A Model-Based Approach to Investigate the Effect of a Long Bone Fracture on Ultrasound Strain Elastography

Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.

Assessment of spinal cord injury using ultrasound elastography in a rabbit model in vivo

The effect of the mechanical micro-environment on spinal cord injury (SCI) and treatment effectiveness remains unclear. Currently, there are limited imaging methods that can directly assess the localized mechanical behavior of spinal cords in vivo. In this study, we apply new ultrasound elastography (USE) techniques to assess SCI in vivo at the site of the injury and at the time of one week post injury, in a rabbit animal model. Eleven rabbits underwent laminectomy procedures. Among them, spinal cords of five rabbits were injured during the procedure. The other six rabbits were used as control. Two neurological statuses were achieved: non-paralysis and paralysis. Ultrasound data were collected one week post-surgery and processed to compute strain ratios. Histologic analysis, mechanical testing, magnetic resonance imaging (MRI), computerized tomography and MRI diffusion tensor imaging (DTI) were performed to validate USE results. Strain ratios computed via USE were found to be significantly different in paralyzed versus non-paralyzed rabbits. The myelomalacia histologic score and spinal cord Young's modulus evaluated in selected animals were in good qualitative agreement with USE assessment. It is feasible to use USE to assess changes in the spinal cord of the presented animal model. In the future, with more experimental data available, USE may provide new quantitative tools for improving SCI diagnosis and prognosis.

A CNN-based method to reconstruct 3-D spine surfaces from US images in vivo

Three-dimensional (3-D) reconstruction of the spine surface is of strong clinical relevance for the diagnosis and prognosis of spine disorders and intra-operative image guidance. In this paper, we report a new technique to reconstruct lumbar spine surfaces in 3-D from non-invasive ultrasound (US) images acquired in free-hand mode. US images randomly sampled from in vivo scans of 9 rabbits were used to train a U-net convolutional neural network (CNN). More specifically, a late fusion (LF)-based U-net trained jointly on B-mode and shadow-enhanced B-mode images was generated by fusing two individual U-nets and expanding the set of trainable parameters to around twice the capacity of a basic U-net. This U-net was then applied to predict spine surface labels in in vivo images obtained from another rabbit, which were then used for 3-D spine surface reconstruction. The underlying pose of the transducer during the scan was estimated by registering stacks of US images to a geometrical model derived from corresponding CT data and used to align detected surface points. Final performance of the reconstruction method was assessed by computing the mean absolute error (MAE) between pairs of spine surface points detected from US and CT and by counting the total number of surface points detected from US. Comparison was made between the LF-based U-net and a previously developed phase symmetry (PS)-based method. Using the LF-based U-net, the averaged number of US surface points across the lumbar region increased by 21.61% and MAE reduced by 26.28% relative to the PS-based method. The overall MAE (in mm) was 0.24±0.29. Based on these results, we conclude that: 1) the proposed U-net can detect the spine posterior arch with low MAE and large number of US surface points and 2) the newly proposed reconstruction framework may complement and, under certain circumstances, be used without the aid of an external tracking system in intra-operative spine applications.

Identification of ultrasound imaging markers to quantify long bone regeneration in a segmental tibial defect sheep model in vivo

The healing of large bone defects has been investigated for decades due to its complexity and clinical relevance. Ultrasound (US) methods have shown promise in monitoring bone healing, but no quantitative method to assess regenerated bone morphology in US images has been presented yet. In this study, we investigate new US morphometric parameters to quantify bone regeneration in vivo. A segmental tibial defect was surgically created and stabilized in a sheep animal model. US and computed tomography (CT) imaging data were collected two months post-surgery. New bone was assessed, reconstructed and quantified from the US and CT data using 3 morphometric parameters: the new-bone bulk (NBB), new-bone surface (NBS) and new-bone contact (NBC). The distance (mm) between surface reconstructions from repeated US was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.49\pm 0.30$$\end{document}0.49±0.30 and from US and CT was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.89\pm 0.49$$\end{document}0.89±0.49. In the mid-shaft of the defected tibia, US measurements of NBB, NBS and NBC were significantly higher than the corresponding CT measurements (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.001$$\end{document}p<0.001). Based on our results, we conclude that US may complement CT to reconstruct and quantify bone regrowth, especially in its early stages.

Modeling and Analysis of Ultrasound Elastographic Axial Strains for Spine Fracture Identification

This study reports the first use of ultrasound (US) elastography for imaging spinal fractures by assessing the mechanical response of the soft tissue at the posterior vertebra boundary to a uniaxial compression in rabbit ex vivo samples. Three-dimensional finite-element (FE) models of the vertebra-soft tissue complex in rabbit samples are generated and analyzed to evaluate the distribution of the axial normal and shear strains at the vertebra-soft tissue interface. Experiments on the same samples are performed to corroborate simulation findings. Results of this study indicate that the distribution of the axial strains manifests as distinct patterns around intact and fractured vertebrae. Numerical characteristics of the axial strain's spatial distribution are further used to construct two shape descriptors to make inferences on spinal abnormalities: 1) axial normal strain asymmetry for assessing the presence of fractures and 2) principal orientation of axial shear strain concentration regions (shear zones) for measurement of spinous process dislocation. This study demonstrates that axial normal strain and axial shear strain maps obtained via US elastography can provide a new means to detect spine fractures and abnormalities in the selected ex vivo animal models. Spinal fracture detection is important for the assessment of spinal cord injuries and stability. However, identification of spinal fractures using US is currently challenging. Our results show that features resulting from strain elastograms can serve as a useful adjunct to B-mode images in identifying spine fractures in the selected animal samples, and this information could be helpful in clinical settings.

Assessment of the long bone inter-fragmentary gap size in ultrasound strain elastograms

The inter-fragmentary gap size (IFGS) is a critical factor affecting the propensity of bone healing. In this paper, we present a study to analyze ultrasound strain elastographic numerical features in samples with distinct IFGS using both simulations and experiments. Six fractured rabbit hind leg samples in total were used in this study with controlled IFGS of 1 mm, 5 mm and 1 cm. For the simulation, computed tomography (CT) scans of all six samples were used to create solid models. Finite element analysis (FEA) and subsequent elastography simulations were performed on the 3D models to produce tensorial strain field data. Features of bony fragment separation were defined on different strain components and computed for strains segmented at varying thresholds to evaluate their performance in estimating the IFGS. A threshold for each strain component was then determined, based on which extra 3D features of interest were defined and extracted from the segmented strain data. Then, all 3D features were compared statistically among the three nominal groups. Additional simulations and experiments of axial shear strain elastography (ASSE) on the median coronal plane of the same samples were also performed. Our results indicate that coronal plane axial shear (CPAS) strain elastography produces a separation feature which is statistically correlated with the IFGS, and that our elastography simulation module is effective in predicting the CPAS elastographic strain behavior for different IFGS.