Deep Learning for Ultrasound Beamforming in 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.

Deep Beamforming for Real-Time 3-D Passive Acoustic Mapping With Row-Column-Addressed Arrays

Passive acoustic mapping (PAM) is a promising tool to monitor acoustic cavitation activities for focused ultrasound (FUS) therapies. While 2-D matrix arrays allow 3-D PAM, the high channel count requirement and the complexity of the receiving electronics limit their practical value in real-time imaging applications. In this regard, row-column-addressed (RCA) arrays have shown great potential in addressing the difficulties in real-time 3-D ultrasound imaging. However, currently, there is no applicable method for 3-D PAM with RCA arrays. In this work, we propose a deep beamformer for real-time 3-D PAM with RCA arrays. The deep beamformer leverages a deep neural network (DNN) to map radio frequency (RF) microbubble (MB) cavitation signals acquired with the RCA array to 3-D PAM images, achieving similar image quality to the reconstructions performed using the fully populated 2-D matrix array with the angular spectrum (AS) method. In the simulation, the images reconstructed by the deep beamformer showed less than 13.2% and 1.8% differences in the energy spread volume (ESV) and image signal-to-noise ratio (ISNR), compared with those reconstructed using the matrix array. However, the image reconstruction time was reduced by 11 and 30 times on the CPU and GPU, respectively, achieving 42.4 volumes per second image reconstruction speed on a GPU for a volume sized $128\times 128\times 128$ . Experimental data further validated the capabilities of the deep beamformer to accurately localize MB cavitation activities in 3-D space. These results clearly demonstrated the feasibility of real-time and 3-D monitoring of MB cavitation activities with RCA arrays and neural network-based beamformers.

Emerging Wearable Ultrasound Technology

This perspective article provides a brief overview on materials, fabrications, beamforming, and applications for wearable ultrasound devices, a rapidly growing field with versatile implications. Recent developments in miniaturization and soft electronics have significantly advanced wearable ultrasound devices. Such devices offer distinctive advantages over traditional ultrasound probes, including prolonged usability and operator independence, and have demonstrated their effectiveness in continuous monitoring, noninvasive therapies, and advanced human-machine interfaces. Wearable ultrasound devices can be classified into three main categories: rigid, flexible, and stretchable, each having distinctive properties and fabrication strategies. Key unique strategies in device design, packaging, and beamforming for each type of wearable ultrasound devices are reviewed. Furthermore, we highlight the latest applications enabled by wearable ultrasound technology in various areas. This article concludes by discussing the outstanding challenges within the field and outlines potential pathways for future advancements.

Flexible Ultrasonic Transducers for Wearable Biomedical Applications: A Review on Advanced Materials, Structural Designs, and Future Prospects

Due to the rapid developments in materials science and fabrication techniques, wearable devices have recently received increased attention for biomedical applications, particularly in medical ultrasound (US) imaging, sensing, and therapy. US is ubiquitous in biomedical applications because of its noninvasive nature, nonionic radiating, high precision, and real-time capabilities. While conventional US transducers are rigid and bulky, flexible transducers can be conformed to curved body areas for continuous sensing without restricting tissue movement or transducer shifting. This article comprehensively reviews the application of flexible US transducers in the field of biomedical imaging, sensing, and therapy. First, we review the background of flexible US transducers. Following that, we discuss advanced materials and fabrication techniques for flexible US transducers and their enabling technology status. Finally, we highlight and summarize some promising preliminary data with recent applications of flexible US transducers in biomedical imaging, sensing, and therapy. We also provide technical barriers, challenges, and future perspectives for further research and development.

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.

An Emerging Era: Conformable Ultrasound Electronics

Conformable electronics are regarded as the next generation of personal healthcare monitoring and remote diagnosis devices. In recent years, piezoelectric-based conformable ultrasound electronics (cUSE) have been intensively studied due to their unique capabilities, including nonradiative monitoring, soft tissue imaging, deep signal decoding, wireless power transfer, portability, and compatibility. This review provides a comprehensive understanding of cUSE for use in biomedical and healthcare monitoring systems and a summary of their recent advancements. Following an introduction to the fundamentals of piezoelectrics and ultrasound transducers, the critical parameters for transducer design are discussed. Next, five types of cUSE with their advantages and limitations are highlighted, and the fabrication of cUSE using advanced technologies is discussed. In addition, the working function, acoustic performance, and accomplishments in various applications are thoroughly summarized. It is noted that application considerations must be given to the tradeoffs between material selection, manufacturing processes, acoustic performance, mechanical integrity, and the entire integrated system. Finally, current challenges and directions for the development of cUSE are highlighted, and research flow is provided as the roadmap for future research. In conclusion, these advances in the fields of piezoelectric materials, ultrasound transducers, and conformable electronics spark an emerging era of biomedicine and personal healthcare.

Investigating pulse-echo sound speed estimation in breast ultrasound with deep learning

Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians in diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form diagnostic B-mode images. However, the components of breast tissue, such as glandular tissue, fat, and lesions, differ in sound speed. Given a constant sound speed assumption, these differences can degrade the quality of reconstructed images via phase aberration. Sound speed images can be a powerful tool for improving image quality and identifying diseases if properly estimated. To this end, we propose a supervised deep-learning approach for sound speed estimation from analytic ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates representative breast tissue samples by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We adopt a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map. The simulated tissue is interrogated with a plane wave transmit sequence, and the complex-value reconstructed images are used as input for the convolutional network. The network is trained on the sound speed distribution map of the simulated data, and the trained model can estimate sound speed given reconstructed pulse-echo signals. We further incorporate thermal noise augmentation during training to enhance model robustness to artifacts found in real ultrasound data. To highlight the ability of our model to provide accurate sound speed estimations, we evaluate it on simulated, phantom, and in-vivo breast ultrasound data.

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.

Visualizing tactile feedback: an overview of current technologies with a focus on ultrasound elastography

Tissue elasticity remains an essential biomarker of health and is indicative of irregularities such as tumors or infection. The timely detection of such abnormalities is crucial for the prevention of disease progression and complications that arise from late-stage illnesses. However, at both the bedside and the operating table, there is a distinct lack of tactile feedback for deep-seated tissue. As surgical techniques advance toward remote or minimally invasive options to reduce infection risk and hasten healing time, surgeons lose the ability to manually palpate tissue. Furthermore, palpation of deep structures results in decreased accuracy, with the additional barrier of needing years of experience for adequate confidence of diagnoses. This review delves into the current modalities used to fulfill the clinical need of quantifying physical touch. It covers research efforts involving tactile sensing for remote or minimally invasive surgeries, as well as the potential of ultrasound elastography to further this field with non-invasive real-time imaging of the organ's biomechanical properties. Elastography monitors tissue response to acoustic or mechanical energy and reconstructs an image representative of the elastic profile in the region of interest. This intuitive visualization of tissue elasticity surpasses the tactile information provided by sensors currently used to augment or supplement manual palpation. Focusing on common ultrasound elastography modalities, we evaluate various sensing mechanisms used for measuring tactile information and describe their emerging use in clinical settings where palpation is insufficient or restricted. With the ongoing advancements in ultrasound technology, particularly the emergence of micromachined ultrasound transducers, these devices hold great potential in facilitating early detection of tissue abnormalities and providing an objective measure of patient health.

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.

A flexible optoacoustic blood 'stethoscope' for noninvasive multiparametric cardiovascular monitoring

Quantitative and multiparametric blood analysis is of great clinical importance in cardiovascular disease diagnosis. Although there are various methods to extract blood information, they often require invasive procedures, lack continuity, involve bulky instruments, or have complicated testing procedures. Flexible sensors can realize on-skin assessment of several vital signals, but generally exhibit limited function to monitor blood characteristics. Here, we report a flexible optoacoustic blood 'stethoscope' for noninvasive, multiparametric, and continuous cardiovascular monitoring, without requiring complicated procedures. The optoacoustic blood 'stethoscope' features the light delivery elements to illuminate blood and the piezoelectric acoustic elements to capture light-induced acoustic waves. We show that the optoacoustic blood 'stethoscope' can adhere to the skin for continuous and non-invasive in-situ monitoring of multiple cardiovascular biomarkers, including hypoxia, intravascular exogenous agent concentration decay, and hemodynamics, which can be further visualized with a tailored 3D algorithm. Demonstrations on both in-vivo animal trials and human subjects highlight the optoacoustic blood 'stethoscope''s potential for cardiovascular disease diagnosis and prediction.

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.

DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer

Purpose

The study aimed to implement a novel, deeply accelerated adaptive radiation therapy (DAART) approach for lung cancer radiotherapy (RT). Lung cancer is the most common cause of cancer-related death, and RT is the preferred medically inoperable treatment for early stage non-small cell lung cancer (NSCLC). In the current lengthy workflow, it takes a median of four weeks from diagnosis to RT treatment, which can result in complete restaging and loss of local control with delay. We implemented the DAART approach, featuring a novel deepPERFECT system, to address unwanted delays between diagnosis and treatment initiation.

Materials and methods

We developed a deepPERFECT to adapt the initial diagnostic imaging to the treatment setup to allow initial RT planning and verification. We used data from 15 patients with NSCLC treated with RT to train the model and test its performance. We conducted a virtual clinical trial to evaluate the treatment quality of the proposed DAART for lung cancer radiotherapy.

Results

We found that deepPERFECT predicts planning CT with a mean high-intensity fidelity of 83 and 14 HU for the body and lungs, respectively. The shape of the body and lungs on the synthesized CT was highly conformal, with a dice similarity coefficient (DSC) of 0.91, 0.97, and Hausdorff distance (HD) of 7.9 mm, and 4.9 mm, respectively, compared with the planning CT scan. The tumor showed less conformality, which warrants acquisition of treatment Day1 CT and online adaptive RT. An initial plan was designed on synthesized CT and then adapted to treatment Day1 CT using the adapt to position (ATP) and adapt to shape (ATS) method. Non-inferior plan quality was achieved by the ATP scenario, while all ATS-adapted plans showed good plan quality.

Conclusion

DAART reduces the common online ART (ART) treatment course by at least two weeks, resulting in a 50% shorter time to treatment to lower the chance of restaging and loss of local control.

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.

Supervised segmentation for guiding peripheral revascularization with forward-viewing, robotically steered ultrasound guidewire

BACKGROUND

Approximately 500 000 patients present with critical limb ischemia (CLI) each year in the U.S., requiring revascularization to avoid amputation. While peripheral arteries can be revascularized via minimally invasive procedures, 25% of cases with chronic total occlusions are unsuccessful due to inability to route the guidewire beyond the proximal occlusion. Improvements to guidewire navigation would lead to limb salvage in a greater number of patients.

PURPOSE

Integrating ultrasound imaging into the guidewire could enable direct visualization of routes for guidewire advancement. In order to navigate a robotically-steerable guidewire with integrated imaging beyond a chronic occlusion proximal to the symptomatic lesion for revascularization, acquired ultrasound images must be segmented to visualize the path for guidewire advancement.

METHODS

The first approach for automated segmentation of viable paths through occlusions in peripheral arteries is demonstrated in simulations and experimentally-acquired data with a forward-viewing, robotically-steered guidewire imaging system. B-mode ultrasound images formed via synthetic aperture focusing (SAF) were segmented using a supervised approach (U-net architecture). A total of 2500 simulated images were used to train the classifier to distinguish the vessel wall and occlusion from viable paths for guidewire advancement. First, the size of the synthetic aperture resulting in the highest classification performance was determined in simulations (90 test images) and compared with traditional classifiers (global thresholding, local adaptive thresholding, and hierarchical classification). Next, classification performance as a function of the diameter of the remaining lumen (0.5 to 1.5 mm) in the partially-occluded artery was tested using both simulated (60 test images at each of 7 diameters) and experimental data sets. Experimental test data sets were acquired in four 3D-printed phantoms from human anatomy and six ex vivo porcine arteries. Accuracy of classifying the path through the artery was evaluated using microcomputed tomography of phantoms and ex vivo arteries as a ground truth for comparison.

RESULTS

An aperture size of 3.8 mm resulted in the best-performing classification based on sensitivity and Jaccard index, with a significant increase in Jaccard index (p < 0.05) as aperture diameter increased. In comparing the performance of the supervised classifier and traditional classification strategies with simulated test data, sensitivity and F1 score for U-net were 0.95 ± 0.02 and 0.96 ± 0.01, respectively, compared to 0.83 ± 0.03 and 0.41 ± 0.13 for the best-performing conventional approach, hierarchical classification. In simulated test images, sensitivity (p < 0.05) and Jaccard index both increased with increasing artery diameter (p < 0.05). Classification of images acquired in artery phantoms with remaining lumen diameters ≥ 0.75 mm resulted in accuracies > 90%, while mean accuracy decreased to 82% when artery diameter decreased to 0.5 mm. For testing in ex vivo arteries, average binary accuracy, F1 score, Jaccard index, and sensitivity each exceeded 0.9.

CONCLUSIONS

Segmentation of ultrasound images of partially-occluded peripheral arteries acquired with a forward-viewing, robotically-steered guidewire system was demonstrated for the first-time using representation learning. This could represent a fast, accurate approach for guiding peripheral revascularization.

A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data

High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model's discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation.

A wearable cardiac ultrasound imager

Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients1-4. However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness5-11, and existing wearable cardiac devices can only capture signals on the skin12-16. Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments.

Improving Lateral Resolution in 3-D Imaging With Micro-beamforming Through Adaptive Beamforming by Deep Learning

There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral resolution a challenge. As micro-beamforming is often employed to reduce data rate and cable count to acceptable levels, receive processing methods that try to improve spatial resolution will have to compensate the introduced reduction in focusing. Existing beamformers do not realize sufficient improvement and/or have a computational cost that prohibits their use. Here we propose the use of adaptive beamforming by deep learning (ABLE) in combination with training targets generated by a large aperture array, which inherently has better lateral resolution. In addition, we modify ABLE to extend its receptive field across multiple voxels. We illustrate that this method improves lateral resolution both quantitatively and qualitatively, such that image quality is improved compared with that achieved by existing delay-and-sum, coherence factor, filtered-delay-multiplication-and-sum and Eigen-based minimum variance beamformers. We found that only in silica data are required to train the network, making the method easily implementable in practice.

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.

Systematic study of the iodinated rectal hydrogel spacer material discrepancy on accuracy of proton dosimetry

PURPOSE

Iodination of rectal hydrogel spacer increases the computed tomography (CT) visibility. The effect of iodinated hydrogel spacer material on the accuracy of proton dosimetry has not been fully studied yet. We presented a systematic study to determine the effect of iodination on proton dosimetry accuracy during proton therapy (PT).

METHODS

PT plans were designed for 20 prostate cancer patients with rectal hydrogel spacer. Three variations of hydrogel density were considered. First, as the ground truth, the true elemental composition of hydrogel true material (TM), verified by our measurement of spacer stopping power ratio, was used for plan optimization and Monte Carlo dose calculation. The dose distribution was recalculated with (1) no material (NM) override based on the CT intensity of the iodinated spacer, and (2) the water material (WM) override, where spacer material was replaced by water. The plans were compared with the ground truth using the metrics of gamma index (GI) and dosimetric indices.

RESULTS

The iodination of hydrogel spacer affected the proton dose distribution with the NM scenario showing the most deviation from the ground truth. The iodination of spacer resulted in a notable increase in CT intensity and led to the treatment planning systems mistreating the iodinated spacer as a high-density material. Among the structures adjacent to the target, neurovascular bundles showed the largest dose difference, up to 350 cGy or about 5% of the prescribed dose with NM. Compared to the WM scenario, dose distribution similarity and GI passing ratios were lower in the NM scenario.

CONCLUSION

The inaccurate CT intensity-based material for iodinated spacer resulted in errors in PT dose calculation. We found that the error was negligible if the iodinated spacer was replaced with water. Water density can be used as a clinically accessible and convenient alternative material override to true spacer material.

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.

A phantom-based analysis for tracking intra-fraction pancreatic tumor motion by ultrasound imaging during radiation therapy

Purpose

In this study, we aim to further evaluate the accuracy of ultrasound tracking for intra-fraction pancreatic tumor motion during radiotherapy by a phantom-based study.

Methods

Twelve patients with pancreatic cancer who were treated with stereotactic body radiation therapy were enrolled in this study. The displacement points of the respiratory cycle were acquired from 4DCT and transferred to a motion platform to mimic realistic breathing movements in our phantom study. An ultrasound abdominal phantom was placed and fixed in the motion platform. The ground truth of phantom movement was recorded by tracking an optical tracker attached to this phantom. One tumor inside the phantom was the tracking target. In the evaluation of the results, the monitoring results from the ultrasound system were compared with the phantom motion results from the infrared camera. Differences between infrared monitoring motion and ultrasound tracking motion were analyzed by calculating the root-mean-square error.

Results

The 82.2% ultrasound tracking motion was within a 0.5 mm difference value between ultrasound tracking displacement and infrared monitoring motion. 0.7% ultrasound tracking failed to track accurately (a difference value &gt; 2.5 mm). These differences between ultrasound tracking motion and infrared monitored motion do not correlate with respiratory displacements, respiratory velocity, or respiratory acceleration by linear regression analysis.

Conclusions

The highly accurate monitoring results of this phantom study prove that the ultrasound tracking system may be a potential method for real-time monitoring targets, allowing more accurate delivery of radiation doses.

Demonstrating the benefits of corrective intraoperative feedback in improving the quality of duodenal hydrogel spacer placement

Purpose

Pancreatic cancer is the fourth leading cause of cancer‐related death with a 10% 5‐year overall survival rate (OS). Radiation therapy (RT) in addition to dose escalation improves the outcome by significantly increasing the OS at 2 and 3 years but is hindered by the toxicity of the duodenum. Our group showed that the insertion of hydrogel spacer reduces duodenal toxicity, but the complex anatomy and the demanding procedure make the benefits highly uncertain. Here, we investigated the feasibility of augmenting the workflow with intraoperative feedback to reduce the adverse effects of the uncertainties.

Materials and Methods

We simulated three scenarios of the virtual spacer for four cadavers with two types of gross tumor volume (GTV) (small and large); first, the ideal injection; second, the nonideal injection that incorporates common spacer placement uncertainties; and third, the corrective injection that uses the simulation result from nonideal injection and is designed to compensate for the effect of uncertainties. We considered two common uncertainties: (1) “Narrowing” is defined as the injection of smaller spacer volume than planned. (2) “Missing part” is defined as failure to inject spacer in the ascending section of the duodenum. A total of 32 stereotactic body radiation therapy (SBRT) plans (33 Gy in 5 fractions) were designed, for four cadavers, two GTV sizes, and two types of uncertainties. The preinjection scenario for each case was compared with three scenarios of virtual spacer placement from the dosimetric and geometric points of view.

Results

We found that the overlapping PTV space with the duodenum is an informative quantity for determining the effective location of the spacer. The ideal spacer distribution reduced the duodenal V33Gy for small and large GTV to less than 0.3 and 0.1cc, from an average of 3.3cc, and 1.2cc for the preinjection scenario. However, spacer placement uncertainties reduced the efficacy of the spacer in sparing the duodenum (duodenal V33Gy: 1.3 and 0.4cc). The separation between duodenum and GTV decreased by an average of 5.3 and 4.6 mm. The corrective feedback can effectively bring back the expected benefits from the ideal location of the spacer (averaged V33Gy of 0.4 and 0.1cc).

Conclusions

An informative feedback metric was introduced and used to mitigate the effect of spacer placement uncertainties and maximize the benefits of the EUS‐guided procedure.