Shape Estimation Algorithm for Ultrasound Imaging by Flexible Array Transducer

Ultrasound imaging with flexible transducers based on real-time and high-accuracy shape estimation

Ultrasound imaging with flexible transducers requires the knowledge of shape geometry for effective beamforming, which such geometry is variable and often unknown. The conventional iteration-based shape estimation methods estimate transducer shape with high computational expense. Although deep-learning-based methods are introduced to reduce computation time, their low shape estimation accuracy limits the practical applications. In this paper, we propose a novel deep-learning-based approach, called FlexSANet, for shape estimation in ultrasound imaging with flexible transducers, which rapidly achieves precise shape estimation and then reconstructs high-quality images. First, in-phase/quadrature (I/Q) data are demodulated from raw radio frequency (RF) data to provide comprehensive guidance for the estimation task. A sparse processing mechanism is employed to extract crucial channel signals, resulting in sparse I/Q data and reducing the estimation time. Then, a spatial-aware shape estimation network establishes a one-shot mapping between the sparse I/Q data and the flexible probe shape. Finally, the ultrasound image is reconstructed using the delay-and-sum (DAS) beamformer with estimated shape. Massive comparisons on simulation datasets and in vivo datasets demonstrate the superiority of the proposed shape estimation method in rapidly and accurately estimating the transducer shape, leading to real-time and high-quality imaging. The mean absolute error of element position in shape estimation is below 1/8 wavelengths for simulation and in vivo experiments, indicating minimal element position error. The structural similarity between the ultrasound images reconstructed with real and estimated shapes is above 0.84 for simulation experiments and 0.80 for in vivo experiments, demonstrating superior image quality. More significantly, its estimation time on CPU of only 0.12 s promises clinical application potential of flexible ultrasound transducers.

Optimization of array encoding for ultrasound imaging

Objective. The transmit encoding model for synthetic aperture imaging is a robust and flexible framework for understanding the effects of acoustic transmission on ultrasound image reconstruction. Our objective is to use machine learning (ML) to construct scanning sequences, parameterized by time delays and apodization weights, that produce high-quality B-mode images.Approach. We use a custom ML model in PyTorch with simulated RF data from Field II to probe the space of possible encoding sequences for those that minimize a loss function that describes image quality. This approach is made computationally feasible by a novel formulation of the derivative for delay-and-sum beamforming.Main results. When trained for a specified experimental setting (imaging domain, hardware restrictions, etc), our ML model produces optimized encoding sequences that, when deployed in the REFoCUS imaging framework, improve a number of standard quality metrics over conventional sequences including resolution, field of view, and contrast. We demonstrate these results experimentally on both wire targets and a tissue-mimicking phantom.Significance. This work demonstrates that the set of commonly used encoding schemes represent only a narrow subset of those available. Additionally, it demonstrates the value for ML tasks in synthetic transmit aperture imaging to consider the beamformer within the model, instead of purely as a post-processing step.

FLEX: FLexible Transducer With External Tracking for Ultrasound Imaging With Patient-Specific Geometry Estimation

Flexible array transducers can adapt to patient-specific geometries during real-time ultrasound (US) image-guided therapy monitoring. This makes the system radiation-free and less user-dependency. Precise estimation of the flexible transducer's geometry is crucial for the delay-and-sum (DAS) beamforming algorithm to reconstruct B-mode US images. The primary innovation of this research is to build a system named FLexible transducer with EXternal tracking (FLEX) to estimate the position of each element of the flexible transducer and reconstruct precise US images. FLEX utilizes customized optical markers and a tracker to monitor the probe's geometry, employing a polygon fitting algorithm to estimate the position and azimuth angle of each transducer element. Subsequently, the traditional DAS algorithm processes the delay estimation from the tracked element position, reconstructing US images from radio-frequency (RF) channel data. The proposed method underwent evaluation on phantoms and cadaveric specimens, demonstrating its clinical feasibility. Deviations in tracked probe geometry compared to ground truth were minimal, measuring 0.50 ± 0.29 mm for the CIRS phantom, 0.54 ± 0.35 mm for the deformable phantom, and 0.36 ± 0.24 mm on the cadaveric specimen. Reconstructing the US image using tracked probe geometry significantly outperformed the untracked geometry, as indicated by a Dice score of 95.1 ± 3.3% versus 62.3 ± 9.2% for the CIRS phantom. The proposed method achieved high accuracy (<0.5 mm error) in tracking the element position for various random curvatures applicable for clinical deployment. The evaluation results show that the radiation-free proposed method can effectively reconstruct US images and assist in monitoring image-guided therapy with minimal user dependency.

Enhancing Image-Guided Radiation Therapy for Pancreatic Cancer: Utilizing Aligned Peak Response Beamforming in Flexible Array Transducers

To develop ultrasound-guided radiotherapy, we proposed an assistant structure with embedded markers along with a novel alternative method, the Aligned Peak Response (APR) method, to alter the conventional delay-and-sum (DAS) beamformer for reconstructing ultrasound images obtained from a flexible array. We simulated imaging targets in Field-II using point target phantoms with point targets at different locations. In the experimental phantom ultrasound images, image RF data were acquired with a flexible transducer with in-house assistant structures embedded with needle targets for testing the accuracy of the APR method. The lateral full width at half maximum (FWHM) values of the objective point target (OPT) in ground truth ultrasound images, APR-delayed ultrasound images with a flat shape, and images acquired with curved transducer radii of 500 mm and 700 mm were 3.96 mm, 4.95 mm, 4.96 mm, and 4.95 mm. The corresponding axial FWHM values were 1.52 mm, 4.08 mm, 5.84 mm, and 5.92 mm, respectively. These results demonstrate that the proposed assistant structure and the APR method have the potential to construct accurate delay curves without external shape sensing, thereby enabling a flexible ultrasound array for tracking pancreatic tumor targets in real time for radiotherapy.

Shape estimation of flexible ultrasound arrays using spatial coherence: A preliminary study

A flexible ultrasound array can potentially provide a larger field-of-view, enhanced imaging resolution, and less operator dependency compared to conventional rigid transducer arrays. However, such transducer arrays require information about relative element positions for beamforming and reconstructing geometrically accurate sonograms. In this study, we assess the potential utility of using spatial coherence of backscattered radiofrequency data to estimate transducer array shape (inverse problem). The methodology is evaluated through 1) simulation of flexible arrays and 2) blinded in vivo experiments using commercial rigid transducer arrays on various anatomical targets (shoulder, forearm, scapular, posterior calf muscles, and abdomen) and multi-purpose ultrasound phantoms. The average Euclidean error of shape estimation is below 0.1 wavelengths for simulated arrays and below 1.4 wavelengths (median: 0.58 wavelengths) for real arrays. The complex wavelet structural similarity index between the B-mode images reconstructed with estimated and ground truth array shapes is above 99 % and 96 %, for simulations and experiments, respectively. These findings suggest that optimizing for spatial coherence may be an effective way to estimate the unknown shape of conformal ultrasound arrays.

Flexible Ultrasound Transducer With Embedded Optical Shape Sensing Fiber for Biomedical Imaging Applications

Flexible ultrasound transducers (FUTs), capable of conforming to irregular surfaces, have become a research hotspot in the field of medical imaging. With these transducers, high-quality ultrasound images can be obtained only if strict design criteria are fulfilled. Moreover, the relative positions of array elements must be determined, which are important for ultrasound beamforming and image reconstruction. These two major characteristics present great challenges to the design and fabrication of FUTs compared to that for traditional rigid probes. In this study, an optical shape-sensing fiber was embedded into a 128-element flexible linear array transducer to acquire the real-time relative positions of array elements to produce high-quality ultrasound images. Minimum concave and convex bend diameters of approximately 20 and 25 mm, respectively, were achieved. The transducer was flexed 2000 times, and yet no obvious damage was observed. Stable electrical and acoustic responses confirmed its mechanical integrity. The developed FUT exhibited an average center frequency of 6.35 MHz, and average -6-dB bandwidth of 69.2%. The array profile and element positions measured by the optic shape-sensing system were instantly transferred to the imaging system. Phantom experiments for both spatial resolution and contrast-to-noise ratio proved that FUTs can maintain satisfactory imaging capability despite bending to sophisticated geometries. Finally, color Doppler images and Doppler spectra of the peripheral arteries of healthy volunteers were obtained in real time.

Flexible array transducer for photoacoustic-guided interventions: phantom and ex vivo demonstrations

Photoacoustic imaging has demonstrated recent promise for surgical guidance, enabling visualization of tool tips during surgical and non-surgical interventions. To receive photoacoustic signals, most conventional transducers are rigid, while a flexible array is able to deform and provide complete contact on surfaces with different geometries. In this work, we present photoacoustic images acquired with a flexible array transducer in multiple concave shapes in phantom and ex vivo bovine liver experiments targeted toward interventional photoacoustic applications. We validate our image reconstruction equations for known sensor geometries with simulated data, and we provide empirical elevation field-of-view, target position, and image quality measurements. The elevation field-of-view was 6.08 mm at a depth of 4 cm and greater than 13 mm at a depth of 5 cm. The target depth agreement with ground truth ranged 98.35-99.69%. The mean lateral and axial target sizes when imaging 600 μm-core-diameter optical fibers inserted within the phantoms ranged 0.98-2.14 mm and 1.61-2.24 mm, respectively. The mean ± one standard deviation of lateral and axial target sizes when surrounded by liver tissue were 1.80±0.48 mm and 2.17±0.24 mm, respectively. Contrast, signal-to-noise, and generalized contrast-to-noise ratios ranged 6.92-24.42 dB, 46.50-67.51 dB, and 0.76-1, respectively, within the elevational field-of-view. Results establish the feasibility of implementing photoacoustic-guided surgery with a flexible array transducer.

Ultrasound Imaging with Flexible Array Transducer for Pancreatic Cancer Radiation Therapy

Pancreatic cancer with less than 10% 3-year survival rate is one of deadliest cancer types and greatly benefits from enhanced radiotherapy. Organ motion monitoring helps spare the normal tissue from high radiation and, in turn, enables the dose escalation to the target that has been shown to improve the effectiveness of RT by doubling and tripling post-RT survival rate. The flexible array transducer is a novel and promising solution to address the limitation of conventional US probes. We proposed a novel shape estimation for flexible array transducer using two sequential algorithms: (i) an optical tracking-based system that uses the optical markers coordinates attached to the probe at specific positions to estimate the array shape in real-time and (ii) a fully automatic shape optimization algorithm that automatically searches for the optimal array shape that results in the highest quality reconstructed image. We conducted phantom and in vivo experiments to evaluate the estimated array shapes and the accuracy of reconstructed US images. The proposed method reconstructed US images with low full-width-at-half-maximum (FWHM) of the point scatters, correct aspect ratio of the cyst, and high-matching score with the ground truth. Our results demonstrated that the proposed methods reconstruct high-quality ultrasound images with significantly less defocusing and distortion compared with those without any correction. Specifically, the automatic optimization method reduced the array shape estimation error to less than half-wavelength of transmitted wave, resulting in a high-quality reconstructed image.

Ultrasound Imaging With a Flexible Probe Based on Element Array Geometry Estimation Using Deep Neural Network

Conventionally, ultrasound (US) diagnosis is performed using hand-held rigid probes. Such devices are difficult to be used for long-term monitoring because they need to be continuously pressed against the body to remove the air between the probe and body. Flexible probes, which can deform and effectively adhere to the body, are a promising technology for long-term monitoring applications. However, owing to the flexible element array geometry, the reconstructed image becomes blurred and distorted. In this study, we propose a flexible probe U.S. imaging method based on element array geometry estimation from radio frequency (RF) data using a deep neural network (DNN). The input and output of the DNN are the RF data and parameters that determine the element array geometry, respectively. The DNN was first trained from scratch with simulation data and then fine-tuned with in vivo data. The DNN performance was evaluated according to the element position mean absolute error (MAE) and the reconstructed image quality. The reconstructed image quality was evaluated with peak-signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM). In the test conducted with simulation data, the average element position MAE was 0.86 mm, and the average reconstructed image PSNR and MSSIM were 20.6 and 0.791, respectively. In the test conducted with in vivo data, the average element position MAE was 1.11 mm, and the average reconstructed image PSNR and MSSIM were 19.4 and 0.798, respectively. The average estimation time was 0.045 s. These results demonstrate the feasibility of the proposed method for long-term real-time monitoring using flexible probes.

Flexible ultrasound transceiver array for non-invasive surface-conformable imaging enabled by geometric phase correction

Ultrasound imaging provides the means for non-invasive real-time diagnostics of the internal structure of soft tissue in living organisms. However, the majority of commercially available ultrasonic transducers have rigid interfaces which cannot conform to highly-curved surfaces. These geometric limitations can introduce a signal-quenching air gap for certain topographies, rendering accurate imaging difficult or impractical. Here, we demonstrate a 256-element flexible two-dimensional (2D) ultrasound piezoelectric transducer array with geometric phase correction. We show surface-conformable real-time B-mode imaging, down to an extreme radius of curvature of 1.5 cm, while maintaining desirable performance metrics such as high signal-to-noise ratio (SNR) and minimal elemental cross-talk at all stages of bending. We benchmark the array capabilities by resolving reflectors buried at known locations in a medical-grade tissue phantom, and demonstrate how phase correction can improve image reconstruction on curved surfaces. With the current array design, we achieve an axial resolution of ≈ 2 mm at clinically-relevant depths in tissue, while operating the array at 1.4 MHz with a bandwidth of ≈ 41%. We use our prototype to image the surface of the human humerus at different positions along the arm, demonstrating proof-of-concept applicability for real-time diagnostics using phase-corrected flexible ultrasound probes.

Deep Learning for Ultrasound Beamforming in Flexible Array Transducer

Ultrasound imaging has been developed for image-guided radiotherapy for tumor tracking, and the flexible array transducer is a promising tool for this task. It can reduce the user dependence and anatomical changes caused by the traditional ultrasound transducer. However, due to its flexible geometry, the conventional delay-and-sum (DAS) beamformer may apply incorrect time delay to the radio-frequency (RF) data and produce B-mode images with considerable defocusing and distortion. To address this problem, we propose a novel end-to-end deep learning approach that may alternate the conventional DAS beamformer when the transducer geometry is unknown. Different deep neural networks (DNNs) were designed to learn the proper time delays for each channel, and they were expected to reconstruct the undistorted high-quality B-mode images directly from RF channel data. We compared the DNN results to the standard DAS beamformed results using simulation and flexible array transducer scan data. With the proposed DNN approach, the averaged full-width-at-half-maximum (FWHM) of point scatters is 1.80 mm and 1.31 mm lower in simulation and scan results, respectively; the contrast-to-noise ratio (CNR) of the anechoic cyst in simulation and phantom scan is improved by 0.79 dB and 1.69 dB, respectively; and the aspect ratios of all the cysts are closer to 1. The evaluation results show that the proposed approach can effectively reduce the distortion and improve the lateral resolution and contrast of the reconstructed B-mode images.