Borislav Gueorguiev Tomov

A transverse oscillation approach for estimation of three-dimensional velocity vectors, Part II: experimental validation

The 3-D transverse oscillation method is investigated by estimating 3-D velocities in an experimental flow-rig system. Measurements of the synthesized transverse oscillating fields are presented as well. The method employs a 2-D transducer; decouples the velocity estimation; and estimates the axial, transverse, and elevation velocity components simultaneously. Data are acquired using a research ultrasound scanner. The velocity measurements are conducted with steady flow in sixteen different directions. For a specific flow direction with [α, β] = [45, 15]°, the mean estimated velocity vector at the center of the vessel is (v_x, v_y, v_z) = (33.8, 34.5, 15.2) ± (4.6, 5.0, 0.6) cm/s where the expected velocity is (34.2, 34.2, 13.0) cm/s. The velocity magnitude is 50.6 ± 5.2 cm/s with a bias of 0.7 cm/s. The flow angles α and β are estimated as 45.6 ± 4.9° and 17.6 ± 1.0°. Subsequently, the precision and accuracy are calculated over the entire velocity profiles. On average for all direction, the relative mean bias of the velocity magnitude is -0.08%. For α and β, the mean bias is -0.2° and -1.5°. The relative standard deviations of the velocity magnitude ranges from 8 to 16%. For the flow angles, the ranges of the mean angular deviations are 5° to 16° and 0.7° and 8°.

Delay generation methods with reduced memory requirements

Modern diagnostic ultrasound beamformers require delay information for each sample along the image lines. In order to avoid storing large amounts of focusing data, delay generation techniques have to be used. In connection with developing a compact beamformer architecture, recursive algorithms were investigated. These included an original design and a technique developed by another research group. A piecewise-linear approximation approach was also investigated. Two imaging setups were targeted -- conventional beamforming with a sampling frequency of 40 MHz and subsample precision of 2 bits, and an oversampled beamformer that performs a sparse sample processing by reconstructing the in-phase and quadrature components of the echo signal for 512 focal points. The algorithms were synthesized for a FPGA device XCV2000E-7, for a phased array image with a depth of 15 cm. Their performance was as follows: (1) For the best parametric approach, the gate count was 2095, the maximum operation speed was 131.9 MHz, the power consumption at 40 MHz was 10.6 mW, and it requires 4 12-bit words for each image line and channel. (2) For the piecewise-linear approximation, the corresponding numbers are 1125 gates, 184.9 MHz, 7.8 mW, and 15 16-bit words.

Safety Assessment of Advanced Imaging Sequences I: Measurements

A method for rapid measurement of intensities (Ispta), mechanical index (MI), and probe surface temperature for any ultrasound scanning sequence is presented. It uses the scanners sampling capability to give an accurate measurement of the whole imaging sequence for all emissions to yield the true distributions. The method is several orders of magnitude faster than approaches using an oscilloscope, and it also facilitates validating the emitted pressure field and the scanners emission sequence software. It has been implemented using the experimental synthetic aperture real-time ultrasound system (SARUS) scanner and the Onda AIMS III intensity measurement system (Onda Corporation, Sunnyvale, CA, USA). Four different sequences have been measured: a fixed focus emission, a duplex sequence containing B-mode and flow emissions, a vector flow sequence with B-mode and flow emissions in 17 directions, and finally a SA duplex flow sequence. A BK8820e (BK Medical, Herlev, Denmark) convex array probe is used for the first three sequences and a BK8670 linear array probe for the SA sequence. The method is shown to give the same intensity values within 0.24% of the AIMS III Soniq 5.0 (Onda Corporation, Sunnyvale, CA, USA) commercial intensity measurement program. The approach can measure and store data for a full imaging sequence in 3.8-8.2 s per spatial position. Based on Ispta, MI, and probe surface temperature, the method gives the ability to determine whether a sequence is within U.S. FDA limits, or alternatively indicate how to scale it to be within limits.

Rapid measurements of intensities for safety assessment of advanced imaging sequences

FDA requires that intensity and safety parameters are measured for all imaging schemes for clinical imaging. This is often cumbersome, since the scan sequence has to broken apart, measurements conducted for the individually emitted beams, and the final intensity levels calculated by combining the intensities from the individual beams. This paper suggests a fast measurement scheme using the multi-line sampling capability of modern scanners and research systems. The hydrophone is connected to one sampling channel in the research system, and the intensity is measured for all imaging lines in one emission sequence. This makes it possible to map out the pressure field and hence intensity level for all imaging lines in a single measurement. The approach has several advantages: the scanner does not have to be re-programmed and can use the scan sequence without modification. The measurements are orders of magnitude faster (minutes rather than hours) and the final intensity level calculation can be made generic and reused for any kind of scan sequence by just knowing the number of imaging lines and the pulse repetition time. The scheme has been implemented on the Acoustic Intensity Measurement System AIMS III (Onda, Sunnyvale, California, USA). The research scanner SARUS is used for the experiments, where one of the channels is used for the hydrophone signal. A 3 MHz BK 8820e (BK Medical, Herlev, Denmark) convex array with 192 elements is used along with an Onda HFL-0400 hydrophone connected to a AH-2010 pre-amplifier (Onda Corporation, Sunnyvale, USA). A single emission sequence is employed for testing and calibrating the approach. The measurements using the AIMS III and SARUS systems after calibration agree within a relative standard deviation of 0.24%. A duplex B-mode and flow sequence is also investigated. The complex intensity map is measured and the time averaged spatial peak intensity is found. A single point measurement takes 3.43 seconds and the whole sequence can be characterized on the acoustical axis in around 6 minutes.

Synthetic Aperture Sequential Beamforming implemented on multi-core platforms

This paper compares several computational approaches to Synthetic Aperture Sequential Beamforming (SASB) targeting consumer level parallel processors such as multi-core CPUs and GPUs. The proposed implementations demonstrate that ultrasound imaging using SASB can be executed in real-time with a significant headroom for post-processing. The CPU implementations are optimized using Single Instruction Multiple Data (SIMD) instruction extensions and multithreading, and the GPU computations are performed using the APIs, OpenCL and OpenGL. The implementations include refocusing (dynamic focusing) of a set of fixed focused scan lines received from a BK Medical UltraView 800 scanner and subsequent image processing for B-mode imaging and rendering to screen. The benchmarking is performed using a clinically evaluated imaging setup consisting of 269 scan lines × 1472 complex samples (1.58 MB per frame, 16 frames per second) on an Intel Core i7 2600 CPU with an AMD HD7850 and a NVIDIA GTX680 GPU. The fastest CPU and GPU implementations use 14% and 1.3% of the real-time budget of 62 ms/frame, respectively. The maximum achieved processing rate is 1265 frames/s.

Real-time synthetic aperture imaging: opportunities and challenges

Synthetic aperture (SA) ultrasound imaging has not been introduced in commercial scanners mainly due to the computational cost associated with the hardware implementation of this imaging modality. SA imaging redefines the term beamformed line. Since the acquired information comes from all points in the region of interest it is possible to beamform the signals along a desired path, thus, improving the estimation of blood flow. The transmission of coded excitations makes it possible to achieve higher contrast and larger penetration depth compared to conventional scanners. This paper presents the development and implementation of the signal processing stages employed in SA imaging: compression of received data acquired using codes, and beamforming. The goal was to implement the system using commercially available field programmable gate arrays. The compression filter operates on frequency modulated pulses with duration of up to 50 mus sampled at 70 MHz. The beamformer can process data from 256 channels at a pulse repetition frequency of 5000 Hz and produces 192 lines of 1024 complex samples in real time. The lines are described by their origin, direction, length and distance between two samples in 3D. This parametric description makes it possible to quickly change the image geometry during scanning, thus enabling adaptive imaging and precise flow estimation. The paper addresses problems such as large bandwidth and computational load and gives the solutions that have been adopted for the implementation.

Preliminary examples of 3D vector flow imaging

This paper presents 3D vector flow images obtained using the 3D Transverse Oscillation (TO) method. The method employs a 2D transducer and estimates the three velocity components simultaneously, which is important for visualizing complex flow patterns. Data are acquired using the experimental ultrasound scanner SARUS on a flow-rig system with steady flow. The vessel of the flow-rig is centered at a depth of 30 mm, and the flow has an expected 2D circular-symmetric parabolic profile with a peak velocity of 1 m/s. Ten frames of 3D vector flow images are acquired in a cross-sectional plane orthogonal to the center axis of the vessel, which coincides with the y-axis and the flow direction. Hence, only out-of-plane motion is expected. This motion cannot be measured by typical commercial scanners employing 1D arrays. Each frame consists of 16 flow lines steered from -15 to 15 degrees in steps of 2 degrees in the ZX-plane. For the center line, 3200 M-mode lines are acquired yielding 100 velocity profiles. At the center of the vessel, the mean and standard deviation of the estimated velocity vectors are (vx, vy, vz) = (-0.026, 95, 1.0)±(8.8, 6.2, 0.84) cm/s compared to the expected (0.0, 96, 0.0) cm/s. Relative to the velocity magnitude this yields standard deviations of (9.1, 6.4, 0.88) %, respectively. Volumetric flow rates were estimated for all ten frames yielding 57.9±2.0 mL/s in comparison with 56.2 mL/s measured by a commercial magnetic flow meter. One frame of the obtained 3D vector flow data is presented and visualized using three alternative approaches. Practically no in-plane motion (vx and vz) is measured, whereas the out-of-plane motion (vy) and the velocity magnitude exhibit the expected 2D circular-symmetric parabolic shape. It shown that the ultrasound method is suitable for real-time data acquisition as opposed to magnetic resonance imaging (MRI). The results demonstrate that the 3D TO method is capable of performing 3D vector flow imaging.

Simulation and efficient measurements of intensities for complex imaging sequences

It is investigated how linear simulation can be used to predict both the magnitude of the intensities as well as the placement of the peak values. An ultrasound sequence is defined through the normal setup routines for the experimental SARUS scanner, and Field II is then used automatically on the sequence to simulate both intensity and mechanical index (MI) according to FDA rules. A 3 MHz BK Medical 8820e convex array transducer is used with the SARUS scanner. An Onda HFL-0400 hydrophone and the Onda AIMS III system measures the pressure field for three imaging schemes: a fixed focus, single emission scheme, a duplex vector flow scheme, and finally a vector flow imaging scheme. The hydrophone is connected to a receive channel in SARUS, which automatically measures the emitted pressure for the complete imaging sequence. MI can be predicted with an accuracy of 16.4 to 38 %. The accuracy for the intensity is from -17.6 to 9.7 %, although the measured fields are highly non-linear (several MPa) and linear simulation is used. Linear simulation can, thus, be used to accurately predict intensity levels for any advanced imaging sequence and is an efficient tool in predicting the energy distribution.

Real-time GPU implementation of transverse oscillation vector velocity flow imaging

Rapid estimation of blood velocity and visualization of complex flow patterns are important for clinical use of diagnostic ultrasound. This paper presents real-time processing for two-dimensional (2-D) vector flow imaging which utilizes an off-the-shelf graphics processing unit (GPU). In this work, Open Computing Language (OpenCL) is used to estimate 2-D vector velocity flow in vivo in the carotid artery. Data are streamed live from a BK Medical 2202 Pro Focus UltraView Scanner to a workstation running a research interface software platform. Processing data from a 50 millisecond frame of a duplex vector flow acquisition takes 2.3 milliseconds seconds on an Advanced Micro Devices Radeon HD 7850 GPU card. The detected velocities are accurate to within the precision limit of the output format of the display routine. Because this tool was developed as a module external to the scanners built-in processing, it enables new opportunities for prototyping novel algorithms, optimizing processing parameters, and accelerating the path from development lab to clinic.

OC095: Synthetic aperture imaging in medical ultrasound

After pituitary suppression, the ovarian vascularization/follicle was lower in PCO-patients. The follicles in polycystic ovaries required much less FSH stimulation to acquire the same level of vascularization than the follicles in normal ovaries. Also hCG induced an increase in the follicular vascularization in both normal and polycystic ovaries. The follicle count correlated with the total vascularized volume in the ovaries throughout the IVF-cycle. Conclusions: Follicles in polycystic ovaries seem to be less vascularized than the follicles in normal ovaries after GnRHtreatment. It is possible that restricted blood supply to the follicles in PCO might be associated with the follicular arrest observed with PCO. We could confirm that follicles in PCO are more sensitive to gonadotrophin stimulation than follicles in normal ovaries.