Multi-Frequency 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) System for the Prostate.

This article describes a novel system for quantitative and volumetric measurement of tissue elasticity in the prostate using simultaneous multi-frequency tissue excitation. Elasticity is computed by using a local frequency estimator to measure the three-dimensional local wavelengths of steady-state shear waves within the prostate gland. The shear wave is created using a mechanical voice coil shaker which transmits simultaneous multi-frequency vibrations transperineally. Radio frequency data is streamed directly from a BK Medical 8848 transrectal ultrasound transducer to an external computer where tissue displacement due to the excitation is measured using a speckle tracking algorithm. Bandpass sampling is used that eliminates the need for an ultra-fast frame rate to track the tissue motion and allows for accurate reconstruction at a sampling frequency that is below the Nyquist rate. A roll motor with computer control is used to rotate the transducer and obtain 3D data. Two commercially available phantoms were used to validate both the accuracy of the elasticity measurements as well as the functional feasibility of using the system for in vivo prostate imaging. The phantom measurements were compared with 3D Magnetic Resonance Elastography (MRE), where a high correlation of 96% was achieved. In addition, the system has been used in two separate clinical studies as a method for cancer identification. Qualitative and quantitative results of 11 patients from these clinical studies are presented here. Furthermore, an AUC of 0.87±0.12 was achieved for malignant vs. benign classification using a binary support vector machine classifier trained with data from the latest clinical study with leave one patient out cross-validation.

Automatic measurement of kidney dimensions in two-dimensional ultrasonography is comparable to expert sonographers.

Purpose: Length and width measurements of the kidneys aid in the detection and monitoring of structural abnormalities and organ disease. Manual measurement results in intra- and inter-rater variability, is complex and time-consuming, and is fraught with error. We propose an automated approach based on machine learning for quantifying kidney dimensions from two-dimensional (2D) ultrasound images in both native and transplanted kidneys. Approach: An nnU-net machine learning model was trained on 514 images to segment the kidney capsule in standard longitudinal and transverse views. Two expert sonographers and three medical students manually measured the maximal kidney length and width in 132 ultrasound cines. The segmentation algorithm was then applied to the same cines, region fitting was performed, and the maximum kidney length and width were measured. Additionally, single kidney volume for 16 patients was estimated using either manual or automatic measurements. Results: The experts resulted in length of 84.8±26.4 mm [95% CI: 80.0, 89.6] and a width of 51.8±10.5 mm [49.9, 53.7]. The algorithm resulted a length of 86.3±24.4 [81.5, 91.1] and a width of 47.1±12.8 [43.6, 50.6]. Experts, novices, and the algorithm did not statistically significant differ from one another (p>0.05). Bland-Altman analysis showed the algorithm produced a mean difference of 2.6 mm (SD = 1.2) from experts, compared to novices who had a mean difference of 3.7 mm (SD = 2.9 mm). For volumes, mean absolute difference was 47 mL (31%) consistent with ∼1 mm error in all three dimensions. Conclusions: This pilot study demonstrates the feasibility of an automatic tool to measure in vivo kidney biometrics of length, width, and volume from standard 2D ultrasound views with comparable accuracy and reproducibility to expert sonographers. Such a tool may enhance workplace efficiency, assist novices, and aid in tracking disease progression.

3-D Ultrafast Shear Wave Absolute Vibro-Elastography Using a Matrix Array Transducer.

Real-time ultrasound imaging plays an important role in ultrasound-guided interventions. The 3-D imaging provides more spatial information compared to conventional 2-D frames by considering the volumes of data. One of the main bottlenecks of 3-D imaging is the long data acquisition time, which reduces practicality and can introduce artifacts from unwanted patient or sonographer motion. This article introduces the first shear wave absolute vibro-elastography (S-WAVE) method with real-time volumetric acquisition using a matrix array transducer. In S-WAVE, an external vibration source generates mechanical vibrations inside the tissue. The tissue motion is then estimated and used in solving a wave equation inverse problem to provide the tissue elasticity. A matrix array transducer is used with a Verasonics ultrasound machine and a frame rate of 2000 volumes/s to acquire 100 radio frequency (RF) volumes in 0.05 s. Using plane wave (PW) and compounded diverging wave (CDW) imaging methods, we estimate axial, lateral, and elevational displacements over 3-D volumes. The curl of the displacements is used with local frequency estimation to estimate elasticity in the acquired volumes. Ultrafast acquisition extends substantially the possible S-WAVE excitation frequency range, now up to 800 Hz, enabling new tissue modeling and characterization. The method was validated on three homogeneous liver fibrosis phantoms and on four different inclusions within a heterogeneous phantom. The homogeneous phantom results show less than 8% (PW) and 5% (CDW) difference between the manufacturer values and the corresponding estimated values over a frequency range of 80-800 Hz. The estimated elasticity values for the heterogeneous phantom at 400-Hz excitation frequency show the average errors of 9% (PW) and 6% (CDW) compared to the provided average values by magnetic resonance elastography (MRE). Furthermore, both imaging methods were able to detect the inclusions within the elasticity volumes. An ex vivo study on a bovine liver sample shows less than 11% (PW) and 9% (CDW) difference between the estimated elasticity ranges by the proposed method and the elasticity ranges provided by MRE and acoustic radiation force impulse (ARFI).

3-D Transducer Mounted Shear Wave Absolute Vibro-Elastography: Proof of Concept.

Quantitative tissue stiffness characterization using ultrasound (US) has been shown to improve prostate cancer (PCa) detection in multiple studies. Shear wave absolute vibro-elastography (SWAVE) allows quantitative and volumetric assessment of tissue stiffness using external multifrequency excitation. This article presents a proof of concept of a first-of-a-kind 3-D hand-operated endorectal SWAVE system designed to be used during systematic prostate biopsy. The system is developed with a clinical US machine, requiring only an external exciter that can be mounted directly to the transducer. Subsector acquisition of radio frequency (RF) data allows imaging of shear waves with a high effective frame rate (up to 250 Hz). The system was characterized using eight different quality assurance phantoms. Due to the invasive nature of prostate imaging, at this early stage of development, validation of in vivo human tissue was instead carried out by intercostally scanning the livers of n = 7 healthy volunteers. The results are compared with 3-D magnetic resonance elastography (MRE) and an existing 3-D SWAVE system with a matrix array transducer (M-SWAVE). High correlations were found with MRE (99% in phantoms, 94% in liver data) and with M-SWAVE (99% in phantoms, 98% in liver data).

The Kidneys Are Not All Normal: Transplanted Kidneys and Their Speckle Distributions.

OBJECTIVE: Modelling ultrasound speckle to characterise tissue properties has generated considerable interest. As speckle is dependent on the underlying tissue architecture, modelling it may aid in tasks such as segmentation or disease detection. For the transplanted kidney, where ultrasound is used to investigate dysfunction, it is unknown which statistical distribution best characterises such speckle. This applies to the regions of the transplanted kidney: the cortex, the medulla and the central echogenic complex. Furthermore, it is unclear how these distributions vary by patient variables such as age, sex, body mass index, primary disease or donor type. These traits may influence speckle modelling given their influence on kidney anatomy. We investigate these two aims. METHODS: B-mode images from n = 821 kidney transplant recipients (one image per recipient) were automatically segmented into the cortex, medulla and central echogenic complex using a neural network. Seven distinct probability distributions were fitted to each region's histogram, and statistical analysis was performed. DISCUSSION: The Rayleigh and Nakagami distributions had model parameters that differed significantly between the three regions (p ≤ 0.05). Although both had excellent goodness of fit, the Nakagami had higher Kullbeck-Leibler divergence. Recipient age correlated weakly with scale in the cortex (Ω: ρ = 0.11, p = 0.004), while body mass index correlated weakly with shape in the medulla (m: ρ = 0.08, p = 0.04). Neither sex, primary disease nor donor type exhibited any correlation. CONCLUSION: We propose the Nakagami distribution be used to characterize transplanted kidneys regionally independent of disease etiology and most patient characteristics.

SWAVE 2.0 Imaging of Placental Elasticity and Viscosity: Potential Biomarkers for Placenta-Mediated Disease Detection.

Pregnancy complications such as pre-eclampsia (PE) and intrauterine growth restriction (IUGR) are associated with structural and functional changes in the placenta. Different elastography techniques with an ability to assess the mechanical properties of tissue can identify and monitor the pathological state of the placenta. Currently available elastography techniques have been used with promising results to detect placenta abnormalities; however, limitations include inadequate measurement depth and safety concerns from high negative pressure pulses. Previously, we described a shear wave absolute vibro-elastography (SWAVE) method by applying external low-frequency mechanical vibrations to generate shear waves and studied 61 post-delivery clinically normal placentas to explore the feasibility of SWAVE for placental assessment and establish a measurement baseline. This next phase of the study, namely, SWAVE 2.0, improves the previous system and elasticity reconstruction by incorporating a multi-frequency acquisition system and using a 3-D local frequency estimation (LFE) method. Compared with its 2-D counterpart, the proposed system using 3-D LFE was found to reduce the bias and variance in elasticity measurements in tissue-mimicking phantoms. In the aim of investigating the potential of improved SWAVE 2.0 measurements to identify placental abnormalities, we studied 46 post-delivery placentas, including 26 diseased (16 IUGR and 10 PE) and 20 normal control placentas. By use of a 3.33-MHz motorized curved-array transducer, multi-frequency (80,100 and 120 Hz) elasticity measures were obtained with 3-D LFE, and both IUGR (15.30 ± 2.96 kPa, p = 3.35e-5) and PE (12.33 ± 4.88 kPa, p = 0.017) placentas were found to be significantly stiffer compared with the control placentas (8.32 ± 3.67 kPa). A linear discriminant analysis (LDA) classifier was able to classify between healthy and diseased placentas with a sensitivity, specificity and accuracy of 87%, 78% and 83% and an area under the receiver operating curve of 0.90 (95% confidence interval: 0.8-0.99). Further, the pregnancy outcome in terms of neonatal intensive care unit admission was predicted with a sensitivity, specificity and accuracy of 70%, 71%, 71%, respectively, and area under the receiver operating curve of 0.78 (confidence interval: 0.62-0.93). A viscoelastic characterization of placentas using a fractional rheological model revealed that the viscosity measures in terms of viscosity parameter n were significantly higher in IUGR (2.3 ± 0.21) and PE (2.11 ± 0.52) placentas than in normal placentas (1.45 ± 0.65). This work illustrates the potential relevance of elasticity and viscosity imaging using SWAVE 2.0 as a non-invasive technology for detection of placental abnormalities and the prediction of pregnancy outcomes.

A novel ultrasound software system for lumbar level identification in obstetric patients.

PURPOSE: Using machine learning, we developed a proprietary ultrasound software called the Spine Level Identification (SLIDE) system, which automatically identifies lumbar landmarks in real time as the operator slides the transducer over the lumber spine. Here, we assessed the agreement between SLIDE and manual palpation and traditional lumbar ultrasound (LUS) for determining the primary target L3-4 interspace. METHODS: Upon institutional ethics approval and informed consent, 76 healthy term parturients scheduled for elective Caesarean delivery were recruited. The L3-4 interspace was identified by manual palpation and then by the SLIDE method. The reference standard was located using traditional LUS by an experienced operator. The primary outcome was the L3-4 interspace identification agreement of manual palpation and SLIDE with the reference standard, as percentage agreement and Gwet's agreement coefficient (AC1). RESULTS: The raw agreement was 70% with Gwet's agreement coefficient (AC1) = 0.59 (95% confidence interval [CI], 0.41 to 0.77) for manual palpation and 84% with Gwet's AC1 = 0.82 (95% CI, 0.70 to 0.93) for SLIDE. When the levels differ from the reference, the manual palpation method identified L2-3 more often than L4-5 while the SLIDE method identified equally above or below L3-4. The SLIDE system had greater agreement than palpation in locating L3-4 and all other lumber interspaces after controlling for body mass index (adjusted odds ratio, 2.99; 95% CI, 1.21 to 8.7; P = 0.02). CONCLUSION: The SLIDE system had higher agreement with traditional ultrasound than manual palpation did in identifying L3-4 and all other lumber interspaces after adjusting for BMI in healthy term obstetric patients. Future studies should examine factors that affect agreement and ways to improve SLIDE for clinical integration. STUDY REGISTRATION: www. CLINICALTRIALS: gov (NCT02982317); registered 5 December 2016.

RéSUMé: OBJECTIF: À l'aide de l'apprentissage automatique, nous avons développé un logiciel d'échographie propriétaire appelé SLIDE (pour Spine Level Identification, c.-à-d. système d'identification du niveau vertébral), qui identifie automatiquement les points de repère lombaires en temps réel lorsque l'opérateur fait passer le transducteur sur la colonne lombaire. Ici, nous avons évalué l'agrément entre le SLIDE et la palpation manuelle et l'échographie lombaire traditionnelle pour déterminer l'espace intervertébral cible principal L3­L4. MéTHODE: Après avoir obtenu l'approbation du comité d'éthique de l'établissement et le consentement éclairé, 76 parturientes en bonne santé et à terme devant bénéficier d'un accouchement par césarienne programmée ont été recrutées. L'espace intervertébral L3­L4 a été identifié par palpation manuelle puis avec le logiciel SLIDE. L'étalon de référence a été localisé à l'aide d'une échographie lombaire traditionnelle par un opérateur expérimenté. Le critère d'évaluation principal était l'agrément entre l'identification de l'espace intervertébral L3­L4 par palpation manuelle et par logiciel SLIDE avec l'étalon de référence, en pourcentage d'agrément et coefficient d'agrément de Gwet (CA1). RéSULTATS: L'agrément brut était de 70 % avec le coefficient d'agrément de Gwet (CA1) = 0,59 (intervalle de confiance [IC] à 95 %, 0,41 à 0,77) pour la palpation manuelle et de 84 % avec le CA1 de Gwet = 0,82 (IC 95 %, 0,70 à 0,93) pour le logiciel SLIDE. Lorsque les niveaux lombaires différaient de la référence, la méthode de palpation manuelle a identifié L2­L3 plus souvent que L4­L5, tandis que la méthode SLIDE a identifié les vertèbres supérieures ou inférieures à L3­L4 de manière égale. Le système SLIDE a affiché un agrément plus important que la palpation pour localiser L3­L4 et tous les autres espaces intervertébraux lombaires après ajustement pour tenir compte de l'indice de masse corporelle (rapport de cotes ajusté, 2,99; IC 95 %, 1,21 à 8,7; P = 0,02). CONCLUSION: Le système SLIDE avait affiché un agrément plus élevé avec l'échographie traditionnelle que la palpation manuelle pour identifier le niveau L3­L4 et tous les autres espaces intervertébraux lombaires après ajustement pour tenir compte de l'IMC chez les patientes obstétricales à terme en bonne santé. Une étude future devrait examiner les facteurs qui affectent l'agrément et les moyens d'améliorer le logiciel SLIDE pour une intégration clinique. ENREGISTREMENT DE L'éTUDE: www.clinicaltrials.gov (NCT02982317); enregistrée le 5 décembre 2016.

Project SWAVE 2.0: An overview of the study design for multimodal placental image acquisition and alignment.

Development of non-invasive and in utero placenta imaging techniques can potentially identify biomarkers of placental health. Correlative imaging using multiple multiscale modalities is particularly important to advance the understanding of placenta structure, function and their relationship. The objective of the project SWAVE 2.0 was to understand human placental structure and function and thereby identify quantifiable measures of placental health using a multimodal correlative approach. In this paper, we present a multimodal image acquisition protocol designed to acquire and align data from ex vivo placenta specimens derived from both healthy and complicated pregnancies. Qualitative and quantitative validation of the alignment method were performed. The qualitative analysis showed good correlation between findings in the MRI, ultrasound and histopathology images. The proposed protocol would enable future studies on comprehensive analysis of placental anatomy, function and their relationship. ● An overview of a novel multimodal placental image acquisition protocol is presented. ● A co-registration method using surface markers and external fiducials is described. ● A preliminary correlative imaging analysis for a placenta specimen is presented.

Kidney Ultrasound for Nephrologists: A Review.

Ultrasound imaging is a key investigatory step in the evaluation of chronic kidney disease and kidney transplantation. It uses nonionizing radiation, is noninvasive, and generates real-time images, making it the ideal initial radiographic test for patients with abnormal kidney function. Ultrasound enables the assessment of both structural (form and size) and functional (perfusion and patency) aspects of kidneys, both of which are especially important as the disease progresses. Ultrasound and its derivatives have been studied for their diagnostic and prognostic significance in chronic kidney disease and kidney transplantation. Ultrasound is rapidly growing more widely accessible and is now available even in handheld formats that allow for bedside ultrasound examinations. Given the trend toward ubiquity, the current use of kidney ultrasound demands a full understanding of its breadth as it and its variants become available. We described the current applications and future directions of ultrasound imaging and its variants in the context of chronic kidney disease and transplantation in this review.

Model-Based Quantitative Elasticity Reconstruction Using ADMM.

We introduce two model-based iterative methods to obtain shear modulus images of tissue using magnetic resonance elastography. The first method jointly finds the displacement field that best fits tissue displacement data and the corresponding shear modulus. The displacement satisfies a viscoelastic wave equation constraint, discretized using the finite element method. Sparsifying regularization terms in both shear modulus and displacement are used in the cost function minimized for the best fit. The second method extends the first method for multifrequency tissue displacement data. The formulated problems are bi-convex. Their solution can be obtained iteratively by using the alternating direction method of multipliers. Sparsifying regularizations and the wave equation constraint filter out sensor noise and compressional waves. Our methods do not require bandpass filtering as a preprocessing step and converge fast irrespective of the initialization. We evaluate our new methods in multiple in silico and phantom experiments, with comparisons with existing methods, and we show improvements in contrast to noise and signal-to-noise ratios. Results from an in vivo liver imaging study show elastograms with mean elasticity comparable to other values reported in the literature.

A Unified Representation of Control Logic in Human-Ultrasound Machine Interaction.

Advances in human-computer interaction (HCI) technologies have granted sonographers and radiologists a much improved user experience when operating different ultrasound (US) machines. Continued HCI improvements in US would benefit from a systematic study of the HCI control logic used in this domain. Such a study has not been presented previously and is the subject of this paper. We surveyed sonographers to determine the most frequently used controls in US machines. We standardized the representation of the US machine HCI control logic by using the unified modelling language (UML). We used UML diagrams to analyze the HCI control logic of 10 different cart-based US machines from several major manufacturers, and we discovered that the control logic for the most frequently used functions are identical. While this control logic does not follow an established standard, it has been commonly adopted. Using the UML for the visualization and formulation of control logic, we can target logically optimal interactions (whose operation steps cannot be further reduced), e.g., adjustment of B-mode gain, frequency and depth, and can derive methods to simplify logically sub-optimal interactions, e.g., the pointing and selecting operation, as well as image measurements.Our study provides insights into existing HCI approaches used in US machines and establishes a rigorous UML-based framework for future US machine design to improve interoperability, efficiency and ease-of-use.

Breast Cancer Detection Using Multimodal Time Series Features From Ultrasound Shear Wave Absolute Vibro-Elastography.

In shear wave absolute vibro-elastography (S-WAVE), a steady-state multi-frequency external mechanical excitation is applied to tissue, while a time-series of ultrasound radio-frequency (RF) data are acquired. Our objective is to determine the potential of S-WAVE to classify breast tissue lesions as malignant or benign. We present a new processing pipeline for feature-based classification of breast cancer using S-WAVE data, and we evaluate it on a new data set collected from 40 patients. Novel bi-spectral and Wigner spectrum features are computed directly from the RF time series and are combined with textural and spectral features from B-mode and elasticity images. The Random Forest permutation importance ranking and the Quadratic Mutual Information methods are used to reduce the number of features from 377 to 20. Support Vector Machines and Random Forest classifiers are used with leave-one-patient-out and Monte Carlo cross-validations. Classification results obtained for different feature sets are presented. Our best results (95% confidence interval, Area Under Curve = 95%±1.45%, sensitivity = 95%, and specificity = 93%) outperform the state-of-the-art reported S-WAVE breast cancer classification performance. The effect of feature selection and the sensitivity of the above classification results to changes in breast lesion contours is also studied. We demonstrate that time-series analysis of externally vibrated tissue as an elastography technique, even if the elasticity is not explicitly computed, has promise and should be pursued with larger patient datasets. Our study proposes novel directions in the field of elasticity imaging for tissue classification.

U-LanD: Uncertainty-Driven Video Landmark Detection.

This paper presents U-LanD, a framework for automatic detection of landmarks on key frames of the video by leveraging the uncertainty of landmark prediction. We tackle a specifically challenging problem, where training labels are noisy and highly sparse. U-LanD builds upon a pivotal observation: a deep Bayesian landmark detector solely trained on key video frames, has significantly lower predictive uncertainty on those frames vs. other frames in videos. We use this observation as an unsupervised signal to automatically recognize key frames on which we detect landmarks. As a test-bed for our framework, we use ultrasound imaging videos of the heart, where sparse and noisy clinical labels are only available for a single frame in each video. Using data from 4,493 patients, we demonstrate that U-LanD can exceedingly outperform the state-of-the-art non-Bayesian counterpart by a noticeable absolute margin of 42% in R2 score, with almost no overhead imposed on the model size.

A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization.

Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS) method, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical 1/N rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.

Investigation of photoacoustic tomography reconstruction with a limited view from linear array.

SIGNIFICANCE: As linear array transducers are widely used in clinical ultrasound imaging, photoacoustic tomography (PAT) with linear arrays is similarly suitable for clinical applications. However, due to the limited-view problem, a linear array has limited performance and leads to artifacts and blurring, which has hindered its broader application. There is a need to address the limited-view problem in PAT imaging with linear arrays. AIM: We investigate potential approaches for improving PAT reconstruction from linear array, by optimizing the detection geometry and implementing iterative reconstruction. APPROACH: PAT imaging with a single-array, dual-probe configurations in parallel-shape and L-shape, and square-shape configuration are compared in simulations and phantom experiments. An iterative model-based algorithm based on the variance-reduced stochastic gradient descent (VR-SGD) method is implemented. The optimum configuration found in simulation is validated on phantom experiments. RESULTS: PAT imaging with dual-probe detection and VR-SGD algorithm is found to improve the limited-view problem compared to a single probe and provide comparable performance as full-view geometry in simulation. This configuration is validated in experiments where more complete structure is obtained with reduced artifacts compared with a single array. CONCLUSIONS: PAT with dual-probe detection and iterative reconstruction is a promising solution to the limited-view problem of linear arrays.

Echo-SyncNet: Self-Supervised Cardiac View Synchronization in Echocardiography.

In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements. However, in emergencies or point-of-care situations, acquiring an ECG is often not an option, hence motivating the need for alternative temporal synchronization methods. Here, we propose Echo-SyncNet, a self-supervised learning framework to synchronize various cross-sectional 2D echo series without any human supervision or external inputs. The proposed framework takes advantage of two types of supervisory signals derived from the input data: spatiotemporal patterns found between the frames of a single cine (intra-view self-supervision) and interdependencies between multiple cines (inter-view self-supervision). The combined supervisory signals are used to learn a feature-rich and low dimensional embedding space where multiple echo cines can be temporally synchronized. Two intra-view self-supervisions are used, the first is based on the information encoded by the temporal ordering of a cine (temporal intra-view) and the second on the spatial similarities between nearby frames (spatial intra-view). The inter-view self-supervision is used to promote the learning of similar embeddings for frames captured from the same cardiac phase in different echo views. We evaluate the framework with multiple experiments: 1) Using data from 998 patients, Echo-SyncNet shows promising results for synchronizing Apical 2 chamber and Apical 4 chamber cardiac views, which are acquired spatially perpendicular to each other; 2) Using data from 3070 patients, our experiments reveal that the learned representations of Echo-SyncNet outperform a supervised deep learning method that is optimized for automatic detection of fine-grained cardiac cycle phase; 3) We go one step further and show the usefulness of the learned representations in a one-shot learning scenario of cardiac key-frame detection. Without any fine-tuning, key frames in 1188 validation patient studies are identified by synchronizing them with only one labeled reference cine. We do not make any prior assumption about what specific cardiac views are used for training, and hence we show that Echo-SyncNet can accurately generalize to views not present in its training set. Project repository: github.com/fatemehtd/Echo-SyncNet>.

Automated estimation of echocardiogram image quality in hospitalized patients.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

Enhancement of needle visualization and localization in ultrasound.

PURPOSE: This scoping review covers needle visualization and localization techniques in ultrasound, where localization-based approaches mostly aim to compute the needle shaft (and tip) location while potentially enhancing its visibility too. METHODS: A literature review is conducted on the state-of-the-art techniques, which could be divided into five categories: (1) signal and image processing-based techniques to augment the needle, (2) modifications to the needle and insertion to help with needle-transducer alignment and visibility, (3) changes to ultrasound image formation, (4) motion-based analysis and (5) machine learning. RESULTS: Advantages, limitations and challenges of representative examples in each of the categories are discussed. Evaluation techniques performed in ex vivo, phantom and in vivo studies are discussed and summarized. CONCLUSION: Greatest limitation of the majority of the literature is that they rely on original visibility of the needle in the static image. Need for additional/improved apparatus is the greatest limitation toward clinical utility in practice. SIGNIFICANCE: Ultrasound-guided needle placement is performed in many clinical applications, including biopsies, treatment injections and anesthesia. Despite the wide range and long history of this technique, an ongoing challenge is needle visibility in ultrasound. A robust technique to enhance ultrasonic needle visibility, especially for steeply inserted hand-held needles, and while maintaining clinical utility requirements is needed.

Three-Dimensional Multi-Frequency Shear Wave Absolute Vibro-Elastography (3D S-WAVE) With a Matrix Array Transducer: Implementation and Preliminary In Vivo Study of the Liver.

Magnetic resonance elastography (MRE) is commonly regarded as the imaging-based gold-standard for liver fibrosis staging, comparable to biopsy. While ultrasound-based elastography methods for liver fibrosis staging have been developed, they are confined to a 1D or a 2D region of interest and to a limited depth. 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) is a steady-state, external excitation, volumetric elastography technique that is similar to MRE, but has the additional advantage of multi-frequency excitation. We present a novel ultrasound matrix array implementation of S-WAVE that takes advantage of 3D imaging. We use a matrix array transducer to sample axial multi-frequency steady-state tissue motion over a volume, using a Color Power Angiography sequence. Tissue motion with the frequency components {40,50,60} and {45,55,65} Hz are acquired over a (90° lateral) × (40° elevational) × (16 cm depth) sector with an acquisition time of 12 seconds. We compute the elasticity map in 3D using local spatial frequency estimation. We characterize this new approach in tissue phantoms against measurements obtained with transient elastography and MRE. Six healthy volunteers and eight patients with chronic liver disease were imaged. Their MRE and S-WAVE volumes were aligned using T1 to B-mode registration for direct comparison in common regions of interest. S-WAVE and MRE results are correlated with R2 = 0.92, while MRE and TE results are correlated with R2 = 0.71. Our findings show that S-WAVE with matrix array has the potential to deliver a similar assessment of liver fibrosis as MRE in a more accessible, inexpensive way, to a broader set of patients.