Resolution Ultrasound Imaging Research Articles

Sonographic imaging of the genicular nerves of the knee

Introduction: The genicular nerves are sensory nerves around the knee. With the improvements in ultrasound imaging resolution, these nerves can be identified sonographically. Topic description: Due to their small size, genicular nerves are often underappreciated and overlooked during routine sonographic imaging. Chronic pain associated with knee osteoarthritis, or postsurgical knee pain can be relieved by ultrasound-guided radiofrequency ablations or nerve blockades targeting the genicular nerves. Discussion: To sonographically image these nerves, or guide nerve blocks or ablations, knowledge of the sonographic anatomy, technique and normal appearances of the genicular nerves and their surrounding structures is required and is unpacked in this pictorial essay. Conclusion: Enhanced knowledge of the anatomy and a systematic approach to sonographically image and assess the genicular nerves of the knee can enhance the diagnosis of pathology, nerve injury or entrapment, and sonographic guidance of interventional procedures for pain relief.

A temporal enhanced semi-supervised training framework for needle segmentation in 3D ultrasound images

Objective. Automated biopsy needle segmentation in 3D ultrasound images can be used for biopsy navigation, but it is quite challenging due to the low ultrasound image resolution and interference similar to the needle appearance. For 3D medical image segmentation, such deep learning networks as convolutional neural network and transformer have been investigated. However, these segmentation methods require numerous labeled data for training, have difficulty in meeting the real-time segmentation requirement and involve high memory consumption. Approach. In this paper, we have proposed the temporal information-based semi-supervised training framework for fast and accurate needle segmentation. Firstly, a novel circle transformer module based on the static and dynamic features has been designed after the encoders for extracting and fusing the temporal information. Then, the consistency constraints of the outputs before and after combining temporal information are proposed to provide the semi-supervision for the unlabeled volume. Finally, the model is trained using the loss function which combines the cross-entropy and Dice similarity coefficient (DSC) based segmentation loss with mean square error based consistency loss. The trained model with the single ultrasound volume input is applied to realize the needle segmentation in ultrasound volume. Main results. Experimental results on three needle ultrasound datasets acquired during the beagle biopsy show that our approach is superior to the most competitive mainstream temporal segmentation model and semi-supervised method by providing higher DSC (77.1% versus 76.5%), smaller needle tip position (1.28 mm versus 1.87 mm) and length (1.78 mm versus 2.19 mm) errors on the kidney dataset as well as DSC (78.5% versus 76.9%), needle tip position (0.86 mm versus 1.12 mm) and length (1.01 mm versus 1.26 mm) errors on the prostate dataset. Significance. The proposed method can significantly enhance needle segmentation accuracy by training with sequential images at no additional cost. This enhancement may further improve the effectiveness of biopsy navigation systems.

Fully Integrated Pitch-Matched AlScN PMUT-on-CMOS Array for High- Resolution Ultrasound Images

Fully Integrated Pitch-Matched AlScN PMUT-on-CMOS Array for High- Resolution Ultrasound Images

Speckle noise reduction on aligned consecutive ultrasound frames via deep neural network

Despite the benefits of ultrasound (US) imaging systems for medical diagnosis and treatment, US images are prone to low resolution and contrast due to US’s inherent attributes, as well as affected by speckle noise that directly influences their quality. In retrospective studies, diverse filters have been applied to minimize the effects of speckle noise and enhance the quality of US images. In this article, we propose a method of enhancing US images inspired by synthetic aperture imaging, which provides high-resolution images by adding low-resolution images and measuring the probe’s movement. Our proposed method does not involve synthetic aperture imaging but compensates for the motion effect in the temporal dimension, aligns consecutive images, and stacks aligned images to suppress speckle noise and consequently enhance the resolution of US images. We exploited deep neural network (DNN) models to estimate motion parameters between consecutive US images. In a new database of US images, we also collected the images’ position-related information implicitly measured in inertial measurement units, which was exploited as a ground truth for motion parameters between consecutive images. Compared with other image-enhancing techniques involving conventional filters and DNN modalities, our method demonstrated superiority in enhancing the quality of US images. We also found that estimating motion parameters directly influenced the success of the image-stacking process. As in ablation studies in DNNs, we additionally investigated the effect of dropping some images in the temporal dimension, which revealed that contextual differences and excessive rates of movement in successive US images weakens the image-stacking process and thus the potential enhancement of US images.

TransVFS: A spatio-temporal local–global transformer for vision-based force sensing during ultrasound-guided prostate biopsy

Robot-assisted prostate biopsy is a new technology to diagnose prostate cancer, but its safety is influenced by the inability of robots to sense the tool-tissue interaction force accurately during biopsy. Recently, vision based force sensing (VFS) provides a potential solution to this issue by utilizing image sequences to infer the interaction force. However, the existing mainstream VFS methods cannot realize the accurate force sensing due to the adoption of convolutional or recurrent neural network to learn deformation from the optical images and some of these methods are not efficient especially when the recurrent convolutional operations are involved. This paper has presented a Transformer based VFS (TransVFS) method by leveraging ultrasound volume sequences acquired during prostate biopsy. The TransVFS method uses a spatio-temporal local–global Transformer to capture the local image details and the global dependency simultaneously to learn prostate deformations for force estimation. Distinctively, our method explores both the spatial and temporal attention mechanisms for image feature learning, thereby addressing the influence of the low ultrasound image resolution and the unclear prostate boundary on the accurate force estimation. Meanwhile, the two efficient local–global attention modules are introduced to reduce 4D spatio-temporal computation burden by utilizing the factorized spatio-temporal processing strategy, thereby facilitating the fast force estimation. Experiments on prostate phantom and beagle dogs show that our method significantly outperforms existing VFS methods and other spatio-temporal Transformer models. The TransVFS method surpasses the most competitive compared method ResNet3dGRU by providing the mean absolute errors of force estimation, i.e., 70.4 ± 60.0 millinewton (mN) vs 123.7 ± 95.6 mN, on the transabdominal ultrasound dataset of dogs.

Evaluating a 3D Ultrasound Imaging Resolution of Single Transmitter/Receiver with Coding Mask by Extracting Phase Information.

We are currently investigating the ultrasound imaging of a sensor that consists of a randomized encoding mask attached to a single lead zirconate titanate (PZT) oscillator for a puncture microscope application. The proposed model was conducted using a finite element method (FEM) simulator. To increase the number of measurements required by a single element system that affects its resolution, the transducer was rotated at different angles. The image was constructed by solving a linear equation of the image model resulting in a poor quality. In a previous work, the phase information was extracted from the echo signal to improve the image quality. This study proposes a strategy by integrating the weighted frequency subbands compound and a super-resolution technique to enhance the resolution in range and lateral direction. The image performance with different methods was also evaluated using the experimental data. The results indicate that better image resolution and speckle suppression were obtained by applying the proposed method.

Beyond synthetic aperture focusing: deconvolution-based elevation resolution enhancement using simulated point spread function for linear array-based three-dimensional photoacoustic imaging.

This paper introduces a deconvolution-based method to enhance the elevation resolution of a linear array-based three-dimensional (3D) photoacoustic (PA) imaging system. PA imaging combines the high contrast of optical imaging with the deep, multi-centimeter spatial resolution of ultrasound (US) imaging, providing structural and functional information about biological tissues. Linear array-based 3D PA imaging is easily accessible and applicable for ex vivo studies, small animal research, and clinical applications in humans. However, its elevation resolution is limited by the acoustic lens geometry, which establishes a single elevation focus. Previous work used synthetic aperture focusing (SAF) to enhance elevation resolution, but the resolution achievable by SAF is constrained by the size of the elevation focus. Here, we introduce the application of Richardson-Lucy deconvolution, grounded in simulated point-spread-functions, to surpass the elevation resolution attainable with SAF alone. We validated this approach using both simulation and experimental data, demonstrating that the full-width-at-half-maximum of point targets on the elevation plane was reduced compared to using SAF only, suggesting resolution improvement. This method shows promise for improving 3D image quality of existing linear array-based PA imaging systems, offering potential benefits for disease diagnosis and monitoring.

A Multiscale Deep Encoder–Decoder with Phase Congruency Algorithm Based on Deep Learning for Improving Diagnostic Ultrasound Image Quality

Ultrasound imaging is widely used as a noninvasive lesion detection method in diagnostic medicine. Improving the quality of these ultrasound images is very important for accurate diagnosis, and deep learning-based algorithms have gained significant attention. This study proposes a multiscale deep encoder–decoder with phase congruency (MSDEPC) algorithm based on deep learning to improve the quality of diagnostic ultrasound images. The MSDEPC algorithm included low-resolution (LR) images and edges as inputs and constructed a multiscale convolution and deconvolution network. Simulations were conducted using the Field 2 program, and data from real experimental research were obtained using five clinical datasets containing images of the carotid artery, liver hemangiomas, breast malignancy, thyroid carcinomas, and obstetric nuchal translucency. LR images, bicubic interpolation, and super-resolution convolutional neural networks (SRCNNs) were modeled as comparison groups. Through visual assessment, the image processed using the MSDEPC was the clearest, and the lesions were clearly distinguished. The structural similarity index metric (SSIM) value of the simulated ultrasound image using the MSDEPC algorithm improved by approximately 38.84% compared to LR. In addition, the peak signal-to-noise ratio (PSNR) and SSIM values of clinical ultrasound images using the MSDEPC algorithm improved by approximately 2.33 times and 88.58%, respectively, compared to LR. In conclusion, the MSDEPC algorithm is expected to significantly improve the spatial resolution of ultrasound images.

Improvement of low-frequency ultrasonic image quality using a enhanced convolutional neural network

Ultrasound has become an indispensable clinical scanning and diagnostic tool because of its convenience, rapidity, and radiation-free properties. In ultrasound imaging, the resolution and detection depth of ultrasound images are closely related to the frequency of the transmitted ultrasound. Ultrasound with low transmission frequency has a wide detection range, but its images have problems such as low resolution, blurred edge contours and obvious background noise. In contrast, the higher transmission frequency, the smaller the detection range, but the resolution has improved. The rapid development of deep learning provides new approaches and enormous potential for improving resolution of ultrasound images and acquiring high-resolution images with an extensive detection range. In this paper, the attention mechanism is combined into a convolutional neural network based on the encoder-decoder, and the feature maps extracted at different scales are merged by skip connections to preserve more structure and contrast details, thus realizing the improvement of lower-frequency ultrasound image resolution. This work utilizes the capacitive micromechanical ultrasound transducer (CMUT) probe for dataset acquisition. The network's performance is validated by simulated datasets for point targets and experimental datasets for breast body models. The experimental results show that for ultrasound images, the network output map can improve by 0.2 in structural similarity and at least 11 dB in peak signal-to-noise ratio. The proposed network can effectively preserve ultrasound images' structural and textural information, improve edge contour definition, and significantly suppress noise and imaging artifacts, which has significant development potential in ultrasound imaging.

Transcriptional regulation of macrophage inflammation in the heart failure with preserved ejection fraction: a translational study

Abstract Background/Introduction Inflammatory response is a key player in heart failure with preserved ejection fraction (HFpEF) but the underlying mechanisms are poorly understood (1). Recent studies have shown a critical involvement of cardiac macrophage recruitment and activation in myocardial remodelling (2,3). Purpose here we sought to determine the role of macrophage inflammation in experimental and human HFpEF. Methods We employed a translational approach combining human and murine LV myocardial tissue, FACS analysis of cardiac immune cells, in vitro experiments in primary macrophages and cardiac cells as well as ex vivo functional analyses in LV tissues. Human LV myocardial samples were used to evaluate the pro-inflammatory activity of macrophages in the HFpEF myocardium. Induction of HFpEF in mice was done through a validated mouse model combining metabolic (high-fat diet) and hemodynamic stress (L-NAME). Cardiac function was investigated by high resolution ultrasound imaging (Vevo3100) and the LV myocardial samples were used to study the macrophage polarization and their inflammatory activity. Based on interrogation of available RNA-seq datasets –showing the transcriptional repressor NCOR1 as a relevant signature in macrophages during cardiac inflammation, we generated myeloid cell-specific NCOR1 knockout (Ncor1-KO) transgenic mice to investigate the in vivo effects of NCOR1 deletion on the HFpEF phenotype. In vitro experiments included RAW 264.7 cells polarization through a metabolic stress model via palmitic acid (PA) and the assessment of macrophage phenotype switching in the presence or the absence of NCOR1. To study the crosstalk of macrophages with the surrounding cardiac cells, cardiomyocytes (H9c2) and endothelial cells (HAECS) were exposed to conditioned media from cultured RAW. Afterwards, cellular function and the pro-inflammatory cytokine levels were investigated. Results The expression of macrophage markers in human LV specimens showed an increase in pro-inflammatory macrophages (M1) and a decrease in regulatory macrophages (M2) as compared to age-matched control mice. HFpEF mice showed increased expression of vascular adhesion molecules (ICAM1, ICAM2, E-selectin) and a significant recruitment of M1 macrophages to the myocardium. Of interest, cardiac function and myocardial inflammation (TNF-a, IL6, IL-1b) were significantly reduced in Ncor1-KO mice. In vitro assays showed that stimulation of macrophages with PA induced M1 type switch and increased NCOR1, TNF-a, IL-6, and IL-1b levels, whereas NCOR1 depletion prevented detrimental changes of macrophage secretome (Fig.2). Conclusion NCOR1 is a crucial co-regulator of immune response and macrophage inflammation in the heart. Our findings pave the way for mechanism-based therapies to prevent HFpEF development.

Development of a lead-free, high-frequency ultrasound transducer with broad bandwidth and enhanced pulse-echo response, employing β-Ni(OH)2/PVDF-TrFE piezoelectric composite

Piezoelectric composite-based high-frequency ultrasound transducers (USTs) have gained significant prominence as a leading and effective tool in non-invasive medical diagnosis, non-destructive testing and diverse industrial applications. In this study, we present the fabrication of a lead-free, high-frequency ultrasound transducer (P-UST), having a very simple and unique assembly procedure. To achieve this, we employed PVDF-TrFE based piezoelectric composite thin film (NTr5) integrated with a transition metal hydroxide (β-Ni(OH)2 nanoplates) and demonstrated its outstanding performance, thereby showcasing its potential in various applications. The composite exhibited exceptional β-phase crystallization, reaching impressive maximum value of 90.04%, owing to the favorable interaction between electrostatic ions and dipoles, along with the presence of strong surface polarization. Upon analysis, remarkable piezoelectric (d33∼50.22 pm/V) and ferroelectric properties were observed in the composite material, accompanied by significant enhancement in surface roughness. Here, the piezoelectric property's impact on UST output is well-explained using Maxwell's equation. P-UST, being a versatile system, is capable of generating and receiving pulse signals. By conducting a pulse-echo test on an aluminium block with ultrasound gel, we achieved a peak-to-peak output voltage of 5.19 V, surpassing the output voltage magnitude of any previously reported UST in this field. The Fast Fourier transformed data of the voltage output revealed an impressive −6 dB bandwidth of 51.22 MHz (102.4%) with a high central frequency of 50.01 MHz. Hence, our P-UST's exceptionally high central frequency promises significantly enhanced ultrasound image resolution, which expands the potential for highly accurate medical assessments for advanced healthcare outcomes and improving patient well-being.

Three-dimensional super resolution ultrasound imaging with a multi-frequency hemispherical phased array.

High resolution imaging of the microvasculature plays an important role in both diagnostic and therapeutic applications in the brain. However, ultrasound pulse-echo sonography imaging the brain vasculatures has been limited to narrow acoustic windows and low frequencies due to the distortion of the skull bone, which sacrifices axial resolution since it is pulse length dependent. To overcome the detect limit, a large aperture 256-module sparse hemispherical transmit/receive array was used to visualize the acoustic emissions of ultrasound-vaporized lipid-coated decafluorobutane nanodroplets flowing through tube phantoms and within rabbit cerebral vasculature in vivo via passive acoustic mapping and super resolution techniques. Nanodroplets were vaporized with 55kHz burst-mode ultrasound (burst length=145μs, burst repetition frequency=9-45Hz, peak negative acoustic pressure=0.10-0.22MPa), which propagates through overlying tissues well without suffering from severe distortions. The resulting emissions were received at a higher frequency (612 or 1224kHz subarray) to improve the resulting spatial resolution during passive beamforming. Normal resolution three-dimensional images were formed using a delay, sum, and integrate beamforming algorithm, and super-resolved images were extracted via Gaussian fitting of the estimated point-spread-function to the normal resolution data. With super resolution techniques, the mean lateral (axial) full-width-at-half-maximum image intensity was 16±3 (32±6)μm, and 7±1 (15±2)μm corresponding to ∼1/67 of the normal resolution at 612 and 1224kHz, respectively. The mean positional uncertainties were ∼1/350 (lateral) and ∼1/180 (axial) of the receive wavelength in water. In addition, a temporal correlation between nanodroplet vaporization and the transmit waveform shape was observed, which may provide the opportunity to enhance the signal-to-noise ratio in future studies. Here, we demonstrate the feasibility of vaporizing nanodroplets via low frequency ultrasound and simultaneously performing spatial mapping via passive beamforming at higher frequencies to improve the resulting spatial resolution of super resolution imaging techniques. This method may enable complete four-dimensional vascular mapping in organs where a hemispherical array could be positioned to surround the target, such as the brain, breast, or testicles.

Research on magneto-acoustic-electrical tomography method based on liquid metal contrast agent and M sequence coded excitation

Magneto-acoustic-electric tomography (MAET) boasts high resolution in ultrasound imaging and high contrast in electrical impedance imaging, making it of significant research value in the fields of early tumor diagnosis and bioelectrical monitoring. In this study, a method was proposed that combined high conductivity liquid metal and maximum length sequence (M sequence) coded excitation to improve the signal-to-noise ratio. It was shown that, under rotational scanning, the liquid metal significantly improved the signal-to-noise ratio of the inter-tissue magneto-acoustic-electric signal and enhanced the quality of the reconstructed image. The signal-to-noise ratio of the signal was increased by 5.6, 11.1, 21.7, and 45.7 times under the excitation of 7-, 15-, 31-, and 63-bit M sequence code, respectively. The total usage time of 31-bit M sequence coded excitation imaging was shortened by 75.6% compared with single-pulse excitation when the same signal-to-noise ratio was improved. In conclusion, the imaging method combining liquid metal and M-sequence coding excitation has positive significance for improving MAET image quality.

Two-step detection of concrete internal condition using array ultrasound and deep learning

Internal defects in concrete structures pose a great risk and potential hazard to the safety and durability of civil structure. Reliable and accurate detection of the internal conditions of structures is a prerequisite for structural safety assessment. To realize the visualization of the internal condition of concrete structures and the identification of defects, this paper proposes a two-step detection method for the internal condition of concrete structures based on array ultrasound and deep learning. First, to address the limited resolution problems of conventional ultrasound synthetic aperture focusing technology (SAFT) imaging and the time-consuming computation of wavefield reconstruction methods such as reverse time migration (RTM) and full waveform inversion (FWI), an array-SAFT imaging algorithm is introduced to balance the resolution and real-time performance of the inspection results. The variational mode decomposition (VMD) and bilateral filtering methods are used before and after the signal focus imaging for signal noise reduction and image edge processing to further improve the resolution of ultrasound images while satisfying the real-time visualization of the detection results. Secondly, since pre-buried objects such as rebar and internal defects are reflected on the surface in ultrasonic images, a method based on the improved YOLOv5 network target detection model is proposed to achieve reliable identification of internal defects and intelligent interpretation of abnormal features in ultrasonic images. The proposed method is effectively validated in the ultrasonic inspection of concrete specimens with different emulated defects pre-built in the laboratory. Additionally, ultrasonic detection is also implemented in the internal defect detection of tunnel secondary linings.

Single Image based Super Resolution Ultrasound Imaging Using Residual Learning of Wavelet Features.

The generation of super resolution ultrasound images from the low-resolution (LR) brightness mode (B-mode) images acquired by the portable point of care ultrasound systems has been of sufficient interest in the recent past. With the advancements in deep learning, there have been numerous attempts in this direction. However, all the approaches have been concentrated on employing the direct image as the input to the neural network. In this work, a stationary wavelet (SWT) decomposition is employed to extract the features from the input LR image which is passed through a modified residual network and the learned features are combined using the inverse SWT to reconstruct the high resolution (HR) image at a 4× scale factor. The proposed approach when compared to the state-of-the art approaches, results in an improved high resolution reconstruction.Clinical relevance- The proposed approach will enable the generation of high-resolution images from portable ultrasound systems, allowing for easier interpretation and faster diagnostics in primary care settings.

Pulse Stretching Correction for Improving Ultrasound Image Resolution.

Recent advances in ultrasound technology have led to the development of wide band large-aperture transducer arrays that can provide high-resolution images with deeper imaging depth using delay-and-sum synthetic aperture (SA) imaging techniques. However, imaging with long array signals may result in resolution degradation and image aliasing due to pulse stretching at the near field where large angle of reflection often occurs. To address this issue, this paper proposes a solution known as pulse stretching correction (PSC). The PSC method involves mathematically developing a pulse stretching model and reformulating the delay-and-sum SA equation into a common-angle form. Pulse stretching is then corrected in the frequency domain to reduce or eliminate it in the reflection angle domain. The effectiveness of this method is demonstrated through simulations and experimental results, which show that it can effectively suppress shallow noise and improve image resolution.

Initial results of a prospective, multicenter study on the use of a biosynthesized cellulose membrane as a separation in de novo CIED implantations

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Public Grant: EIC Accelerator pilot –SME Instrument (H2020-EIC-SMEInst-2018-2020) Private Company: Hylomorph AG Background Fibrosis is the result of foreign body reaction against the hardware of cardiac implantable electronic devices (CIEDs). Generator pocket fibrotic adhesions increase the complexity and risks of CIED revision surgeries. There is limited evidence that synthetic or ex vivo resorbable envelopes can prevent or reduce fibrosis. Objective We conducted a multicenter, prospective, single-arm, interventional study of microengineered biosynthesized cellulose (BC) as separation layer for CIED pockets. Patients who were undergoing a CIED de novo implantation were included to receive the protective device. The primary end point was the maintenance of a detectable barrier between the CIED generator and the surrounding tissue at 12 months. The secondary end point for safety was procedure-related or system-related complications within 12 months. Methods High resolution ultrasound (HRUS) imaging of the protective layer, the CIED and the surrounding tissue of the generator pocket were collected at the day of implant, and at one, six-, and twelve-months follow-up visits in 35 patients (of 46 recruited). The images were individually processed and analyzed. Results HRUS imaging allowed the reproducible detection of (1) the CIED interface as fiducial reference marker, (2) the densification of the surrounding tissue and (3) the segregation between the two. A detectable separation between the CIED and the surrounding tissue was identified in all patients at all follow-up visits demonstrating the enforcement of a durable and stable barrier. The evolution of HRUS signal intensity indicated a time-dependent densification compatible with the buildup of fibrotic tissue around the generator. The thickness of this tissue was measured rendering values below 1 mm. Mean time for cellulose membrane application was 7.5±4 minutes. Investigational device related major complications were 0%. Conclusion Adjunctive use of an antifibrotic device established a durable separation between the CIED and the surrounding soft tissue in the generator pocket without a significantly higher incidence of complications or a relevantly increased procedural time. HRUS imaging indicated a limited buildup of denser tissue around the generator and no adhesions inside the cellulose membrane. Altogether these results suggest that BC may be beneficial for post-implantation pocket health as an alternative to envelopes of synthetic or animal origin and may enable easy pocket access in revision procedures.

Novel techniques in performing extraoral ultrasound in diagnosing dental abscesses

IntroductionDental abscesses are a common reason patients present to the emergency department (ED). Facial and dental imaging are sometimes necessary to support the clinical diagnosis. While radiographs and computed tomography scans are frequently used, point-of-care ultrasound (US) offers several advantages, including decreased radiation exposure, reduced costs, and shorter patient length of stay. This report outlines the use of US in evaluating patients with suspected dental abscesses in the ED. DiscussionThe typical orofacial US techniques include examining the affected area for cobblestoning or fluid collections. To improve the accuracy of diagnoses, novel techniques such as the Oral Hydroscan (OHS) and the Tongue Pointing Techniques (TPT) may be employed in specific cases. The OHS utilizes a water-filled oral cavity to enhance the spatial resolution of ultrasound images, providing improved visualization of near-field structures and preventing air accumulation between the gingiva and the buccal mucosa. The TPT involves asking the patient to extend their tongue towards the affected area, pointing to the painful location, and serving as a visual reference for an extraoral US. ConclusionUS presents several advantages as an alternative imaging method for patients with suspected dental abscesses in the ED. Using innovative techniques such as the OHS and the TPT can further increase the visibility of tissue planes and help define the area of interest in these cases.

Implementation of constrained swept synthetic aperture using a mechanical fixture.

Resolution and target detectability in ultrasound imaging are directly tied to the size of the imaging array. This is especially important for imaging at depth, such as in the detection and diagnosis of hepatocellular carcinoma and other lesions in the liver. Swept synthetic aperture (SSA) imaging has shown promise for building large effective apertures from small physical arrays using motion, but has required bulky fixtures and external motion tracking for precise positioning. In this study we present an approach that constrains the transducer motion with a simple linear sliding fixture and estimates motion from the ultrasound data itself using either speckle tracking or channel correlation. We demonstrate in simulation and phantom experiments the ability of both techniques to accurately estimate lateral transducer motion and form SSA images with improved resolution and target detectability. We observed errors under 83 μm across a 50 mm sweep in simulation and found improvements of up to 61% in resolution and up to 33% in lesion detectability experimentally even imaging through ex vivo tissue layers. This approach will increase the accessibility of SSA imaging and allow us to test its use in clinical settings.

CORRELATION OF PRE OPERATIVE AND INTRA OPERATIVE FINDINGS IN GALL STONE DISEASE

Background: Gallstone disease (GD) is a chronic recurrent hepatobiliary disease, the basis for which is the impaired metabolism of cholesterol, bilirubin and bile acids which is characterized by the formation of gallstones in the hepatic bile duct, common bile duct or gallbladder. GD is one of the most prevalent gastrointestinal diseases with a substantial burden to health care systems. GD can result in serious outcomes such as acute gallstone pancreatitis and gallbladder cancer. The epidemiology, correlation with USG ndings and anatomical variants encountered during cholecystectomy are discussed in this study. To compare preoperative and intraope Aims and objectives: rative ndings of gall stone disease in patients who underwent cholecystectomy. 104 patients who underwent cholecystect Materials and methods: omy in Kanyakumari Government Medical College from January 2019 to December 2022. Majority of the patients were Results: in age group between 19 to 78 years of age with a mean age of 42.5. Female predominance was reported in our study comprising 65% of total study participants. The study identied a sensitivity of 0.84 and a specicity of 1 for ultrasound in the identication of gallstones.10 % of the study population had different anatomical variants which were encountered during cholecystectomy. <4% of study population was intraoperatively converted from laparoscopic to open cholecystectomy. 78% of stones are pigment stones based on dry weight analysis. Reproductive Age and fe Summary: male sex are profoundly associated with the incidence of gallstone disease. This study suggests that despite an evolution in the resolution of ultrasound imaging, there has not been a corresponding improvement in sensitivity. Knowledge of Variation in cystic duct anatomy is important to avoid inadvertent injury to the biliary tree during Cholecystectomy. The study population have a predominance of pigment stones based on dry weight analysis