Search

Cardiac hemangioma is a rare benign tumour that presents diagnostic challenges due to its variable clinical symptoms, imaging features, and locations. This study proposes a novel segmentation method based on a Convolutional Neural Network (CNN) and Transformer integration, with Swin‐UNet as the core model. We incorporated a U‐shaped convolutional neural network block into the original jump connection of Swin‐UNet. The Binary Cross Entropy Loss (BCE Loss) algorithm was added, and the learning rate decay algorithm was modified to select the appropriate one by comparing loss values. This paper utilizes the publicly available cardiac angioma dataset in AI Studio, consisting of 215 images for training and testing. To evaluate the effectiveness of the proposed model, this paper demonstrates its optimality through ablation experiments and comparisons with other mainstream models. The comparison experiments show that this model improves Dice by approximately 12%, HD95 by approximately 4.7 mm, Accuracy by approximately 6.1%, and F1 score by 0.11 compared to models such as UNet, UNet++, and Deeplabv3+, etc. For the recently proposed SOTO models, such as TransUNet, Swin‐UNet, and MultiResUnet, the Dice score improved by about 1.2%, HD95 reduced by about 1mm, Accuracy improved by about 0.3%, and F1 score improved by 0.015. [ABSTRACT FROM AUTHOR]

Abstract Cardiac hemangioma is a rare benign tumour that presents diagnostic challenges due to its variable clinical symptoms, imaging features, and locations. This study proposes a novel segmentation method based on a Convolutional Neural Network (CNN) and Transformer integration, with Swin‐UNet as the core model. We incorporated a U‐shaped convolutional neural network block into the original jump connection of Swin‐UNet. The Binary Cross Entropy Loss (BCE Loss) algorithm was added, and the learning rate decay algorithm was modified to select the appropriate one by comparing loss values. This paper utilizes the publicly available cardiac angioma dataset in AI Studio, consisting of 215 images for training and testing. To evaluate the effectiveness of the proposed model, this paper demonstrates its optimality through ablation experiments and comparisons with other mainstream models. The comparison experiments show that this model improves Dice by approximately 12%, HD95 by approximately 4.7 mm, Accuracy by approximately 6.1%, and F1 score by 0.11 compared to models such as UNet, UNet++, and Deeplabv3+, etc. For the recently proposed SOTO models, such as TransUNet, Swin‐UNet, and MultiResUnet, the Dice score improved by about 1.2%, HD95 reduced by about 1mm, Accuracy improved by about 0.3%, and F1 score improved by 0.015.

Abstract In this work, the design and development of a 128‐channel transceiver hardware for medical ultrasound imaging systems and research is presented. The proposed hardware solution integrates the analog front‐end (AFE) sections, high voltage transmit pulser sections, field programmable gate array (FPGA)‐based transmit beamforming and control logic, time gain compensation (TGC) and continuous (CW) Doppler functional circuits, and the necessary power supplies (high voltage (HV) and low voltage (LV)) into a single board. In addition, it integrates pervasive segments like power, clock tree sections, and power management and debugger logic. The developed transceiver solution helps to advance the research in medical ultrasound imaging techniques and technologies. To prototype an ultrasound imaging system, the developed hardware can be interfaced with a 128‐channel ultrasound transducer array and an FPGA‐based signal processing module. As the transceiver hardware is designed with commercially available chipsets, it provides the flexibility to programme the ultrasound AFE signal chain, transmit beamforming and the arbitrary transmit wave pattern. Besides, compared to the commercial open ultrasound research scanners, the flexibility to interface FPGA‐based signal processing module helps to investigate the performance of hardware realisation of various ultrasound signal processing algorithms. Moreover, the work realises a single‐board transceiver solution for multichannel ultrasound system fulfilment.

Echography imaging is an important technique frequently used in medical diagnostics due to low-cost, non-ionising characteristics, and pragmatic convenience. Due to the shortage of skilful technicians and injuries of physicians sustained from diagnosing several patients, robot-assisted echography (RAE) system is gaining great attention in recent decades. A thorough study of the recent research advances in the field of perception, control and cognition techniques used in RAE systems is presented in this study. This survey introduces the representative system structure, applications and projects, and products. Challenges and key technological issues faced by the traditional RAE system and how the current artificial intelligence and cobots attempt to overcome these issues are summarised. Furthermore, significant future research directions in this field have been identified by this study as cognitive computing, operational skills transfer, and commercially feasible system design.

Traditional cardiovascular ultrasound detection using focused ultrasound can cause a decrease in frame rate, which affects the results of the diagnosis. In order to improve the effect of cardiovascular ultrasound detection, this study used ultrasound vector blood flow imaging technology to improve the image frame rate and introduce planar high frame rate imaging technology. Simultaneously, this work studies the parameters of the image through parameter analysis and combines the advantages of various methods to improve the analysis. In addition, this work proposes a high frame rate blood flow imaging method, and designs simulation experiments to analyse the effectiveness of the method. The results show that the proposed algorithm has a certain effect in two‐dimensional vector blood flow imaging, which can be applied to clinical practice, and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

The proposed 1D elastic metamaterial rod finds application in non-destructive evaluation, as a filter in the transduction side, where only the torsional and flexural modes need to be generated by isolating longitudinal modes. In this Letter, this is achieved by obtaining a wide bandgap in low-frequency ultrasound regime. The novelty of the proposed design arises from the use of a high impedance mismatch between the materials selected for the rod. The proposed concept is demonstrated and verified with numerical simulations. The effectiveness of the proposed technique is confirmed by comparing the results obtained from the rod made of a single material. [ABSTRACT FROM AUTHOR]

Conventional ultrasonic imaging systems suffer from poor resolution imposed by the diffraction limit. The authors have recently demonstrated the use of holey-structured metamaterials (HSMs) to enable resolution beyond the diffraction limit in the ultrasonic regime. However, imaging with HSM requires acquisition of data at fine spatial intervals. Although the use of laser Doppler vibrometers (LDV) as a receiver can be a solution for this as reported in earlier experimental studies, they are highly sensitive to ambient disturbances, suffer from low signal-to-noise ratio and are also expensive, hampering a wider practical implementation. This Letter presents experimental results using hollow cone attachments coupled with conventional ultrasonic transducers for achieving subwavelength resolution imaging at a relatively higher speed and minimal cost. The proposed methodology can be implemented easily for practical inspections and has great potential for commercialisation. [ABSTRACT FROM AUTHOR]

Ultrasound (US) imaging is the leading diagnostic method for assessing the early‐stage thyroid nodule. However, the visual evaluation of nodules can be influenced by the subjectivity of radiologists' interpretations. Computer‐aided Diagnostic (CAD) systems can be useful in classifying these nodules according to their benign or malignant nature. The extraction of the characteristics, which relate in the author's case to the US of thyroid nodules, is essential in the differentiation of these nodules. The complex nature of images, however, generates a significant number of features, many of which are either redundant or irrelevant. This study presents a new CAD system that has been developed to categorise thyroid nodules. In this survey, 447 US images of thyroid nodules were retained. These images were used to extract features using statistical features extraction methods. A feature selection method based on the multi objective particle swarm optimisation algorithm was used to choose the most relevant and non‐redundant ones. Then, support vector machine (SVM) and random forests (RFs) were applied to classify these nodules. 10‐fold cross‐validation was used to assess the classification performance metrics. Their proposed CAD has reached a maximum accuracy of 94.28% for SVM; and 96.13% for RF using the contour‐based ROI. [ABSTRACT FROM AUTHOR]

Speckle noise is an undesirable phenomenon that exhibits granular pattern which reduces the diagnostic capability of clinical ultrasound (US) images. In this study, a new speckle denoising method based on modelling of detail band shearlet coefficients of log‐transformed US images is presented. In each detail shearlet subband, coefficients corresponding to signal and speckle noise are modelled as normal inverse Gaussian and Gaussian priors, respectively. These coefficients, based on image local statistics are segmented into different regions of heterogeneity viz. homogeneous, heterogeneous and strongly heterogeneous regions, respectively, so as to control over smoothing of denoised images. Then, using the prior distributions maximum a posteriori (MAP) estimation is performed over all regions of detail bands except those regions that represent strongly heterogeneous coefficients. For better performance, an adaptive weight function is also used in the MAP expression which reduces the loss of feature information. Experimentation is done on noise‐free synthetic kidney and foetus US images and a set of 60 real US images. The results are presented for objective and subjective quality assessment of the proposed method and five other methods for speckle denoising. The potential of the proposed method in comparison to other methods can easily be ascertained from the obtained denoising results. [ABSTRACT FROM AUTHOR]

The overall prevalence of chronic kidney disease in the general population is ∼14% with more than 661,000 Americans having a kidney failure. Ultrasound (US)-guided renal biopsy is a critically important tool in the evaluation and management of renal pathologies. This Letter presents KBVTrainer, a virtual simulator that the authors developed to train clinicians to improve procedural skill competence in US-guided renal biopsy. The simulator was built using low-cost hardware components and open source software libraries. They conducted a face validation study with five experts who were either adult/pediatric nephrologists or interventional/diagnostic radiologists. The trainer was rated very highly (>4.4) for the usefulness of the real US images (highest at 4.8), potential usefulness of the trainer in training for needle visualization, tracking, steadiness and hand-eye coordination, and overall promise of the trainer to be useful for training US-guided needle biopsies. The lowest score of 2.4 was received for the look and feel of the US probe and needle compared to clinical practice. The force feedback received a moderate score of 3.0. The clinical experts provided abundant verbal and written subjective feedback and were highly enthusiastic about using the trainer as a valuable tool for future trainees.

Neurovascular surgery aims to repair diseased or damaged blood vessels in the brain or spine. There are numerous procedures that fall under this category, and in all of them, the direction of blood flow through these vessels is crucial information. Current methods to determine this information intraoperatively include static pre-operative images combined with augmented reality, Doppler ultrasound, and injectable fluorescent dyes. Each of these systems has inherent limitations. This study includes the proposal and preliminary validation of a technique to identify the direction of blood flow through vessels using only video segments of a few seconds acquired from routinely used surgical microscopes. The video is enhanced to reveal subtle colour fluctuations related to blood pulsation, and these rhythmic signals are further analysed in Fourier space to reveal the direction of blood flow. The proposed method was validated using a novel physical phantom and retrospective analysis of surgical videos and demonstrated high accuracy in identifying the direction of blood flow.

High-intensity focused ultrasound (HIFU) therapy represents an image-guided and non-invasive surgical approach to treat uterine fibroid. During the HIFU operation, it is challenging to obtain the real-time and accurate lesion contour automatically in ultrasound (US) video. The current intraoperative image processing is completed manually or semi-automatic. In this Letter, the authors propose a morphological active contour without an edge-based model to obtain accurate real-time and non-rigid US lesion contour. Firstly, a targeted image pre-processing procedure is applied to reduce the influence of inadequate image quality. Then, an improved morphological contour detection method with a customised morphological kernel is harnessed to solve the low signal-to-noise ratio of HIFU US images and obtain an accurate non-rigid lesion contour. A more reasonable lesion tracking procedure is proposed to improve tracking accuracy especially in the case of large displacement and incomplete lesion area. The entire framework is accelerated by the GPU to achieve a high frame rate. Finally, a non-rigid, real-time and accurate lesion contouring for intraoperative US video is provided to the doctor. The proposed procedure could reach a speed of more than 30 frames per second in general computer and a Dice similarity coefficient of 90.67% and Intersection over Union of 90.14%.

The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axillary ultrasound imaging and axillary lymph node metastasis, without exploring whether ultrasound imaging of breast tumour can affect and perform axillary lymph node prediction. Therefore, this Letter proposes a novel particle space-time distribution model to find the correlation between contrast-enhanced ultrasonography of breast tumour and axillary lymphatic metastasis. Starting from the imaging principle of dynamic contrast-enhanced ultrasonography, the particle space-time distribution model not only comprises space-time features of contrast-enhanced ultrasonography with an encoder–decoder network, but also the flow field information of microbubble particles is integrated into the space-time features that better serves the metastasis prediction by enhancing the particle distribution information. Extensive experiments on real patients have demonstrated that dynamic contrast-enhanced ultrasonography of breast tumour can be used to predict the probability of lymphatic metastasis. This conclusion can be interpretable from the clinical and pathological perspectives.

The authors present a deep learning algorithm for the automatic centroid localisation of out-of-plane US needle reflections to produce a semi-automatic ultrasound (US) probe calibration algorithm. A convolutional neural network was trained on a dataset of 3825 images at a 6 cm imaging depth to predict the position of the centroid of a needle reflection. Applying the automatic centroid localisation algorithm to a test set of 614 annotated images produced a root mean squared error of 0.62 and 0.74 mm (6.08 and 7.62 pixels) in the axial and lateral directions, respectively. The mean absolute errors associated with the test set were 0.50 ± 0.40 mm and 0.51 ± 0.54 mm (4.9 ± 3.96 pixels and 5.24 ± 5.52 pixels) for the axial and lateral directions, respectively. The trained model was able to produce visually validated US probe calibrations at imaging depths on the range of 4–8 cm, despite being solely trained at 6 cm. This work has automated the pixel localisation required for the guided-US calibration algorithm producing a semi-automatic implementation available open-source through 3D Slicer. The automatic needle centroid localisation improves the usability of the algorithm and has the potential to decrease the fiducial localisation and target registration errors associated with the guided-US calibration method.

Oxygen loaded microbubbles are being investigated as a means of reducing tumour hypoxia in order to improve response to cancer therapy. To optimise this approach, it is desirable to be able to measure changes in tissue oxygenation in real-time during treatment. In this study, the feasibility of using magnetic resonance imaging (MRI) for this purpose was investigated. Longitudinal relaxation time (T1) measurements were made in simple hydrogel phantoms containing two different concentrations of oxygen microbubbles. T1 was found to be unaffected by the presence of oxygen microbubbles at either concentration. Upon application of ultrasound to destroy the microbubbles, however, a statistically significant reduction in T1 was seen for the higher microbubble concentration. Further work is needed to assess the influence of physiological conditions upon the measurements, but these preliminary results suggest that MRI could provide a method for quantifying the changes in tissue oxygenation produced by microbubbles during therapy.

Ultrasound (US) image segmentation plays a very important role in diagnostic imaging. In order to extract special tissues from images, this study proposes a new method for segmentation of US images. The proposed method uses multilevel threshold segmentation which is based on Otsu and differential search algorithm. Testing in simulation US images shows that the proposed algorithm has a better result of segmentation than the three existing methods, including region growing, the active contour model and k‐means technique. The proposed method gets the highest Fm values and the smallest area errors in experiments. Vivo US images are also tested by the proposed method and it achieves a good segmentation result. [ABSTRACT FROM AUTHOR]

Two‐dimensional transforms are extensively used for speckle noise reduction (despeckling) in ultrasound images. This work proposes a double filter bank structure consisting of a discrete wavelet packet transform (DWPT) and directional filter bank (DFB) along with a fuzzy‐based clustering technique to despeckle ultrasound images. Wavelet packet transform can efficiently reject noises based on grey scale relational thresholding and DFB can efficiently preserve edge information. In this study, instead of conventional thresholding methods, fuzzy‐based clustering techniques are applied for noise rejection. The algorithm provides a consistent improvement over the competing state‐of‐the‐art speckle reduction algorithms due to the improved ability to preserve geometric features while rejecting speckle noise adaptively. The authors claim is validated by applying a number of clinical images with performance indices such as peak signal to noise ratio, mean structural similarity, signal to mean square error, speckle signal to noise ratio, speckle suppression index and edge preservation index. [ABSTRACT FROM AUTHOR]

This study introduces an image denoising method in the presence of combined speckle and Gaussian noise. The dual‐tree complex wavelet transform is applied to the image in order to obtain specific coefficients characterising these types of noise. Then, these extracted coefficients are removed by thresholding and an inverse wavelet transform is applied in order to obtain the reconstructed image. A comparison between the dual‐tree and standard wavelet‐based denoising filters is provided on the basis of different structural metrics. Finally, in order to remove any remaining noise, a spatial denoising filter is applied to the image. The results obtained on medical ultrasound images corrupted by this noise combination support the authors' assertion on the method's resilience to the combined effects of speckle and Gaussian noise, and are compared to well‐known and most effective speckle–Gaussian denoising filters. [ABSTRACT FROM AUTHOR]

Cerebrovascular surgery treats vessel abnormalities in the brain and spinal cord, including arteriovenous malformations (AVMs) and aneurysms. These procedures often involve clipping the vessels feeding blood to these abnormalities, making accurate classification of blood vessel types (feeding versus draining) important during surgery. Previous work to guide the intraoperative identification of the vessels included augmented reality (AR) using pre-operative images, injected dyes, and Doppler ultrasound, but each with their drawbacks. The authors propose and demonstrate a novel technique to help differentiate vessels by enhancing short videos of a few seconds from the surgical microscope using motion magnification and spectral analysis, and constructing AR views that fuse the analysis results as intuitive colourmaps and the surgical microscopic view. They demonstrated the proposed technique retrospectively with two real cerebrovascular surgical cases: one AVM and one aneurysm. The results showed that the proposed technique can help characterise different vessel types (feeding and draining the abnormality), which agree with those identified by the operating surgeon.

Augmented reality (AR) has proven to be a useful, exciting technology in several areas of healthcare. AR may especially enhance the operator's experience in minimally invasive surgical applications by providing more intuitive and naturally immersive visualisation in those procedures which heavily rely on three-dimensional (3D) imaging data. Benefits include improved operator ergonomics, reduced fatigue, and simplified hand–eye coordination. Head-mounted AR displays may hold great potential for enhancing surgical navigation given their compactness and intuitiveness of use. In this work, the authors propose a method that can intra-operatively locate bone structures using tracked ultrasound (US), registers to the corresponding pre-operative computed tomography (CT) data and generates 3D AR visualisation of the operated surgical scene through a head-mounted display. The proposed method deploys optically-tracked US, bone surface segmentation from the US and CT image volumes, and multimodal volume registration to align pre-operative to the corresponding intra-operative data. The enhanced surgical scene is then visualised in an AR framework using a HoloLens. They demonstrate the method's utility using a foam pelvis phantom and quantitatively assess accuracy by comparing the locations of fiducial markers in the real and virtual spaces, yielding root mean square errors of 3.22, 22.46, and 28.30 mm in the x, y, and z directions, respectively.

A non-destructive testing system requires multiple system configuration parameters during the operation process. For a given transducer, scanning frequency and number of measurements for averaging are just among those parameters. This work tests the central limit theorem to optimally set these parameters. The authors have designed a compact ultrasound computer tomography scanner from scratch just to test this criterion. It is shown that optimal frequency value changes with respect to the scanning angle for an object with a heterogeneous inner profile. The proposed criterion helps to estimate the multi-spectral combination before the recovery stage minimum error. Possible industrial applications of such a system are 2D/3D multiphase flow profile recovery or crack detection in the composite matter, non-invasively. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for ultrasound (US) images denoising. It concerns a class of generalised method of moments estimators with interesting asymptotic properties for wavelet coefficients 2D generalised autoregressive conditional heteroscedasticity modelling. Afterwards, these estimators can be used for removing noise from US images. Indeed, a minimum mean ‐square error method is applied for estimating the clean wavelet image coefficients. To judge the quality of the denoising procedure, a link between the denoising efficiency procedure and a proposed asymmetry measure is established. Several tests have been carried out to prove the performance of the proposed approach. The obtained results are compared with those of contemporary image denoising methods using usual image quality assessment metrics and two proposed no‐reference quality metrics. [ABSTRACT FROM AUTHOR]

This study presents a geometric deformable model‐based segmentation approach to segmentation of the intima and media‐adventitial (MA) borders in sequential intravascular ultrasound (IVUS) images. The initial estimation of the vessel borders was done manually only for the first frame of each sequence. After the border initialisation, pre‐processing including edge preservation, noise reduction, and dead zone preservation was successively performed on each IVUS frame. To improve segmentation performance, the image masks were determined preliminarily by local binary pattern‐based mask initialisation. Then, the inner and outer borders were approximated using a modified distance regularised level set evolution model. The results showed superior performance of the suggested approach for estimating intima and MA layers from the IVUS images. The corresponding correlation coefficients of area, vessel perimeter, maximum vessel diameter, and maximum lumen diameter were r = 0.782, r = 0.716, r = 0.956, and r = 0.874 for the 20 MHz images, respectively, and r = 0.990, r = 0.995, r = 0.989, and r = 0.996 for the 45 MHz images, respectively. In addition, linear regression analysis indicated that the manual segmentation had significantly high similarity at r > 0.967 and r > 0.993 for 20 and 45 MHz images, respectively. [ABSTRACT FROM AUTHOR]

Speckle noise is one of the major artefacts in ultrasound images. The denoising faces the trade‐off between noise suppression and structural preservation. In this study, multiscale adaptive regularisation Savitzky–Golay (MARSG) method, the new filter for removing speckle noise, is proposed. The proposed method combines the benefit of the multiscale analysis and the outstanding noise removing capability of Savitzky–Golay (SG) filter. The Laplacian pyramid is employed to separate an image into the noise, texture and object layers. Adaptive regularisation Savitzky–Golay (ARSG) filter is developed as the denoising filter in the noise and the texture layers. The denoising of the ARSG filter is adaptively adjusted in order to preserve the edges of objects in the image. The experiments on the synthetic and ultrasound images demonstrated that MARSG method offered better balance between noise removal and structural preservation than non‐linear multiscale wavelet diffusion, feature‐enhanced speckle reduction and regularised SG filter. [ABSTRACT FROM AUTHOR]

As a particular case of the finite mixture model, Rayleigh mixture model (RMM) is considered as a useful tool for medical ultrasound (US) image segmentation. However, conventional RMM relies on intensity distribution only and does not take any spatial information into account that leads to misclassification on boundaries and inhomogeneous regions. The authors proposed an improved RMM with neighbour (RMMN) information to solve this problem by introducing neighbourhood information through a mean template. The incorporation of the spatial information made RMMN more robust to noise on the boundaries. The size of the window which incorporates neighbour information was resized adaptively according to the local gradient distribution. They evaluated their model on experiments on synthetic data and real US images used by high‐intensity focused ultrasound therapy. On this data, they demonstrated that the proposed model outperforms several state‐of‐the‐art methods in terms of both segmentation accuracy and computation time. [ABSTRACT FROM AUTHOR]

In laparoscopic surgery, the surgeon must operate with a limited field of view and reduced depth perception. This makes spatial understanding of critical structures difficult, such as an endophytic tumour in a partial nephrectomy. Such tumours yield a high complication rate of 47%, and excising them increases the risk of cutting into the kidney's collecting system. To overcome these challenges, an augmented reality guidance system is proposed. Using intra-operative ultrasound, a single navigation aid, and surgical instrument tracking, four augmentations of guidance information are provided during tumour excision. Qualitative and quantitative system benefits are measured in simulated robot-assisted partial nephrectomies. Robot-to-camera calibration achieved a total registration error of 1.0 ± 0.4 mm while the total system error is 2.5 ± 0.5 mm. The system significantly reduced healthy tissue excised from an average (±standard deviation) of 30.6 ± 5.5 to 17.5 ± 2.4 cm^3 (p < 0.05) and reduced the depth from the tumor underside to cut from an average (±standard deviation) of 10.2 ± 4.1 to 3.3 ± 2.3 mm (p < 0.05). Further evaluation is required in vivo, but the system has promising potential to reduce the amount of healthy parenchymal tissue excised.

Shear waves propagating through interfaces where there is a change in stiffness cause reflected waves that can lead to artifacts in shear wave speed (SWS) reconstructions. Two-dimensional (2-D) directional filters are commonly used to reduce in-plane reflected waves; however, SWS artifacts arise from both in- and out-of-imaging-plane reflected waves. Herein, we introduce 3-D shear wave reconstruction methods as an extension of the previous 2-D estimation methods and quantify the reduction in image artifacts through the use of volumetric SWS monitoring and 4-D-directional filters. A Gaussian acoustic radiation force impulse excitation was simulated in phantoms with Young’s modulus ( $E$ ) of 3 kPa and a 5-mm spherical lesion with $E = 6$ , 12, or 18.75 kPa. The 2-D-, 3-D-, and 4-D-directional filters were applied to the displacement profiles to reduce in-and out-of-plane reflected wave artifacts. Contrast-to-noise ratio and SWS bias within the lesion were calculated for each reconstructed SWS image to evaluate the image quality. For 2-D SWS image reconstructions, the 3-D-directional filters showed greater improvements in image quality than the 2-D filters, and the 4-D-directional filters showed marginal improvement over the 3-D filters. Although 4-D-directional filters can further reduce the impact of large magnitude out-of-plane reflection artifacts in SWS images, computational overhead and transducer costs to acquire 3-D data may outweigh the modest improvements in image quality. The 4-D-directional filters have the largest impact in reducing reflection artifacts in 3-D SWS volumes. [ABSTRACT FROM PUBLISHER]

The aim of this study was to investigate the relationships between selected quantitative ultrasound (QUS) parameters and human femur microstructure properties, as quantified by micro‐computed tomography (micro‐CT). The authors employed an innovative custom‐designed experimental set‐up, which allowed the insonification of each portion of an excised femoral head sample, simultaneously including trabecular region, cortical layer and cartilage in their physiologic morphological configuration. Thirty different, uniformly distributed, regions of interest were analysed for the calculation of apparent integrated backscatter (AIB), integrated reflection coefficient (IRC) and several micro‐CT parameters. QUS data acquisitions were performed through both single‐element ultrasound transducers at two different frequencies (2.25 and 3.5 MHz) and a clinically available 128‐element echographic probe. Obtained results showed that AIB was strongly correlated with trabecular network properties (|r| up to 0.80) and IRC had appreciable linear correlations with cortical bone density (|r| up to 0.57). The agreement between single‐element transducers and echographic probe, combined with the innovative approach of considering the entire femoral head in its physiological shape with all its components (cartilage, cortical layer, trabecular region), encourages the clinical translation of the proposed approach as a possible new method for early osteoporosis diagnosis. [ABSTRACT FROM AUTHOR]

A low noise, low power, single‐ended to differential switched‐capacitor variable gain amplifier (SC‐VGA) is designed and fabricated in 0.18 µm complementary metal‐oxide‐semiconductor technology for a 2–6 MHz second harmonic cardiac ultrasound imaging system. The SC‐VGA has 10‐bit dB‐linear gain steps from −14 to 32 dB which are distributed into two stages, and each stage has a single‐stage operational trans‐conductance amplifier using floating capacitors to save the power and improve the noise performance. The first stage converts the single‐ended input to differential outputs with the 2‐bit gain control from 0 to 18 dB, and the second stage exploits 8‐bit capacitor arrays to control the gain from −14 to 14 dB. The measured results show that the second harmonic distortion is <−50 dB, the third harmonic distortion is <−50 dB and the integrated noise from 2 to 6 MHz at the output is −64 dBm at the maximum gain and a sampling frequency of 30 MHz. The simulation results match well with the measured results. The SC‐VGA works at a supply voltage of 1.6 V and consumes the current of 900 µA. The die size of the SC‐VGA is 387 µm × 502 µm. [ABSTRACT FROM AUTHOR]

Aim of this study was to perform a detailed clinical validation of a new fully automatic algorithm for vertebral interface segmentation in echographic images. Abdominal echographic scans of lumbar vertebrae L1–L4 were carried out on 150 female subjects with variable age and body mass index (BMI). Acquired datasets were automatically processed by the algorithm and the accuracy of the obtained segmentations was then evaluated by three independent experienced operators. Obtained results showed a very good specificity in vertebra detection (93.3%), coupled with a reasonable sensitivity (68.1%), representing a suitable compromise between the detection of a sufficient number of vertebrae for reliable diagnoses and the limitation of the corresponding computation time. Importantly, there was only a minimum presence of 'false vertebrae' detected (2.8%), resulting in a very low influence on subsequent diagnostic analyses. Furthermore, the algorithm was specifically tuned to provide an improved sensitivity (up to 73.1%) with increasing patient BMI, to keep a suitable number of correctly detected vertebrae even when the acquisition was intrinsically more difficult because of the augmented thickness of abdominal soft tissues. The proposed algorithm will represent an essential added value for developing echographic methods for the diagnosis of osteoporosis on lumbar vertebrae. [ABSTRACT FROM AUTHOR]

The aim of this study is to assess the accuracy of a novel ultrasound (US) approach for lumbar spine densitometry on overweight and obese women of variable age through a clinical validation study. The US method was originally developed in women with body mass index (BMI) < 25 kg/m2. In this study, 382 female patients were recruited (45–80 years, BMI > 25 kg/m2) and underwent dual X‐ray absorptiometry (DXA) of lumbar spine (L1–L4) and an US scan of the same vertebrae L1–L4, performed with a dedicated device providing both echographic images and 'raw' radiofrequency signals. Acquired US data were analysed through a novel automatic algorithm that performed a series of spectral and statistical analyses to calculate bone mineral density employing an innovative method. Diagnostic accuracy of US investigations was quantitatively assessed through a direct comparison with DXA results. The average agreement between US and DXA diagnoses was acceptable for patients aged 45–65 years (81.5%), while a slight decrement was observed for older patients (69.6%), which can be partially due to a decrease in DXA accuracy because of age‐related degenerations. The adopted method has a potential for early osteoporosis diagnosis in people younger than 65 years, independent of their BMI. [ABSTRACT FROM AUTHOR]

In this Letter, an auto‐tuning system based on a variable synchronous switched capacitor is proposed for a phased‐array high‐intensity focused ultrasound system. The proposed system can automatically compensate the reactive power and impedance drift of each transducer caused by manufacturing tolerances and temperature variations. In contrast to traditional solutions, this system does not require measuring the impedances of each transducer or need microcontroller units to calculate the switch control signals. Furthermore, by using an air‐core inductor rather than a ferromagnetic‐core inductor in the auto‐tuning system, it naturally complies with the magnetic resonance imaging compatibility regulations. [ABSTRACT FROM AUTHOR]

As one of the key technologies in ultrasound (US)‐guided high‐intensity focused ultrasound (HIFU) ablation systems, a precise and automatic US image segmentation method for tumour localisation can contribute to ablation of the diseased tissues and avoiding unwanted destruction of the healthy tissues. Owing to the speckle noise and the irregular shapes of target tumours in US images, traditional image segmentation methods are not suitable for tumour localisation. In this study, the authors proposed an improved gradient and direction vector flow (G&DVF) model to segment US images for tumour localisation. The conventional G&DVF model was improved in three aspects to obtain a better segmentation for tumour localisation. The straight lines were changed into fold lines to increase their flexibility in guiding the active contour. Several weighting parameters were added to the energy functional to control the influences of these different lines. Moreover, a novel vector field was defined to reduce the computational complexity and save the computation time. With these three aspects improved, the improved G&DVF model was applied in US image segmentation for tumour localisation. The experimental results demonstrate that this proposed model is reliable, accurate and time saving in US image segmentation for tumour localisation. [ABSTRACT FROM AUTHOR]

A wavelet‐based technique for blind deconvolution and denoising of ultrasound scans is introduced. The target application is near‐field ultrasound imaging for non‐destructive testing. Existing blind deconvolution techniques for ultrasound such as cepstrum‐based methods and the work of Adam and Michailovich – based on discrete wavelet transform (DWT) shrinkage of the log‐spectrum – estimate the pulse by exploiting the pulse log‐spectrum smoothness relative to the material reflectivity function. In the proposed technique, the log‐spectrum is localised with respect to time as the continuous wavelet transform (CWT) log‐scalogram to deal with the non‐stationarity of the near‐field ultrasound signals in both the pulse estimation and deconvolution. The pulse is estimated in the wavelet domain via DWT shrinkage of the log‐scalogram and is deconvolved by wavelet‐domain Wiener filtering. Extensions of the proposed technique include: using separate CWT domains for estimation and deconvolution, as inspired by the WienerChop denoising method; and training the algorithm parameters on a subset of scans. [ABSTRACT FROM AUTHOR]

Ultrasound images often exhibit poor signal to noise ratio when compared with optical images, because of the presence multiplicative speckle noise. Speckle suppression is often carried out as a pre‐processing step to aid in diagnosis using ultrasound images. In this study, dual tree complex wavelet transform‐based Levy Shrink algorithm is proposed for denoising ultrasound images. The coefficients in each wavelet subband are modelled using a heavy tailed Levy distribution. The scale parameters of the Levy distribution are estimated using fractional moments. Within this framework, a Bayesian estimator is employed to denoise ultrasound images. The proposed Levy Shrink algorithm is verified using evaluation parameters such as peak signal to noise ratio, mean structural similarity index, correlation coefficient and equivalent number of looks. The efficiency of the proposed denoising algorithm is justified by conducting extensive experiments on real as well as simulated ultrasound images. [ABSTRACT FROM AUTHOR]

Preliminary diagnosis based on ultrasound scanning is the first step in the treatment of many abdominal diseases. The noisy nature of the ultrasound image coupled with minimal contrasting features complicates the task of automatic classification if not impossible. This study presents a segmentation‐based approach to automatic classification of ten types of diffused and focal liver diseases from ultrasound images. A novel approach using Isocontour Segmentation based on Marching Squares, a computer graphics algorithm is presented. GLCM and fractal features are extracted from the segmented ultrasound images and classified using support vector machines and artificial neural networks (ANN) and the results are analysed. An overall classification accuracy of 92% is achieved using fractal features and ANN. [ABSTRACT FROM AUTHOR]

Over the last decade, different micro‐tool navigation and localisation algorithms have been developed. They can be divided into three groups: the eigen‐decomposition methods such as principal component analysis (PCA), the transform methods such as Hough transform (HT) and the space random iteration methods such as the random sample consensus (RANSAC) algorithm. To suppress the speckle noise of the ultrasound image, different noise suppression methods are also proposed. In this article, different combinations of preprocessing methods and localisation methods are compared. A tracking system is also developed to adapt these localisation methods to a dynamic situation. So far, the line‐filter method has achieved the best contrast ratio, and the threshold method requires the shortest time. These two preprocessing methods are proposed in the localisation algorithm and the tracking system. Simulations and experiments were conducted to verify the combinations of the localisation and tracking results. In static localisation, the line‐filter+RANSAC method achieves the highest localisation accuracy. In the dynamic situation, the PCA method achieves the highest tracking accuracy. In the real‐time evaluation, the calculation time of the RANSAC algorithm is the shortest. To satisfy the demand for localisation accuracy and calculation time, the line‐filter+RANSAC algorithm is the best choice. [ABSTRACT FROM AUTHOR]

Speckle filtering is of great interest for the ultrasound medical images in which various noises and artefacts are introduced because of various limitations of the acquisition systems and techniques. Speckle is a prime factor to degrade the quality and most importantly, texture information present in the ultrasound images. This study presents a despeckling method based on a modified non‐linear diffusion model and non‐subsampled shearlet transform (NSST). As a new image representation method with the different features of localisation, directionality and multiscale, the NSST is utilised to provide the effective representation of the image coefficients. The modified anisotropic diffusion is applied to the noisy coarser NSST coefficients to improve the denoising efficiency and preserve the edge features effectively. In the diffusion process, the non‐local pixel information is incorporated to evaluate the gradient of eight connected neighbouring pixels with an adaptive grey variance. The performance of the proposed method is evaluated for both the standard test and real ultrasound images. Experimental results show that the proposed method produces better results of noise suppression with the preservation of more edges compared with several existing methods. [ABSTRACT FROM AUTHOR]

This Letter presents a 12b-control ultra-low-power low-noise two-stage single-ended to differential switched-capacitor variable gain amplifier (SC-VGA) for 2–6-MHz second harmonic cardiac imaging ultrasound probes in 0.18 μm complementary metal oxide semiconductor. The proposed SC-VGA consists of inverters and capacitor (CAP) arrays. By adopting inverters instead of operational trans-conductance amplifiers (OTAs) in traditional SC-amplifiers, both the power and noise are significantly improved. Each stage has a 6b binary-weighted CAP array, and in total the 12b CAP arrays achieve the dB-in-linear gain range from − 21 to 21 dB. The CAP array is divided between the upper 3b and lower 3b by a CAP to decrease the capacitance spread. The total power consumption is 150 μA at 1 V supply voltage, and the input referred noise is 6.5 nV/√HZ at 4 MHz. The second harmonic distortion (HD2) has the mean value − 77 dB at the 460 mV peak-to-peak output swing for 50 samples of Monte Carlo mismatch simulation with a 30 MHz sampling frequency.

Medical image fusion is the process of integrating two medical images with a visual enhanced single fused image, to attain a resultant image richer in information to aid medical practitioners in better diagnosis. A perceptual medical image fusion method is proposed by employing Internal Generative Mechanism. First, source images are divided into a predicted layer and a detail layer with a Bayesian prediction model. Then, the detail layer is merged with the energy of Tchebichef moments for blocks while the predicted layer is fused using the averaging strategy as activity level measurement. The fused image is finally obtained by merging coefficients in both fused layers. Experimental results prove that the proposed fusion algorithm is superior to the previously developed methods. [ABSTRACT FROM AUTHOR]

The demand of the fast acquisition in 3D ultrasound (3D‐US) imaging leads to the dense population of speckle noise which causes the difficulty in medical diagnosis. In this Letter, 3Dadaptive regularisation Savitzky–Golay (3D ARSG) filter is proposed as the real‐time filter for removing speckle noise in 3D‐US imaging. The 3D ARSG filter uses the local intensity homogeneity to classify between noisy and texture regions. The intensity in the noisy regions is heavily smoothed, while the resolvable edges remain sharp. The experiment on the denoising of the 3D simulated models and the 3D‐US images indicated that 3D ARSG filter provided the image with far less noise and crisper objects' boundary than conventional methods. In addition, the proposed filter preserves the structural details with regard to the dimension and can denoise images in real time. [ABSTRACT FROM AUTHOR]

This paper proposes a new electronic mobility cane (EMC) for providing obstacle detection and way-finding assistance to the visually impaired people. The main feature of this cane is that it constructs the logical map of the surrounding environment to deduce the priority information. It provides a simplified representation of the surrounding environment without causing any information overload. It conveys this priority information to the subject by using intuitive vibration, audio or voice feedback. The other novel features of the EMC are staircase detection and nonformal distance scaling scheme. It also provides information about the floor status. It consists of a low power embedded system with ultrasonic sensors and safety indicators. The EMC was subjected to series of clinical evaluations in order to verify its design and to assess its ability to assist the subjects in their daily-life mobility. Clinical evaluations were performed with 16 totally blind and four low vision subjects. All subjects walked controlled and the real-world test environments with the EMC and the traditional white cane. The evaluation results and significant scores of subjective measurements have shown the usefulness of the EMC in vision rehabilitation services. [ABSTRACT FROM AUTHOR]

Managing gait disturbances in people with Parkinson's disease is a pressing challenge, as symptoms can contribute to injury and morbidity through an increased risk of falls. While drug-based interventions have limited efficacy in alleviating gait impairments, certain nonpharmacological methods, such as cueing, can also induce transient improvements to gait. The approach adopted here is to use computationally-generated sounds to help guide and improve walking actions. The first method described uses recordings of force data taken from the steps of a healthy adult which in turn were used to synthesize realistic gravel-footstep sounds that represented different spatio-temporal parameters of gait, such as step duration and step length. The second method described involves a novel method of sonifying, in real time, the swing phase of gait using real-time motion-capture data to control a sound synthesis engine. Both approaches explore how simple but rich auditory representations of action based events can be used by people with Parkinson's to guide and improve the quality of their walking, reducing the risk of falls and injury. Studies with Parkinson's disease patients are reported which show positive results for both techniques in reducing step length variability. Potential future directions for how these sound approaches can be used to manage gait disturbances in Parkinson's are also discussed. [ABSTRACT FROM AUTHOR]

This paper presents a bipolar symmetric output switching dc-dc converter for digital pulse generators in ultrasound medical imaging systems. The proposed power stage architecture can generate symmetric bipolar supply voltages, while operating in a time-interleaving manner. Meanwhile, in order to power unipolar pulse generators with the same peak-to-peak pulse amplitude, the power stage can also be reconfigured to deliver a 2× unipolar output voltage. The power converter employs an adaptive ΔI charge balance control scheme to ensure that the outputs are symmetrically balanced and well regulated. At the circuit level, to utilize a single feedback controller for both power stage topologies, the assignment of a suitable chip ground is discussed. A prototype is fabricated using a 0.25-μm CMOS process and occupies a chip area of 6 mm 2. For an input supply voltage of 1.5 V, the dc-dc converter can regulate the bipolar outputs at a nominal voltage level of ±2.5 V and at 5 V for the unipolar output configuration and a maximum load current of 45 mA for both. The bipolar output power converter achieves a maximum efficiency of 83.4% at a load power of 187.5 mW, with the voltage balance error maintained within ± 2% over the entire load range. [ABSTRACT FROM AUTHOR]

Photoacoustic tomography (PAT) is one of the fastest growing biomedical imaging modalities in the last decade. Building on its high scalability and complementary imaging contrast to other mainstream modalities, PAT has gained substantial momentum in both preclinical and clinical studies. In 2013, PAT has grown markedly in both its technological capabilities and biomedical applications. In particular, breakthroughs have been made in super-resolution imaging, deep blood flow measurement, small animal resting state brain mapping, video rate functional human imaging, and human breast imaging. These breakthroughs have either successfully solved long-standing technical issues in PAT or significantly enhanced its imaging capability. This review will summarize state-of-the-art developments in PAT and highlight a few representative achievements of the year 2013. [ABSTRACT FROM PUBLISHER]

An electrophoretic deposition (EPD) process with high deposition rate was used to fabricate a curved piezoelectric thick film devoted to high-frequency transducers for medical imaging. Niobium-doped lead zirconate titanate (PZTNb) powder was stabilized in ethanol to prepare a suspension with high zeta potential and low conductivity. A gold layer, pad-printed and fired on a curved porous PZT substrate, was used as the working electrode for the deposition of the PZTNb thick film. This substrate was chosen because it has the required properties (acoustic impedance and attenuation) to be used directly as a backing for the high-frequency transducer, leading to a simplified process for transducer assembly with this integrated structure. PZT-Nb thick films were also deposited by EPD on flat gold-coated alumina substrates as a reference. The thickness of the films was between 20 and 35 μm, and their electromechanical performance was comparable to standard PZT bulk ceramics with a thickness coupling factor of 48%. For the curved thick film, the thickness coupling factor was slightly lower. The corresponding integrated structure was used to fabricate a transducer with a center frequency of 40 MHz and an f-number of 2.8. It was integrated into a realtime ultrasound scanner and used to image human forearm skin; the resulting images showed, for the first time, the efficacy of the EPD process for these imaging applications. [ABSTRACT FROM AUTHOR]

Photoacoustic imaging, frequently coregistered with ultrasonic imaging, can provide functional and cellular/ molecular information about tissue within the anatomical landmarks of an imaged region. This review details the fundamentals of photoacoustic imaging and its most promising imaging applications. Particular attention is paid to photoacoustic imaging's relationship with ultrasound, focusing on distinct differences and similarities between the two modalities and highlighting the mutual benefit of using both concurrently in certain preclinical and clinical applications. Much like its origins as an imaging modality were intertwined with ultrasonic imaging (namely, its acoustic transducers and hardware), the future of photoacoustic imaging-particularly in the clinical arena-similarly depends on ultrasound and its time-tested ability to provide real-time visualization of soft tissue. [ABSTRACT FROM AUTHOR]

In vivo ultrasonic imaging with transducer arrays suffers from image degradation resulting from beamforming limitations, including diffraction-limited beamforming and beamforming degradation caused by tissue inhomogeneity. Additionally, based on recent studies, multipath scattering also causes significant image degradation. To reduce degradation from both sources, we propose a model-based signal decomposition scheme. The proposed algorithm identifies spatial frequency signatures to decompose received wavefronts into their most significant scattering sources. Scattering sources originating from a region of interest are used to reconstruct decluttered wavefronts, which are beamformed into decluttered RF scan lines or A-lines. To test the algorithm, ultrasound system channel data were acquired during liver scans from 8 patients. Multiple data sets were acquired from each patient, with 55 total data sets, 43 of which had identifiable hypoechoic regions on normal B-mode images. The data sets with identifiable hypoechoic regions were analyzed. The results show the decluttered B-mode images have an average improvement in contrast over normal images of 7.3 ± 4.6 dB. The contrast-to-noise ratio (CNR) changed little on average between normal and decluttered Bmode, -0.4 ± 5.9 dB. The in vivo speckle SNR decreased; the change was -0.65 ± 0.28. Phantom speckle SNR also decreased, but only by -0.40 ± 0.03. [ABSTRACT FROM AUTHOR]

Ultrasound current source density imaging (UCSDI), which has application to the heart and brain, exploits the acoustoelectric (AE) effect and Ohm's law to detect and map an electrical current distribution. In this study, we describe 4-D UCSDI simulations of a dipole field for comparison and validation with bench-top experiments. The simulations consider the properties of the ultrasound pulse as it passes through a conductive medium, the electric field of the injected dipole, and the lead field of the detectors. In the simulation, the lead fields of detectors and electric field of the dipole were calculated by the finite element (FE) method, and the convolution and correlation in the computation of the detected AE voltage signal were accelerated using 3-D fast Fourier transforms. In the bench-top experiment, an electric dipole was produced in a bath of 0.9% NaCl solution containing two electrodes, which injected an ac pulse (200 Hz, 3 cycles) ranging from 0 to 140 mA. Stimulating and recording electrodes were placed in a custom electrode chamber made on a rapid prototype printer. Each electrode could be positioned anywhere on an x-y grid (5 mm spacing) and individually adjusted in the depth direction for precise control of the geometry of the current sources and detecting electrodes. A 1-MHz ultrasound beam was pulsed and focused through a plastic film to modulate the current distribution inside the saline-filled tank. AE signals were simultaneously detected at a sampling frequency of 15 MHz on multiple recording electrodes. A single recording electrode is sufficient to form volume images of the current flow and electric potentials. The AE potential is sensitive to the distance from the dipole, but is less sensitive to the angle between the detector and the dipole. Multi-channel UCSDI potentially improves 4-D mapping of bioelectric sources in the body at high spatial resolution, which is especially important for diagnosing and guiding treatment of cardiac and neurologic disorders, including arrhythmia and epilepsy. [ABSTRACT FROM AUTHOR]