 degrees, and lines 257 to 384 at -30 degrees.IV. EXPERIMENTAL EVALUATIONThe architecture described in the previous section is de-scribed in VHDL with supporting peripherals (such as memorycontrollers and multi gigabit transceivers) generated usingXilinx Core Generator. The code is then implemented in aXilinx xc4vfx100-10ff1152 FPGA.Simulated ultrasound data generated with Field II [8], [9]is then beamformed on a system consisting of four DAUPboards. A reference image from the same data is made withthe BFT3 beamformation toolbox [10]. The simulation usesa 64 element λ/2 pitch linear array transducer with a centerfrequency of 3.5 MHz and an elevation focus at 7 cm. Thesimulated phantom consists of 3 wires at depths of 30, 35, and40 mm and centered relative to the transducer.Lateral distance x [mm]Depth z [mm]  −10 −8 −6 −4 −2 0 2 4 6 8 10303234363840−60−50−40−30−20−100Lateral distance x [mm]Depth z [mm]  −10 −8 −6 −4 −2 0 2 4 6 8 10303234363840−60−50−40−30−20−100Fig. 5. Top: Reference image beamformed using BFT3. Bottom: Imagebeamformed with SARUS.V. RESULTSThe fully implemented beamformer with all peripheralsoccupies 76% of the logic slices, 64% of the block RAMs,and 60% of the XtremeDSP slices in this FPGA.The design has been implemented at a core clock frequencyof 87.5 MHz. At this frequency, it takes approximately 25 µsto beamform a set of eight lines for a single virtual source. In asetup with 64 virtual sources and 384 lines (48 sets of 8 lines),it thus takes 77 ms to beamform a full image giving a frame-rate of 13 frames per second or 320,000 lines per second or325 million beamformed samples per second per channel. Fora full system beamforming 256 channels per emission, thetotal output is 83,2 billion beamformed samples per second.Fig. 5 shows the reference image and the image beamformedwith SARUS. Both images show similar resolution (FWHM)of 1.2λ axially, and 1.5λ, 1.7λ, and 1.9λ laterally for the threewires respectively.VI. CONCLUSIONThis paper has presented an architecture and implementationfor making real-time synthetic aperture compound images.The beamformer is implemented at a clock frequency of87.5 MHz and takes up 76% of the logic resources in a XilinxVirtex4 FPGA. At this clock frequency, it produces 320,000beamformed lines per second.ACKNOWLEDGEMENTThis work was supported by grant 26-04-0024 from theDanish Science Foundation and by B-K Medical Aps. Thisproject is also supported by grant 024-2008-3 from the DanishAdvanced Technology Foundation.REFERENCES[1] M. Soumekh, Synthetic aperture radar. Signal processing with MATLABalgorithms. New York: John Wiley & Sons, Inc., 1999.[2] J. J. Flaherty, K. R. Erikson, and V. M. Lund, “Synthetic apertureultrasound imaging systems,” United States Patent, US 3,548,642, 1967,Published 22 Dec 1970.[3] D. Shattuck and O. von Ramm, “Compounding scanning with a phasedarray,” Ultrason. Imaging, vol. 4, pp. 93–107, 1982.[4] S. K. Jespersen, J. E. Wilhjelm, and H. Sillesen, “Multi-angle compoundimaging,” Ultrason. Imaging, vol. 20, pp. 81–102, 1998.[5] J. A. Jensen, M. Hansen, B. G. Tomov, S. I. Nikolov, and H. Holten-Lund, “System architecture of an experimental synthetic aperture realtime ultrasound system,” in Proc. IEEE Ultrason. Symp., Oct. 2007, pp.636–640.[6] J. A. Jensen, H. Holten-Lund, R. T. Nielson, B. G. Tomov, M. B. Stuart,S. I. Nikolov, M. Hansen, and U. D. Larsen, “Performance of SARUS:A Synthetic Aperture Real-time Ultrasound System,” in Proc. IEEEUltrason. Symp., Oct. 2010, pp. 305–309.[7] S. Nikolov, J. A. Jensen, and B. G. Tomov, “Fast parametric beamformerfor synthetic aperture imaging,” IEEE Trans. Ultrason., Ferroelec., Freq.Contr., vol. 55, no. 8, pp. 1755–1767, 2008.[8] J. A. Jensen, “Field: A Program for Simulating Ultrasound Systems,”Med. Biol. Eng. Comp., vol. 10th Nordic-Baltic Conference on Biomed-ical Imaging, Vol. 4, Supplement 1, Part 1, pp. 351–353, 1996.[9] J. A. Jensen and N. B. Svendsen, “Calculation of Pressure Fields fromArbitrarily Shaped, Apodized, and Excited Ultrasound Transducers,”IEEE Trans. Ultrason., Ferroelec., Freq. Contr., vol. 39, pp. 262–267,1992.[10] J. M. Hansen, M. C. Hemmsen, and J. A. Jensen, “An object-orientedmulti-threaded software beam formation toolbox,” in Proc. SPIE -Medical Imaging - Ultrasonic Imaging and Signal Processsing, vol.7968, 2011, p. 79680Y.

An Architecture and Implementation of Real-time Synthetic Aperture Compounding with SARUS

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An Architecture and Implementation of Real-time Synthetic Aperture Compounding with SARUS

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