Towards quantitative quasi-static ultrasound elastography using a reference layer for liver imaging application: A preliminary assessment

Advanced synthetic aperture technique to enhance image quality in ultrasound elastography: A novel strategy

Quasi-static elastography (QSE) is a well-established technique used in medical imaging, where ultrasound data is collected both, before and after applying a slight compression on a tissue. This data is then analyzed to create image frames that reveal the stiffness parameter of the underlying tissue medium. Previous studies have focused on assessing how the Conventional Focused Beam (CFB) transmit method impacts the ultrasound elastography image quality. Recent studies have also shown an interest in synthetic aperture techniques like the Diverging Beam Synthetic Aperture Technique (DBSAT), due to its potential to enhance ultrasound image quality. However, its application in elastography has received limited attention. This paper introduces a new strategy of averaging low-resolution elastogram frames (LREA), obtained from DBSAT transmit method to improve the quality of elastography images. The CFB technique involves scanning the tissue line by line. In contrast, DBSAT is a synthetic aperture method that generates multiple low-resolution elastogram frames before combining them together to create a single high-quality image. In this research paper all the experimental studies were conducted on an agar-gelatin phantom, demonstrating the effectiveness of estimating elastograms from the low-resolution frame data of DBSAT transmit scheme and then summing them together to produce an elastogram with enhanced image quality. The results show a maximum improvement of 8 dB in the image quality metric of signal-to-noise ratio (SNR) as well as a 7 dB improvement in contrast-to-noise ratio (CNR) when comparing elastography images obtained by the proposed LREA method and the elastography images obtained by regular processing of the RF data acquired using the different methods of CFB and DBSAT.

3D Ultrasound Shear Wave Elastography for Musculoskeletal Tissue Assessment Under Compressive Load: A Feasibility Study

Given its real-time capability to quantify mechanical tissue properties, ultrasound shear wave elastography holds significant promise in clinical musculoskeletal imaging. However, existing shear wave elastography methods fall short in enabling full-limb analysis of 3D anatomical structures under diverse loading conditions, and may introduce measurement bias due to sonographer-applied force on the transducer. These limitations pose numerous challenges, particularly for 3D computational biomechanical tissue modeling in areas like prosthetic socket design. In this feasibility study, a clinical linear ultrasound transducer system with integrated shear wave elastography capabilities was utilized to scan both a calibrated phantom and human limbs in a water tank imaging setup. By conducting 2D and 3D scans under varying compressive loads, this study demonstrates the feasibility of volumetric ultrasound shear wave elastography of human limbs. Our preliminary results showcase a potential method for evaluating 3D spatially varying tissue properties, offering future extensions to computational biomechanical modeling of tissue for various clinical scenarios.

B-mode ultrasound to elastography synthesis using multiscale learning

Elastography is a promising diagnostic tool that measures the hardness of tissues, and it has been used in clinics for detecting lesion progress, such as benign and malignant tumors. However, due to the high cost of examination and limited availability of elastic ultrasound devices, elastography is not widely used in primary medical facilities in rural areas. To address this issue, a deep learning approach called the multiscale elastic image synthesis network (MEIS-Net) was proposed, which utilized the multiscale learning to synthesize elastic images from ultrasound data instead of traditional ultrasound elastography in virtue of elastic deformation. The method integrates multi-scale features of the prostate in an innovative way and enhances the elastic synthesis effect through a fusion module. The module obtains B-mode ultrasound and elastography feature maps, which are used to generate local and global elastic ultrasound images through their correspondence. Finally, the two-channel images are synthesized into output elastic images. To evaluate the approach, quantitative assessments and diagnostic tests were conducted, comparing the results of MEIS-Net with several deep learning-based methods. The experiments showed that MEIS-Net was effective in synthesizing elastic images from B-mode ultrasound data acquired from two different devices, with a structural similarity index of 0.74 ± 0.04. This outperformed other methods such as Pix2Pix (0.69 ± 0.09), CycleGAN (0.11 ± 0.27), and StarGANv2 (0.02 ± 0.01). Furthermore, the diagnostic tests demonstrated that the classification performance of the synthetic elastic image was comparable to that of real elastic images, with only a 3 % decrease in the area under the curve (AUC), indicating the clinical effectiveness of the proposed method.

A Poly-vinyl Alcohol (PVA)-based phantom and training tool for use in simulated Transrectal Ultrasound (TRUS) guided prostate needle biopsy procedures

Trans-rectal ultrasound-guided needle biopsy is a well-established diagnosis technique for prostate cancer. To enhance the needle manoeuvring skills under ultrasound (US) guidance, it is preferable to train medical practitioners in needle biopsy on tissue-mimicking phantoms. This phantom should mimic the morphology as well as mechanical and acoustic properties of the human male pelvic region to provide a surgical experience and feedback. In this study, polyvinyl alcohol (PVA) was used and evaluated for prostate phantom development, that is stiffness tunable, US-compatible and durable phantom material. Three samples, each with 5%, 10%, and 15% concentration of PVA material, were prepared, and their mechanical and shrinkage characteristics were investigated. The anatomy of male pelvic region was used to develop an anatomically correct phantom. Later US-guided needle biopsy was performed on the phantom. The range of elastic moduli of the PVA samples was 2∼146 kPa. Their elastic moduli and volumes were found to remain statistically close from seventh to eighth freeze-thaw cycle (p>0.05). Initial US scans of the phantom resulted in satisfactory B-mode images, with a clear distinction between the prostate and its surrounding organs. This study demonstrated the applicability of PVA hydrogel as a phantom material for training in US-guided needle biopsy.

A Quasi-Static Quantitative Ultrasound Elastography Algorithm Using Optical Flow

Ultrasound elastography is a constantly developing imaging technique which is capable of displaying the elastic properties of tissue. The measured characteristics could help to refine physiological tissue models, but also indicate pathological changes. Therefore, elastography data give valuable insights into tissue properties. This paper presents an algorithm that measures the spatially resolved Young’s modulus of inhomogeneous gelatin phantoms using a CINE sequence of a quasi-static compression and a load cell measuring the compressing force. An optical flow algorithm evaluates the resulting images, the stresses and strains are computed, and, conclusively, the Young’s modulus and the Poisson’s ratio are calculated. The whole algorithm and its results are evaluated by a performance descriptor, which determines the subsequent calculation and gives the user a trustability index of the modulus estimation. The algorithm shows a good match between the mechanically measured modulus and the elastography result—more precisely, the relative error of the Young’s modulus estimation with a maximum error 35%. Therefore, this study presents a new algorithm that is capable of measuring the elastic properties of gelatin specimens in a quantitative way using only the image data. Further, the computation is monitored and evaluated by a performance descriptor, which measures the trustability of the results.

3D Cell Culture: Recent Development in Materials with Tunable Stiffness

It is widely accepted that three-dimensional cell culture systems simulate physiological conditions better than traditional 2D systems. Although extracellular matrix components strongly modulate cell behavior, several studies underlined the importance of mechanosensing in the control of different cell functions such as growth, proliferation, differentiation, and migration. Human tissues are characterized by different degrees of stiffness, and various pathologies (e.g., tumor or fibrosis) cause changes in the mechanical properties through the alteration of the extracellular matrix structure. Additionally, these modifications have an impact on disease progression and on therapy response. Hence, the development of platforms whose stiffness could be modulated may improve our knowledge of cell behavior under different mechanical stress stimuli. In this review, we have analyzed the mechanical diversity of healthy and diseased tissues, and we have summarized recently developed materials with a wide range of stiffness.

Synthetic aperture with high lateral sampling frequency for ultrasound elastography

Ultrasound elastography is a non-invasive technique for detecting pathological alterations in tissue. It is known that pathological alteration of tissue often has a direct impact on its elastic modulus, which can be revealed using elastography. For estimating elastic modulus, we need to estimate both axial and lateral displacement accurately. Current state of the art elastography techniques provide a substantially less accurate lateral displacement field as compared to the axial displacement field. One of the most important factors in poor lateral estimation is a low sampling frequency in the lateral direction. In this paper, we use synthetic aperture beamforming to benefit from its capability of high sampling frequency in the lateral direction. We compare highly sampled data and focused line per line beam formed data by feeding them to our recently published elastography method, OVERWIND [1]. According to simulation and phantom experiments, not only the lateral displacement estimation is substantially improved, but also the axial displacement estimation is improved.

Accurate and Precise Time-Delay Estimation for Ultrasound Elastography With Prebeamformed Channel Data

Free-hand palpation ultrasound elastography is a noninvasive approach for detecting pathological alteration in tissue. In this method, the tissue is compressed by a handheld probe and displacement of each sample is estimated, a process which is also known as time-delay estimation (TDE). Even with the simplifying assumption that ignores out of plane motion, TDE is an ill-posed problem requiring estimation of axial and lateral displacements for each sample from its intensity. A well-known class of methods for making elastography a well-posed problem is regularized optimization-based methods, which imposes smoothness regularization in the associated cost function. In this article, we propose to utilize channel data that have been compensated for time gain and time delay (introduced by transmission) instead of postbeamformed radio frequency (RF) data in the optimization problem. We name our proposed method Channel data for GLobal Ultrasound Elastography (CGLUE). We analytically derive bias and variances of TDE as functions of data noise for CGLUE and Global Ultrasound Elastography (GLUE) and use the Cauchy-Schwarz inequality to prove that CGLUE provides a TDE with lower bias and variance error. To further illustrate the improved performance of CGLUE, the results of simulation, experimental phantom, and ex-vivo experiments are presented.

Toward Quantitative and Operator-independent Quasi-static Ultrasound Elastography: An Ex Vivo Feasibility Study

It is known that the elasticity of liver reduces progressively in the case of diffuse liver disease. Currently, the diagnosis of diffuse liver disease requires a biopsy, which is an invasive procedure. In this paper, we evaluate and report a noninvasive method that can be used to quantify liver stiffness using quasi-static ultrasound elastography approach. Quasi-static elastography is popular in clinical applications where the qualitative assessment of relative tissue stiffness is enough, whereas its potential is relatively underutilized in liver imaging due to lack of local stiffness contrast in the case of diffuse liver disease. Recently, we demonstrated an approach of using a calibrated reference layer to produce quantitative modulus elastograms of the target tissue in simulations and phantom experiments. In a separate work, we reported the development of a compact handheld device to reduce inter- and intraoperator variability in freehand elastography. In this work, we have integrated the reference layer with a handheld controlled compression device and evaluate it for quantitative liver stiffness imaging application. The performance of this technique was assessed on ex vivo goat liver samples. The Young's modulus values obtained from indentation measurements of liver samples acted as the ground truth for comparison. The results from this work demonstrate that by combining the handheld device along with reference layer, the estimated Young's modulus value approaches the ground truth with less error compared with that obtained using freehand compression (8% vs. 15%). The results suggest that the intra- and interoperator reproducibility of the liver elasticity also improved when using the handheld device. Elastography with a handheld compression device and reference layer is a reliable and simple technique to provide a quantitative measure of elasticity.

Quantitative quasi-static ultrasound elastography using reference layer: Ex-vivo study

It is well-documented in the literature that changes in tissue elasticity are generally correlated with disease condition. In the case of diffuse liver disease, the elasticity of the liver reduces progressively. However, this change does not clearly manifest in conventional ultrasound examinations. Although quasi-static elastography is popular in clinical applications where qualitative assessment of relative tissue stiffness is enough, its potential is relatively underutilized in liver imaging due to the need for quantitative stiffness value. Recently, it was demonstrated that using a reference layer of known stiffness, one could produce quantitative modulus elastograms of the target tissue using quasi-static elastography using simulations and phantom experiments. Here, we examined the performance of this approach on ex-vivo goat liver samples and compare the estimated modulus values to that obtained from indentation measurements. The results suggest that using this approach of reference layer yields Young's modulus values within 10% error compared to the ground truth.