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Supplementary movie 1. Boundary selection software

Thyroid cancer is one of the most common cancers, with a global increase in incidence rate for both genders. Ultrasound-guided fine-needle aspiration is the current gold standard to diagnose thyroid cancers, but the results are inaccurate, leading to repeated biopsies and unnecessary surgeries. To reduce the number of unnecessary biopsies, we explored the use of multiparametric photoacoustic (PA) analysis in combination with the American Thyroid Association (ATA) Guideline (ATAP). In this study, we performed in vivo multispectral PA imaging on thyroid nodules from 52 patients, comprising 23 papillary thyroid cancer (PTC) and 29 benign cases. From the multispectral PA data, we calculated hemoglobin oxygen saturation level in the nodule area, then classified the PTC and benign nodules with multiparametric analysis. Statistical analyses showed that this multiparametric analysis of multispectral PA responses could classify PTC nodules. Combining the photoacoustically indicated probability of PTC and the ATAP led to a new scoring method that achieved a sensitivity of 83% and a specificity of 93%. This study is the first multiparametric analysis of multispectral PA data of thyroid nodules with statistical significance. As a proof of concept, the results show that the proposed new ATAP scoring can help physicians examine thyroid nodules for fine-needle aspiration biopsy, thus reducing unnecessary biopsies.Significance:This report highlights a novel photoacoustic scoring method for risk stratification of thyroid nodules, where malignancy of the nodules can be diagnosed with 83% sensitivity and 93% specificity.

Although accurate detection of breast cancer still poses significant challenges, deep learning (DL) can support more accurate image interpretation. In this study, we develop a highly robust DL model based on combined B-mode ultrasound (B-mode) and strain elastography ultrasound (SE) images for classifying benign and malignant breast tumors. This study retrospectively included 85 patients, including 42 with benign lesions and 43 with malignancies, all confirmed by biopsy. Two deep neural network models, AlexNet and ResNet, were separately trained on combined 205 B-mode and 205 SE images (80% for training and 20% for validation) from 67 patients with benign and malignant lesions. These two models were then configured to work as an ensemble using both image-wise and layer-wise and tested on a dataset of 56 images from the remaining 18 patients. The ensemble model captures the diverse features present in the B-mode and SE images and also combines semantic features from AlexNet and ResNet models to classify the benign from the malignant tumors. The experimental results demonstrate that the accuracy of the proposed ensemble model is 90%, which is better than the individual models and the model trained using B-mode or SE images alone. Moreover, some patients that were misclassified by the traditional methods were correctly classified by the proposed ensemble method. The proposed ensemble DL model will enable radiologists to achieve superior detection efficiency owing to enhance classification accuracy for breast cancers in ultrasound (US) images.

PURPOSE: Our goal was to test transcutaneous focused ultrasound in the form of ultrasonic propulsion and burst wave lithotripsy to reposition ureteral stones and facilitate passage in awake subjects. MATERIALS AND METHODS: Adult subjects with a diagnosed proximal or distal ureteral stone were prospectively recruited. Ultrasonic propulsion alone or with burst wave lithotripsy was administered by a handheld transducer to awake, unanesthetized subjects. Efficacy outcomes included stone motion, stone passage, and pain relief. Safety outcome was the reporting of associated anticipated or adverse events. RESULTS: Twenty-nine subjects received either ultrasonic propulsion alone (n = 16) or with burst wave lithotripsy bursts (n = 13), and stone motion was observed in 19 (66%). The stone passed in 18 (86%) of the 21 distal ureteral stone cases with at least 2 weeks follow-up in an average of 3.9±4.9 days post-procedure. Fragmentation was observed in 7 of the burst wave lithotripsy cases. All subjects tolerated the procedure with average pain scores (0–10) dropping from 2.1±2.3 to 1.6±2.0 (P = .03). Anticipated events were limited to hematuria on initial urination post-procedure and mild pain. In total, 7 subjects had associated discomfort with only 2.2% (18 of 820) propulsion bursts. CONCLUSIONS: This study supports the efficacy and safety of using ultrasonic propulsion and burst wave lithotripsy in awake subjects to reposition and break ureteral stones to relieve pain and facilitate passage.

Thyroid cancer is one of the most common cancers, with a global increase in incidence rate for both genders. Ultrasound-guided fine-needle aspiration is the current gold standard to diagnose thyroid cancers, but the results are inaccurate, leading to repeated biopsies and unnecessary surgeries. To reduce the number of unnecessary biopsies, we explored the use of multiparametric photoacoustic (PA) analysis in combination with the American Thyroid Association (ATA) Guideline (ATAP). In this study, we performed in vivo multispectral PA imaging on thyroid nodules from 52 patients, comprising 23 papillary thyroid cancer (PTC) and 29 benign cases. From the multispectral PA data, we calculated hemoglobin oxygen saturation level in the nodule area, then classified the PTC and benign nodules with multiparametric analysis. Statistical analyses showed that this multiparametric analysis of multispectral PA responses could classify PTC nodules. Combining the photoacoustically indicated probability of PTC and the ATAP led to a new scoring method that achieved a sensitivity of 83% and a specificity of 93%. This study is the first multiparametric analysis of multispectral PA data of thyroid nodules with statistical significance. As a proof of concept, the results show that the proposed new ATAP scoring can help physicians examine thyroid nodules for fine-needle aspiration biopsy, thus reducing unnecessary biopsies. Significance: This report highlights a novel photoacoustic scoring method for risk stratification of thyroid nodules, where malignancy of the nodules can be diagnosed with 83% sensitivity and 93% specificity.

OBJECTIVES—: To quantify the bias of shear wave speed (SWS) measurements between different commercial ultrasonic shear elasticity systems and a magnetic resonance elastography (MRE) system in elastic and viscoelastic phantoms. METHODS—: Two elastic phantoms, representing healthy through fibrotic liver, were measured with 5 different ultrasound platforms, and 3 viscoelastic phantoms, representing healthy through fibrotic liver tissue, were measured with 12 different ultrasound platforms. Measurements were performed with different systems at different sites, at 3 focal depths, and with different appraisers. The SWS bias across the systems was quantified as a function of the system, site, focal depth, and appraiser. A single MRE research system was also used to characterize these phantoms using discrete frequencies from 60 to 500 Hz. RESULTS—: The SWS from different systems had mean difference 95% confidence intervals of ±0.145 m/s (±9.6%) across both elastic phantoms and ± 0.340 m/s (±15.3%) across the viscoelastic phantoms. The focal depth and appraiser were less significant sources of SWS variability than the system and site. Magnetic resonance elastography best matched the ultrasonic SWS in the viscoelastic phantoms using a 140 Hz source but had a − 0.27 ± 0.027-m/s (−12.2% ± 1.2%) bias when using the clinically implemented 60-Hz vibration source. CONCLUSIONS—: Shear wave speed reconstruction across different manufacturer systems is more consistent in elastic than viscoelastic phantoms, with a mean difference bias of < ±10% in all cases. Magnetic resonance elastographic measurements in the elastic and viscoelastic phantoms best match the ultrasound systems with a 140-Hz excitation but have a significant negative bias operating at 60 Hz. This study establishes a foundation for meaningful comparison of SWS measurements made with different platforms.

INTRODUCTION AND OBJECTIVE:The feasibility of ultrasonic propulsion and burst wave lithotripsy (BWL) to noninvasively reposition distal ureteral stones to facilitate passage and relieve pain was te...

Strain ratio (SR) is a semiquantitative parameter in differentiating benign from malignant tumors in breast ultrasound elastography. Currently, SR is computed manually and, thus, user dependent. The objective of this study was to evaluate the performance of a new tool assist strain ratio (ASR) and determine how it performs compared with an expert sonologist.Ninety-one patients scheduled for breast biopsy were included in this institutional review board-approved/Health Insurance Portability and Accountability Act-compliant study. For manual strain ratio (MSR), fat and lesion were manually outlined, whereas for ASR, the clinician indicated the lesion center and the fat-to-lesion ratio is computed automatically. Three measurements were obtained for each lesion. The same raw data were used to calculate the MSR and ASR.The SR thresholds to differentiate benign from malignant tumors were determined using the Youden index. For MSR, the cutoff was 2.7, and for ASR was 2.8. The MSR showed a sensitivity of 88%, specificity of 64%, accuracy of 77%, positive predictive value of 72%, and negative predictive value of 92.1%. Corresponding ASR showed a sensitivity of 86%, specificity of 76%, accuracy of 81%, positive predictive value of 79%, and negative predictive value of 84%. The areas under the curve for the MSR and ASR were 0.83 and 0.85, respectively. The average coefficients of variation for the MSR and ASR measurements were 43% and 30%, respectively.Assist strain ratio demonstrated similar diagnostic performance compared with MSR. In addition, the coefficient of variation of ASR is lower, implying lower intraoperator dependency. Thus, ASR may aid less-experienced scanners in obtaining improved results.

Among clinical photoacoustic and ultrasound beamforming (BF) techniques, a nonlinear p-th root delay and sum (NL-p-DAS) exhibits better contrast and resolution than conventional delay-and-sum. However, despite its advantages, signal loss and grainy speckles often degrade image quality by signal deformation. Thus, we proposed a new nonlinear BF algorithm called nonlinear p-th root spectral magnitude scaling (NL-p-SMS), which scales the signal in the spectral domain instead of the temporal domain. As a result, we achieved the enhanced contrast, contrast noise ratio, spatial resolution, and less grainy texture without significant signal or feature loss in both in vivo photoacoustic and ultrasound images.

We have developed a software package called 3D PHOVIS (3D Photoacoustic Visualization Studio) to support 3D volume data visualization in academia. It was developed with MATLAB-based graphical user interface software that utilizes CPU and GPU parallel computing technology to provide a convenient real-time working environment. 3D PHOVIS includes five modules, which can help volume stitching, scanning distortion removing, depth encoded visualization, and volume rendering with 3D photoacoustic data. It also supports 3D movie generation. In this presentation, we will interpret the algorithms and principles used in software development, and demonstrate its function with several examples.

We report a novel photoacoustic (PA) scoring method for the risk stratification of thyroid nodules, which is combination of the American Thyroid Association (ATA) and PA malignancy probability. We performed multi-spectral PA imaging and multi-parametric PA analysis for thyroid cancer patients (23 malignancy and 29 benign cases). Initial multi-parametric PA analysis showed that malignancy of the thyroid nodules can be diagnosed with a 78% sensitivity and 93% specificity. Moreover, our novel score called ATAP improved the sensitivity to 83% while maintaining the specificity. The results suggest that the ATAP may help physicians examine thyroid nodules, thus reducing unnecessary biopsies.

Thyroid cancer is one of the most common cancers, with a global increase in incidence rate for both genders. Ultrasound-guided fine-needle aspiration is the current gold standard to diagnose thyroid cancers, but the results are inaccurate, leading to repeated biopsies and unnecessary surgeries. To reduce the number of unnecessary biopsies, we explored the use of multiparametric photoacoustic (PA) analysis in combination with the American Thyroid Association (ATA) Guideline (ATAP). In this study, we performed

A recently introduced nonlinear p th root delay-and-sum ( NL- p -DAS ) beamforming (BF) technique for ultrasound (US) and photoacoustic (PA) imaging, achieving better spatial and contrast resolution compared to a conventional delay and sum (DAS) technique. While the method is advantageous for better resolution, it suffers from grainy speckles and dark areas in the image mainly due to the interference of non-sinusoidal functions. In this Letter, we introduce a modified NL- p -DAS technique called nonlinear p th root spectral magnitude scaling ( NL- p -SMS ), which performs the p th root on the spectral magnitude instead of the temporal amplitude. We evaluated the US and PA images of NL- p -SMS against those of NL- p -DAS by comparing the axial and lateral line profiles, contrasts, and contrast-to-noise ratios (CNRs) in both phantom and in vivo imaging studies with various p values. As a result, we found that the NL- p -SMS has better axial resolution and CNR than the NL- p -DAS , and reduces the grainy speckles and dark area artifacts. We believe that, with this enhanced performance, our proposed approach could be an advancement compared to the existing nonlinear BF algorithms.

Feasibility of ultrasonic propulsion and burst wave lithotripsy (BWL) to noninvasively reposition distal ureteral stones to facilitate passage and relieve pain was tested. Patients presenting to the clinic or emergency department were recruited. Thirteen subjects underwent ultrasonic propulsion (lower amplitude, longer duration pulses) alone, and 10 subjects also received intermittent BWL (higher amplitude, shorter duration pulses). All participants were awake and underwent a pain assessment pre- and post-procedure. For analysis, subjects were sub-categorized based on whether their stone was acute or chronic (present for ≤10 days or >10 days from their initial ED or symptomatic presentation, respectively). Seventeen subjects were enrolled in the acute study population and 6 subjects were enrolled in the chronic population. Overall, 94% of acute stones passed in an average of 3.4 days post-procedure relative to 54% in 7.5 days in the American Urological Association guidelines. 67% of chronic cases passed stones or fragments, and two surgeries were cancelled. Pain reduction was statistically significant ( p = 0.0215). Adverse events were limited to hematuria on initial urination post-procedure (BWL only, n = 3) and a mild sensation, akin to a pinprick, associated with fewer than 10 of 620 propulsion bursts ( n = 3). [Work supported by NIH-P01-DK04331.]

The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world. The standard test for screening COVID-19 patients is the polymerase chain reaction test. As this method is time consuming, as an alternative, chest X-rays may be considered for quick screening. However, specialization is required to read COVID-19 chest X-ray images as they vary in features. To address this, we present a multi-channel pre-trained ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. Three ResNet-based models were retrained to classify X-rays in a one-against-all basis from (a) normal or diseased, (b) pneumonia or non-pneumonia, and (c) COVID-19 or non-COVID19 individuals. Finally, these three models were ensembled and fine-tuned using X-rays from 1579 normal, 4245 pneumonia, and 184 COVID-19 individuals to classify normal, pneumonia, and COVID-19 cases in a one-against-one framework. Our results show that the ensemble model is more accurate than the single model as it extracts more relevant semantic features for each class. The method provides a precision of 94% and a recall of 100%. It could potentially help clinicians in screening patients for COVID-19, thus facilitating immediate triaging and treatment for better outcomes.

Photoacoustic (PA) imaging (or optoacoustic imaging) is a novel biomedical imaging method in biological and medical research. This modality performs morphological, functional, and molecular imaging with and without labels in both microscopic and deep tissue imaging domains. A variety of innovations have enhanced 3D PA imaging performance and thus has opened new opportunities in preclinical and clinical imaging. However, the 3D visualization tools for PA images remains a challenge. There are several commercially available software packages to visualize the generated 3D PA images. They are generally expensive, and their features are not optimized for 3D visualization of PA images. Here, we demonstrate a specialized 3D visualization software package, namely 3D Photoacoustic Visualization Studio (3D PHOVIS), specifically targeting photoacoustic data, image, and visualization processes. To support the research environment for visualization and fast processing, we incorporated 3D PHOVIS onto the MATLAB with graphical user interface and developed multi-core graphics processing unit modules for fast processing. The 3D PHOVIS includes following modules: (1) a mosaic volume generator, (2) a scan converter for optical scanning photoacoustic microscopy, (3) a skin profile estimator and depth encoder, (4) a multiplanar viewer with a navigation map, and (5) a volume renderer with a movie maker. This paper discusses the algorithms present in the software package and demonstrates their functions. In addition, the applicability of this software to ultrasound imaging and optical coherence tomography is also investigated. User manuals and application files for 3D PHOVIS are available for free on the website (www.boa-lab.com). Core functions of 3D PHOVIS are developed as a result of a summer class at POSTECH, “High-Performance Algorithm in CPU/GPU/DSP, and Computer Architecture.” We believe our 3D PHOVIS provides a unique tool to PA imaging researchers, expedites its growth, and attracts broad interests in a wide range of studies.

Visualization methods are very important in biomedical imaging. As a technology that understands life, biomedical imaging has the unique advantage of providing the most intuitive information in the image. This advantage of biomedical imaging can be greatly improved by choosing a special visualization method. This is more complicated in volumetric data. Volume data has the advantage of containing 3D spatial information. Unfortunately, the data itself cannot directly represent the potential value. Because images are always displayed in 2D space, visualization is the key and creates the real value of volume data. However, image processing of 3D data requires complicated algorithms for visualization and high computational burden. Therefore, specialized algorithms and computing optimization are important issues in volume data. Photoacoustic-imaging is a unique imaging modality that can visualize the optical properties of deep tissue. Because the color of the organism is mainly determined by its light absorbing component, photoacoustic data can provide color information of tissue, which is closer to real tissue color. In this research, we developed realistic tissue visualization using acoustic-resolution photoacoustic volume data. To achieve realistic visualization, we designed specialized color transfer function, which depends on the depth of the tissue from the skin. We used direct ray casting method and processed color during computing shader parameter. In the rendering results, we succeeded in obtaining similar texture results from photoacoustic data. The surface reflected rays were visualized in white, and the reflected color from the deep tissue was visualized red like skin tissue. We also implemented the CUDA algorithm in an OpenGL environment for real-time interactive imaging.

Visualizing ocular vasculature is important in clinical ophthalmology because ocular circulation abnormalities are early signs of ocular diseases. Photoacoustic microscopy (PAM) images the ocular vasculature without using exogenous contrast agents, avoiding associated side effects. Moreover, 3D PAM images can be useful in understanding vessel-related eye disease. However, the complex structure of the multi-layered vessels still present challenges in evaluating ocular vasculature. In this study, we demonstrate a new method to evaluate blood circulation in the eye by combining in vivo PAM imaging and an ocular surface estimation method based on a machine learning algorithm: a random sample consensus algorithm. By using the developed estimation method, we were able to visualize the PA ocular vascular image intuitively and demonstrate layer-by-layer analysis of injured ocular vasculature. We believe that our method can provide more accurate evaluations of the eye circulation in ophthalmic applications.

One of the key additions to clinical ultrasound (US) systems during the last decade was the incorporation of three-dimensional (3-D) imaging as a native mode. Compared to previous-generation 3-D US imaging systems, today's systems offer easier volume acquisition and deliver superior image quality with various visualization options. This has come as a result of many technological advances and innovations in transducer design, electronics, computer architecture, and algorithms. While freehand 3-D US techniques continue to be used, mechanically scanned and/or two-dimensional (2-D) matrix-array transducers are increasingly adopted, enabling higher volume rates and easier acquisition. More powerful computing engines with instruction-level and data-level parallelism and high-speed memory access support new and improved 3-D visualization capabilities. Many clinical US systems today have a 3-D option that offers interactive acquisition and display. In this paper, we cover the innovations of the last decade that have enabled the current 3-D US systems from acquisition to visualization, with emphasis on transducers, algorithms, and computation.

In this paper, we present a new multi-volume rendering technique (i.e., progressive fusion) to combine 3D anatomical structures from B-mode imaging and flow information from power Doppler imaging. A post-fusion technique, in which B-mode and power Doppler volumes are independently rendered and then fused based on alpha blending, is typically used in 3D power Doppler imaging. However, it has limitations in preserving the spatial relationship (i.e., depth order) between tissue structure and vasculature since they are rendered independently and then merged. With the proposed progressive fusion, B-mode and power Doppler volumes are composited together while rendering by sharing the opacity values. After compositing, two rendered frames are blended by utilizing a 2D color lookup table designed to fuse two properties (i.e., tissues and blood flows). We have evaluated the progressive-fusion multi-volume rendering method with the phantom and in vivo data acquired using a commercial ultrasound machine (EUB-8500, Hitachi Medical Corporation, Japan) with a 3.5 MHz mechanical probe. From the preliminary study, we have found that the new progressive-fusion method can better retain and display the spatial relationship between tissue structure, vasculature and their corresponding depth order.

Color-flow imaging is a well-established ultrasound mode and very valuable for visualizing in real time the distribution of blood flow in a specific region of interest. However, it is computationally quite expensive. To meet the large computational need in color-flow imaging, most ultrasound systems have been designed using fixed-function hardware. In this paper, we present a system where all the color-flow processing is supported on a programmable platform. About 95% of the processing modules were programmed in C language. On a single processor, we were able to achieve 7.9 frames/s, when the input data consist of 192 /spl times/ 512 /spl times/ 8 (ensemble size) samples for color flow and 384 /spl times/ 512 for B mode and the output image size is 600 /spl times/ 420. Additional processors can be added to handle more input data and/or support higher frame rates. Our results demonstrate that a programmable ultrasound system can provide the same functionality for clinical use as conventional ultrasound systems. However, it is more flexible and efficient due to its programmability.

In this article, we present an adaptive clutter rejection method for selecting different clutter filters in ultrasound color flow imaging. A single clutter filter is typically used to reject the clutter. Because the clutter characteristics vary in both space and time, the single clutter filter approach has difficulty in providing optimum clutter rejection in ultrasound images. To achieve more accurate velocity estimation, we have developed a method to select a clutter filter adaptively at each location in an image from a set of predefined filters. Selection criteria have been developed based on the underlying clutter characteristics and the properties of various filters (e.g., minimum-phase finite impulse response, projection-initialized infinite impulse response and polynomial regression). We have incorporated our adaptive clutter rejection method in an ultrasound system. We have found that our adaptive method can reduce the mean absolute error between the estimated and true flow velocities significantly compared with the conventional methods, in which a single clutter filter is used throughout the entire image. With in vivo abdominal data, we obtained an average gain of 5.0 dB in signal-to-clutter ratio (SCR), compared with the conventional method. These preliminary results indicate that the proposed adaptive method could improve the accuracy of flow velocity estimation in ultrasound color flow imaging through the improvement in SCR and the reduction in bias.

We have developed a programmable ultrasound imaging system using a single commercially available mediaprocessor. We have efficiently mapped all of the necessary B-mode processing algorithms on the underlying processor architecture, including envelope detection, dynamic range compression, lateral and axial filtering, persistence processing, and scan conversion. Our system can handle varying specifications ranging from 128 vectors and 512 samples per vector to more than 256 vectors and 1024 samples per vector. For an image size of 330 vectors and 512 samples per vector, it can process 30 frames per second using a 300-MHz MAP-CA mediaprocessor from Hitachi/Equator Technologies. This programmable ultrasound machine will not only offer significant advantages in terms of low cost, portability, scalability, and reduced development time, but also provide a flexible platform for developing and deploying new clinical applications to aid the clinicians and improve the quality of healthcare to patients.

Three-dimensional (3D) ultrasound data are acquired mostly using a dedicated mechanical probe that houses a 1D array transducer. This 1D transducer swivels back and forth continuously in the elevation direction (continuous scanning) for fast acquisition. When 3D ultrasound data are acquired via continuous scanning but the continuous motion of a transducer is not taken into account during reconstruction, the reconstructed volume contains error. In this study, we systematically analyzed this error, which is a complex function of many parameters. The error increases when the transducer angular speed (ω) increases. Also, it varies depending on the voxel location inside an acquired volume. The mean error is calculated by averaging the errors at all acquired voxel locations. With a 60-degree volume angle, a 60-degree sector angle, 12-cm scan depth and 48 transmit beams per slice, the mean error is 5.3 mm when ω is 0.6 degrees/ms. When ω is reduced to 0.1 degrees/ms, the mean error decreases to 0.81 mm. We also assessed the impact of this error on the reconstructed images of a 3D phantom using simulation. At high angular speeds, the error in reconstructed images becomes noticeable and results in missing parts, geometric distortion and lowered image quality.

One of the key additions to clinical ultrasound (US) systems during the last decade has been the incorporation of three-dimensional (3D) imaging as a native mode. These systems provide interactive volume rendering and display of 3D data. However, interactive volume rendering is challenging due to the large amount of data I/O and computation required. Many rendering techniques have been developed ranging from fast algorithms with lower image quality to sophisticated algorithms with better image quality, e.g., shear-warp, shear-image-order, pre-integration and direct ray casting. In this paper, we discuss pros and cons of each of these rendering algorithms in terms of algorithm complexity and image quality. We also discuss a new rendering algorithm that we have proposed: pre-integrated shear-image-order (PISIO). PISIO algorithm overcomes the image-quality limitations of other fast algorithms and provides a fine balance between computation and image quality. To demonstrate the interactive performance of these algorithms, we implemented them on a programmable multicore processor, i.e., IBM Cell processor. We present the results in this paper.

Arbitrary volume reslicing (AVR) is used to generate 2D planes of any orientation from 3D data. Pre-scan-converted (pre-SC) AVR avoids the 3D scan conversion (SC) step and directly uses a pre-SC volume to generate 2D AVR planes. AVR is typically performed on a PC system external to the ultrasound frontend and backend. In this paper, we present an efficient pre-SC AVR algorithm in an ultrasound backend system. The backend supports AVR natively with other backend functions, so the PC system does not need to compute 2D AVR planes from 3D volumes. We used phantom and in vivo datasets to evaluate the pre-SC and post-SC AVR. The normalized root mean squared difference (NRMSD) was 1.23% for phantom data, and 1.21% for in vivo data. Using one of 8 cores in a multi-core processor, Cell, our algorithm can generate a 1024×768 16-bit AVR image in 30.2 ms from a 512×136×64 16-bit input volume.

Multi-volume rendering is a technique that renders and displays multiple volumes simultaneously. In ultrasound imaging, multi-volume rendering is used for mixing 3D anatomical structures from B-mode imaging with blood flow information from power Doppler imaging (PDI) or color Doppler imaging (CDI). A variety of multi-volume rendering techniques have been proposed, such as post fusion (PF), composite fusion (CF) and progressive fusion (PGF). PF, which combines independently-rendered volumes, is unable to depict a spatial relationship between B-mode images (i.e., tissue structure) and PDI/CDI images (i.e., blood flow). The CF technique suffers from color distortion due to intermixing of hue values. In our recent study, the PGF technique was found to better retain and display tissue structures, vasculature and their depth relationship. However, the disadvantages of PGF include its high computational cost due to the requirement of maintaining a separate rendering pipeline for each volume (i.e., B-mode and power/color Doppler) and potential artifacts of depth-order ambiguity. In this paper, we present a new flexible computationally efficient multivolume rendering technique, named volume fusion (VF), and compare it with existing techniques. We have evaluated the VF method and other multi-volume rendering techniques with data acquired from a commercial ultrasound machine and found that the VF technique can preserve the spatial relationships well amongst multiple volumes without color distortion while the same rendering pipeline can be used to support both PDI and CDI volume fusion.

Motivation Commercial ultrasound machines in the past did not provide the ultrasound researchers access to raw ultrasound data. Lack of this ability has impeded evaluation and clinical testing of novel ultrasound algorithms and applications. Objectives Recently, we developed a flexible ultrasound back-end where all the processing for the conventional ultrasound modes, such as B, M, color flow and spectral Doppler, was performed in software. The back-end has been incorporated into a commercial ultrasound machine, the Hitachi HiVision 5500. The goal of this work is to develop an ultrasound research interface on the back-end for acquiring raw ultrasound data from the machine. Methods The research interface has been designed as a software module on the ultrasound back-end. To increase the amount of raw ultrasound data that can be spooled in the limited memory available on the back-end, we have developed a method that can losslessly compress the ultrasound data in real time. Results and discussion The raw ultrasound data could be obtained in any conventional ultrasound mode, including duplex and triplex modes. Furthermore, use of the research interface does not decrease the frame rate or otherwise affect the clinical usability of the machine. The lossless compression of the ultrasound data in real time can increase the amount of data spooled by ∼2.3 times, thus allowing more than 6 s of raw ultrasound data to be acquired in all the modes. The interface has been used not only for early testing of new ideas with in vitro data from phantoms, but also for acquiring in vivo data for fine-tuning ultrasound applications and conducting clinical studies. We present several examples of how newer ultrasound applications, such as elastography, vibration imaging and 3D imaging, have benefited from this research interface. Since the research interface is entirely implemented in software, it can be deployed on existing HiVision 5500 ultrasound machines and may be easily upgraded in the future. Conclusions The developed research interface can aid researchers in the rapid testing and clinical evaluation of new ultrasound algorithms and applications. Additionally, we believe that our approach would be applicable to designing research interfaces on other ultrasound machines.