2.5-D Extended Field-of-View Ultrasound

Anatomical Prior and Inter-Slice Consistency for Semi-Supervised Vertebral Structure Detection in 3D Ultrasound Volume

Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but the current assessment method only uses coronal projection images and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch to detect vertebral structures in 3D ultrasound volume containing a detector and classifier. The detector network finds the potential positions of structures on transverse slice globally, and then the local patches are cropped based on detected positions. The classifier is used to distinguish whether the patches contain real vertebral structures and screen the predicted positions from the detector. VertMatch utilizes unlabeled data in a semi-supervised manner, and we develop two novel techniques for semi-supervised learning: 1) anatomical prior is used to acquire high-quality pseudo labels; 2) inter-slice consistency is used to utilize more unlabeled data by inputting multiple adjacent slices. Experimental results demonstrate that VertMatch can detect vertebra accurately in ultrasound volume and outperforms state-of-the-art methods. Moreover, VertMatch is also validated in automatic spinous process angle measurement on forty subjects with scoliosis, and the results illustrate that it can be a promising approach for the 3D assessment of scoliosis.

Automatic 3-D spine curve measurement in freehand ultrasound via structure-aware reinforcement learning spinous process localization

Freehand 3-D ultrasound systems have been advanced in scoliosis assessment to avoid radiation hazards, especially for teenagers. This novel 3-D imaging method also makes it possible to evaluate the spine curvature automatically from the corresponding 3-D projection images. However, most approaches neglect the three-dimensional spine deformity by only using the rendering images, thus limiting their usage in clinical applications. In this study, we proposed a structure-aware localization model to directly identify the spinous processes for automatic 3-D spine curve measurement using the images acquired with freehand 3-D ultrasound imaging. The pivot is to leverage a novel reinforcement learning (RL) framework to localize the landmarks, which adopts a multi-scale agent to boost structure representation with positional information. We also introduced a structure similarity prediction mechanism to perceive the targets with apparent spinous process structures. Finally, a two-fold filtering strategy was proposed to screen the detected spinous processes landmarks iteratively, followed by a three-dimensional spine curve fitting for the spine curvature assessments. We evaluated the proposed model on 3-D ultrasound images among subjects with different scoliotic angles. The results showed that the mean localization accuracy of the proposed landmark localization algorithm was 5.95 pixels. Also, the curvature angles on the coronal plane obtained by the new method had a high linear correlation with those by manual measurement (R = 0.86, p < 0.001). These results demonstrated the potential of our proposed method for facilitating the 3-D assessment of scoliosis, especially for 3-D spine deformity assessment.

A Study on a Parameter Estimator for the Homodyned K Distribution Based on Table Search for Ultrasound Tissue Characterization

OBJECTIVE

The homodyned K (HK) distribution is considered to be the most suitable distribution in the context of tissue characterization; therefore, the search for a rapid and reliable parameter estimator for HK distribution is important.

METHODS

We propose a novel parameter estimator based on a table search (TS) for HK parameter estimates. The TS estimator can inherit the strength of conventional estimators by integrating various features and taking advantage of the TS method in a rapid and easy operation. Performance of the proposed TS estimator was evaluated and compared with that of XU (the estimation method based on X and U statistics) and artificial neural network (ANN) estimators.

DISCUSSION

The simulation results revealed that the TS estimator is superior to the XU and ANN estimators in terms of normalized standard deviations and relative root mean squared errors of parameter estimation, and is faster. Clinical experiments found that the area under the receiver operating curve for breast lesion classification using the parameters estimated by the TS estimator could reach 0.871.

CONCLUSION

The proposed TS estimator is more accurate, reliable and faster than the state-of-the-art XU and ANN estimators and has great potential for ultrasound tissue characterization based on the HK distribution.

Novel automated spinal ultrasound segmentation approach for scoliosis visualization

Scoliosis is a 3D deformity of the spine in which one or more segments of the spine curve laterally, usually with rotation of the vertebral body. Generally, having a Cobb angle (Cobb) greater than 10° can be considered scoliosis. In spine imaging, reliable and accurate identification and segmentation of bony features are crucial for scoliosis assessment, disease diagnosis, and treatment planning. Compared with commonly used X-ray detection methods, ultrasound has received extensive attention from researchers in the past years because of its lack of radiation, high real-time performance, and low price. On the basis of our previous research on spinal ultrasound imaging, this work combines artificial intelligence methods to create a new spine ultrasound image segmentation model called ultrasound global guidance block network (UGBNet), which provides a completely automatic and reliable spine segmentation and scoliosis visualization approach. Our network incorporates a global guidance block module that integrates spatial and channel attention, through which long-range feature dependencies and contextual scale information are learned. We evaluate the performance of the proposed model in semantic segmentation on spinal ultrasound datasets through extensive experiments with several classical learning segmentation methods, such as UNet. Results show that our method performs better than other approaches. Our UGBNet significantly improves segmentation precision, which can reach 74.2% on the evaluation metric of the Dice score.

Automatic Detection and Measurement of Spinous Process Curve on Clinical Ultrasound Spine Images

The ultrasound (US) imaging technique has been applied to scoliosis assessment, and the proxy Cobb angle can be acquired on the US coronal images. The spinous process angle (SPA) is a valuable parameter to indicate 3-D deformity of spine. However, the SPA cannot be measured on US images since the spinous process (SP) is merged in the soft tissue layer and impossible to be identified on the coronal view directly. A new method based on the gradient vector flow (GVF) snake model was proposed to automatically locate SP position on the US transverse images, and the density-based spatial clustering of application with noise (DBSCAN) was used to remove the outliers out of the detected location results. With marking the SP points on the US coronal image, the SP curve was interpolated and the SPA was measured. The algorithm was evaluated on 50 subjects with various severity of scoliosis, and two raters measured the SPA on both US images and radiographs manually. The mean absolute differences (MADs) of SPAs obtained from the two modalities were 3.4° ± 2.4° and 3.6° ± 2.8° for the two raters, respectively, which were less than the clinical acceptance error (5°), and the results reported a good linear correlation ( ) between the US method and radiography. It indicates that the proposed method can be a promising approach for SPA measurement using the US imaging technique.

Measurement of Quasi-Static 3-D Knee Joint Movement Based on the Registration From CT to US

The measurement of quasi-static 3-D knee joint movement is an important basis for studying the mechanism of knee joint injury. Most of the existing measurement methods make use of computed tomography (CT) and nuclear magnetic resonance (MR) imaging technology and hence have the disadvantages of invasiveness, ionizing radiation, low accuracy, and high cost. To overcome those drawbacks, this article innovatively proposes a 3-D motion measurement system for the knee joint based on the registration of CT images to ultrasound (US) images. More specifically, the lower limbs of a subject were first scanned once to acquire the CT images. A portable handheld device was designed to control a US probe for mechanically scanning the subject's lower limbs in a linear trajectory. During the movement of the subject's lower limbs, the US scanning was performed quasi-statically. The acquired US images were then registered to the CT images, and the 3-D motions of the lower limb bones could be recreated with the bones scanned in CT images. To guarantee the registration accuracy and efficiency, we used the H-shaped multiview slice assembly as the structural image content for the registration process. The experimental results show that our approach can accurately measure the 3-D motion of the knee joint and meet the needs of 3-D motion analysis of knee joint in practice.

Automating Spine Curvature Measurement in Volumetric Ultrasound via Adaptive Phase Features

Ultrasound volume projection imaging (VPI) has been recently suggested. This novel imaging method allows a non-radiation assessment of spine deformity with free-hand 3-D ultrasound imaging. This paper presents a fully automatic method to evaluate the spine curve in VPI images corresponding to different projection depth of the volumetric ultrasound, thus making it possible to analyze 3-D spine deformity. The new automatic method is based on prior knowledge about the geometric arrangement of the spinous processes. The frequency bandwidth of log-Gabor filters is adaptively adjusted to calculate the oriented phase congruency, facilitating the segmentation of the spinous column profile. And the spine curvature angle is finally calculated according to the inflection points of the curve over the segmented spinous column profile. The performance of the automatic method is evaluated on spine VPI images among patients with different scoliotic angles. The curvature angles obtained using the proposed method have a high linear correlation with those by the manual method (r = 0.90, p < 0.001) and X-ray Cobb's method (r = 0.87, p < 0.001). The feasibility of 3-D spine deformity assessment is also demonstrated using VPI images corresponding to various projection depth. The results suggest that this method can substantially improve the recognition of the spinous column profile, especially facilitating the applications of 3-D spine deformity assessment.

Hepatic steatosis assessment using ultrasound homodyned-K parametric imaging: the effects of estimators

BACKGROUND

The homodyned-K (HK) distribution is an important statistical model for describing ultrasound backscatter envelope statistics. HK parametric imaging has shown potential for characterizing hepatic steatosis. However, the feasibility of HK parametric imaging in assessing human hepatic steatosis in vivo remains unclear.

METHODS

In this paper, ultrasound HK μ parametric imaging was proposed for assessing human hepatic steatosis in vivo. Two recent estimators for the HK model, RSK (the level-curve method that uses the signal-to-noise ratio (SNR), skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on the first moment of the intensity and two log-moments, namely X- and U-statistics), were investigated. Liver donors (n=72) and patients (n=204) were recruited to evaluate hepatic fat fractions (HFFs) using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate, and severe) using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct μ RSK and μ XU images to correlate with HFF analyses and fatty liver stages. The μ RSK and μ XU parametric images were constructed using the sliding window technique with the window side length (WSL) =1-9 pulse lengths (PLs). The diagnostic values of the μ RSK and μ XU parametric imaging methods were evaluated using receiver operating characteristic (ROC) curves.

RESULTS

For the 72 participants in Group A, the μ RSK parametric imaging with WSL =2-9 PLs exhibited similar correlation with log10(HFF), and the μ RSK parametric imaging with WSL = 3 PLs had the highest correlation with log10(HFF) (r=0.592); the μ XU parametric imaging with WSL =1-9 PLs exhibited similar correlation with log10(HFF), and the μ XU parametric imaging with WSL =1 PL had the highest correlation with log10(HFF) (r=0.628). For the 204 patients in Group B, the areas under the ROC (AUROCs) obtained using μ RSK for fatty stages ≥ mild (AUROC1), ≥ moderate (AUROC2), and ≥ severe (AUROC3) were (AUROC1, AUROC2, AUROC3) = (0.56, 0.57, 0.53), (0.68, 0.72, 0.75), (0.73, 0.78, 0.80), (0.74, 0.77, 0.79), (0.74, 0.78, 0.79), (0.75, 0.80, 0.82), (0.74, 0.77, 0.83), (0.74, 0.78, 0.84) and (0.73, 0.76, 0.83) for WSL =1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively. The AUROCs obtained using μ XU for fatty stages ≥ mild, ≥ moderate, and ≥ severe were (AUROC1, AUROC2, AUROC3) = (0.75, 0.83, 0.81), (0.74, 0.80, 0.80), (0.76, 0.82, 0.82), (0.74, 0.80, 0.84), (0.76, 0.80, 0.83), (0.75, 0.80, 0.84), (0.75, 0.79, 0.85), (0.75, 0.80, 0.85) and (0.73, 0.77, 0.83) for WSL = 1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively.

CONCLUSIONS

Both the μ RSK and μ XU parametric images are feasible for evaluating human hepatic steatosis. The WSL exhibits little impact on the diagnosing performance of the μ RSK and μ XU parametric imaging. The μ XU parametric imaging provided improved performance compared to the μ RSK parametric imaging in characterizing human hepatic steatosis in vivo.

Scoliotic Imaging With a Novel Double-Sweep 2.5-Dimensional Extended Field-of-View Ultrasound

Extended field-of-view ultrasound (US EFOV) imaging is a technique used extensively in the clinical field to attain interpretable panorama of anatomy; 2.5-D US EFOV has recently been proposed for spine imaging. In the original 2.5-D US EFOV, it makes use of a six degrees-of-freedom positional sensor attached to the US probe to record the corresponding position of each B-scan. By combining the positional information and the B-scan images, the 2.5-D EFOV can reconstruct a panorama on a curved image plane when the scanning trajectory of the US probe is curved. In this paper, an improved method based on the Bezier interpolation is proposed to better reconstruct 2.5-D US EFOV imaging, producing the panoramas with smoother texture and higher quality. To make it more applicable for scoliosis patients, we designed a novel method called double-sweep 2.5-D EFOV to better image the spinal tissues and easily compute the Cobb angle. In vitro and in vivo experiments demonstrated that the 2.5-D EFOV images obtained by the proposed method can present anatomical structures of the scanning region accurately.

In Vivo Measurement of the Human Lumbar Spine Using Magnetic Resonance Imaging to Ultrasound Registration

OBJECTIVE

This study aimed to refine a magnetic resonance imaging (MRI)-ultrasound registration (ie, alignment) technique to make noninvasive, nonionizing, 3-dimensional measurement of the lumbar segmental motion in vivo.

METHODS

Five healthy participants participated in this validation study. We scanned the lumbar region of each participant 5 times using an ultrasound probe while he or she kept a prone lying posture on a plinth. Participant-specific models of L1-L5 were constructed from magnetic resonance (MR) images and aligned with the 3-dimensional ultrasound dataset of each scan using 4 variants of MRI-ultrasound registration approach (simplified intensity-based registration [1] with and [2] without including the transverse processes and their surrounding soft tissues [denoted as TP complex]; and hierarchical intensity-based registration [3] with and [4] without including the TP complex). The robustness and precision of these registration approaches were compared.

RESULTS

Although all registration approaches converged to a similar solution, excluding the TP complex improved the percentage of successful registration from 92% to 100%. There was no significant difference in the precision among the 4 MRI-ultrasound registration variants. For the simplified intensity-based registration without including the TP complex, average precision at each degree of freedom was 1.33° (flexion-extension), 2.48° (lateral bending), 1.32° (axial rotation), 2.15 mm (left/right), 1.08 mm (anterior-posterior), and 1.16 (superior-inferior), respectively.

CONCLUSION

Given that using simplified intensity-based MRI-ultrasound registration can substantially streamline the registration process and excluding the TP complex would improve the robustness of the registration, we conclude that this combination is the method of choice for in vivo human applications.

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Hepatic steatosis is a key manifestation of non-alcoholic fatty liver disease (NAFLD). Early detection of hepatic steatosis is of critical importance. Currently, liver biopsy is the clinical golden standard for hepatic steatosis assessment. However, liver biopsy is invasive and associated with sampling errors. Ultrasound has been recommended as a first-line diagnostic test for the management of NAFLD. However, B-mode ultrasound is qualitative and can be affected by factors including image post-processing parameters. Quantitative ultrasound (QUS) aims to extract quantified acoustic parameters from the ultrasound backscattered signals for ultrasound tissue characterization and can be a complement to conventional B-mode ultrasound. QUS envelope statistics techniques, both statistical model-based and non-model-based, have shown potential for hepatic steatosis characterization. However, a state-of-the-art review of hepatic steatosis assessment using envelope statistics techniques is still lacking. In this paper, envelope statistics-based QUS parametric imaging techniques for characterizing hepatic steatosis are reviewed and discussed. The reviewed ultrasound envelope statistics parametric imaging techniques include acoustic structure quantification imaging, ultrasound Nakagami imaging, homodyned-K imaging, kurtosis imaging, and entropy imaging. Future developments are suggested.

Automated Identification and Localization of the Inferior Vena Cava Using Ultrasound: An Animal Study

Ultrasound measurement of the inferior vena cava (IVC) is widely implemented in the clinic. However, the process is time consuming and labor intensive, because the IVC diameter is continuously changing with respiration. In addition, artificial errors and intra-operator variations are always considerable, making the measurement inconsistent. Research efforts were recently devoted to developing semiautomated methods. But most required an initial identification of the IVC manually. As a first step toward fully automated IVC measurement, in this paper, we present an intelligent technique for automated IVC identification and localization. Forty-eight ultrasound data sets were collected from eight pigs, each of which included two frames in B-mode and color mode (C-mode) collected at the inspiration, and two cine loops in B-mode and C-mode. Static and dynamic automation algorithms were applied to the data sets for identifying and localizing the IVC. The results were evaluated by comparing with the manual measurement of experienced clinicians. The automated approaches successfully identified the IVC in 47 cases (success rate: 97.9%). The automated localization of the IVC is close to the manual counterpart, with the difference within one diameter. The automatically measured diameters are close to those measured manually, with most differences below 15%. It is revealed that the proposed method can automatically identify the IVC with high success rate and localize the IVC with high accuracy. But the study with high accuracy was conducted under good control and without considering difficult cases, which deserve future explorations. The method is a first step toward fully automated IVC measurement, which is suitable for point-of-care applications.