Aiming at an intelligent point-of-care imaging technology for rheumatology clinics, a fully automatic 3D photoacoustic (PA) and ultrasound (US) dual-modality system driven by a robot and powered by deep learning (DL)-based image processing was developed. Automated scanning of volumetric images from patient joints, plus DL-based tissue segmentation and quantification of imaging biomarkers, ensures that the measurements from this system are objective and reproducible. Clinical validation was conducted via a longitudinal study on 43 finger joints from patients affected by inflammatory arthritis. Using manual segmentations as the gold standard, our DL algorithm utilizing the 3D Deep Attentive Feature (DAF3D) model showed satisfactory performance in automatic segmentation of joint space and synovial region and achieved a Dice score of 0.77±0.03 and an IoU of 0.64±0.03. Based on the tissue segmentations facilitated by the DAF3D model, six volumetric imaging biomarkers reflecting the activity of arthritis and its change in response to the treatment were quantified, including hyperemia, blood oxygenation, US power Doppler, joint space echogenicity, joint space volume, and synovial volume. The imaging biomarkers quantified from DL-based segmentation and manual segmentation showed moderate to strong correlations (R: 0.41−0.86). Hyperemia quantified from PA imaging has the strongest association with the disease activity indicated by Clinical Assessment Questionnaire (CAQ) scores, with R2=0.41. Linear models combining the two biomarkers from PA imaging, the four biomarkers from US imaging, and all six imaging biomarkers rendered moderate to very strong associations with the disease activity scores, with R2 of 0.41, 0.26, and 0.52, respectively.

Ultrasound (US) imaging is a standard clinical tool for assessment of soft tissue inflammation, particularly for imaging the peripheral joints of the hands and feet, which are usually the first to be affected by rheumatoid arthritis. Robotic US has recently gained attention as a means of delivering repeatable and operator-independent imaging. Previously developed robotic US for 3D volumetric imaging of human finger joints relies on the linear scan of the target joint to collect a series of 2D B-mode US images. Although effective in many contexts and simple in control and image reconstruction, the linear scan is suboptimal for imaging cylindrical tissue structures such as human fingers, often exhibiting degraded image quality in peripheral regions due to poor angular alignment with the tissue surface. To address this limitation, we developed a robotic US system incorporating an arc-shaped scanning trajectory designed to maintain a consistently perpendicular orientation to the curved surface of the finger throughout the scan. In an experiment on a spherical phantom, arc scan yielded improved boundary sharpness and image contrast compared to linear scan. In a clinical study involving both a healthy volunteer and an arthritis patient, the arc scan produced better B-mode US image quality in imaging finger joints compared to the linear scan, as reflected in more consistent representations of phalangeal and soft tissue structures along different radial directions. Functional Doppler US and photoacoustic (PA) imaging of finger joints were also conducted. Arc scan and linear scan achieved comparable detection of vascular signals. These findings demonstrate that arc scan facilitated by the robotic arm can achieve improved B-mode US imaging of tissue anatomy in human finger joints while preserving the functional imaging capability of Doppler US and PA imaging.

Ultrasonographic optic nerve sheath diameter (ONSD) is a non-invasive intracranial pressure (ICP) surrogate. This article discusses the effect of ultrasound settings and imaging artifacts on ONSD assessment. Ultrasound settings that may affect ONSD assessment include gain, dynamic range, frequency, harmonic imaging, and focal zones. Artifacts can be related to imaged structures (acoustic shadowing, enhancement, comet tail, and speckle artifacts) or to beam properties (partial volume and refraction artifacts). In addition, optic nerve sheath (ONS) properties such as echogenicity changes based on ICP or ONS kinking are discussed.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that can cause severe joint damage and functional impairment. Ultrasound imaging has shown promise in providing real-time assessment of synovium inflammation associated with the early stages of RA. Accurate segmentation of the synovium region and quantification of inflammation-specific imaging biomarkers are crucial for assessing and grading RA. However, automatic segmentation of the synovium in 3D ultrasound is challenging due to ambiguous boundaries, variability in synovium shape, and inhomogeneous intensity distribution. In this work, we introduce a novel network architecture, Swin Transformers with Deep Attentive Features for 3D segmentation (SwinDAF3D), which integrates Swin Transformers into a Deep Attentive Features framework. The developed architecture leverages the hierarchical structure and shifted windows of Swin Transformers to capture rich, multi-scale and attentive contextual information, improving the modeling of long-range dependencies and spatial hierarchies in 3D ultrasound images. In a six-fold cross-validation study with 3D ultrasound images of RA patients’ finger joints (n = 72), our SwinDAF3D model achieved the highest performance with a Dice Score (DSC) of 0.838 ± 0.013, an Intersection over Union (IoU) of 0.719 ± 0.019, and Surface Dice Score (SDSC) of 0.852 ± 0.020, compared to 3D UNet (DSC: 0.742 ± 0.025; IoU: 0.589 ± 0.031; SDSC: 0.661 ± 0.029), DAF3D (DSC: 0.813 ± 0.017; IoU: 0.689 ± 0.022; SDSC: 0.817 ± 0.013), Swin UNETR (DSC: 0.808 ± 0.025; IoU: 0.678 ± 0.032; SDSC: 0.822 ± 0.039), UNETR++ (DSC: 0.810 ± 0.014; IoU: 0.684 ± 0.018; SDSC: 0.829 ± 0.027) and TransUNet (DSC: 0.818 ± 0.013; IoU: 0.692 ± 0.017; SDSC: 0.815 ± 0.016) models. This ablation study demonstrates the effectiveness of combining a Swin Transformers feature pyramid with a deep attention mechanism, improving the segmentation accuracy of the synovium in 3D ultrasound. This advancement shows great promise in enabling more efficient and standardized RA screening using ultrasound imaging.

Ultrasound imaging has shown promise in assessing synovium inflammation associated early stages of rheumatoid arthritis (RA). The precise identification of the synovium and the quantification of inflammation-specific imaging biomarkers is a crucial aspect of accurately quantifying and grading RA. In this study, a deep learning-based approach is presented that automates the segmentation of the synovium in ultrasound images of finger joints affected by RA. Two convolutional neural network architectures for image segmentation were trained and validated in a limited number of 2-D images, extracted from N = 18 3-D ultrasound volumes acquired from N = 9 RA patients, with sparse ground truth annotations of the synovium. Various augmentation strategies were employed to enhance the diversity and size of the training dataset. The utilization of geometric and noise augmentation transforms resulted in the highest dice score (0.768 +/- 0 . 031 , N = 6 ) , and intersection over union ( 0 . 624 +/- 0.040, N = 6), as determined via six-fold cross-validation. In addition, the segmentation model is used to generate dense 3-D segmentation maps in the ultrasound volumes, based on the available sparse annotations. The developed technique shows promise in facilitating more efficient and standardized workflow for RA screening using ultrasound imaging.

Aiming at a point-of-care device for rheumatology clinics, we developed an automatic 3-D imaging system combining the emerging photoacoustic (PA) imaging with conventional Doppler ultrasound (US) for detecting human inflammatory arthritis. This system is based on a commercial-grade GE HealthCare (GEHC, Chicago, IL, USA) Vivid E95 US machine and a Universal Robot UR3 robotic arm. This system automatically locates the patient's finger joints from a photograph taken by an overhead camera powered by an automatic hand joint identification method, followed by the robotic arm moving the imaging probe to the targeted joint to scan and obtain 3-D PA and Doppler US images. The GEHC US machine was modified to enable high-speed, high-resolution PA imaging while maintaining the features available on the system. The commercial-grade image quality and the high sensitivity in detecting inflammation in peripheral joints via PA technology hold great potential to significantly benefit clinical care of inflammatory arthritis in a novel way.

Based on the observations made in rheumatology clinics, autoimmune disease (AD) patients on immunosuppressive (IS) medications have variable vaccine site inflammation responses, whose study may help predict the long-term efficacy of the vaccine in this at-risk population. However, the quantitative assessment of the inflammation of the vaccine site is technically challenging. In this study analyzing AD patients on IS medications and normal control subjects, we imaged the inflammation of the vaccine site 24 h after mRNA COVID-19 vaccinations were administered using both the emerging photoacoustic imaging (PAI) method and the established Doppler ultrasound (US) method. A total of 15 subjects were involved, including 6 AD patients on IS and 9 normal control subjects, and the results from the two groups were compared. Compared to the results obtained from the control subjects, the AD patients on IS medications showed statistically significant reductions in vaccine site inflammation, indicating that immunosuppressed AD patients also experience local inflammation after mRNA vaccination but not in as clinically apparent of a manner when compared to non-immunosuppressed non-AD individuals. Both PAI and Doppler US were able to detect mRNA COVID-19 vaccine-induced local inflammation. PAI, based on the optical absorption contrast, shows better sensitivity in assessing and quantifying the spatially distributed inflammation in soft tissues at the vaccine site.

Aiming at clinical translation, we developed an automatic 3D imaging system combining the emerging photoacoustic imaging with conventional Doppler ultrasound for detecting inflammatory arthritis. This system was built with a GE HealthCare (GEHC) Vivid™ E95 ultrasound system and a Universal Robot UR3 robotic arm. In this work, the performance of this system was examined with a longitudinal study utilizing a clinically relevant adjuvant induced arthritis (AIA) murine model. After adjuvant injection, daily imaging of the rat ankle joints was conducted until joint inflammation was obvious based on visual inspection. Processed imaging results and statistical analyses indicated that both the hyperemia (enhanced blood volume) detected by photoacoustic imaging and the enhanced blood flow detected by Doppler ultrasound reflected the progress of joint inflammation. However, photoacoustic imaging, by leveraging the highly sensitive optical contrast, detected inflammation earlier than Doppler ultrasound, and also showed changes that are more statistically significant. This side-by-side comparison between photoacoustic imaging and Doppler ultrasound using the same commercial grade GEHC ultrasound machine demonstrates the advantage and potential value of the emerging photoacoustic imaging for rheumatology clinical care of arthritis.

When comparing the performance of different industrial X-ray computed tomography (CT) systems, reconstruction algorithms, or scan protocols, it is important to assess how well the required inspection and measurement tasks can be performed. Furthermore, it can be very informative to quantify image quality (IQ) metrics that can provide insight into the IQ characteristics that lead to the resulting inspection or measurement task performance. Inspection and measurement task performance is determined by basic characteristics such as spatial resolution; feature contrast, size, and shape; random noise (noise due to statistical uncertainty in measurements); and image artifacts. In this report, we describe a modular phantom set that enables robustly quantifying these characteristics and also enables assessing the performance of the inspection or measurement tasks themselves. The phantom set includes two phantom bodies and several insert types that can be optionally installed in the bodies. Phantom body extensions can be optionally included to increase scatter. The phantom bodies combined with the available insert types can comprehensively evaluate all important IQ metrics and inspection or measurement tasks. The precisely-known phantom body geometry and insert location, geometry, and orientation supports automatic analysis of large, complex experiments of multiple variables. This phantom set, with the associated image analysis software, could potentially serve as a general evaluation method for non-destructive testing (NDT) CT.

OBJECTIVE:The objective is enhanced ophthalmic ultrasound imaging to monitor ocular structure and intracranial dynamics changes related to visual impairment and intracranial pressure (ICP) induced by microgravity. The goals are to improve the ease of use and reduce operator variability by automatically rendering improved views of the anatomy and deriving new metrics of the morphology and dynamics.MATERIALS AND METHODS:A prototype three-dimensional (3-D) probe was integrated onto a portable ultrasound scanner. Image analysis algorithms were developed to automatically detect the ocular anatomy and simultaneously render views of the optic nerve with improved sheath definition. Curvature metrics were calculated from 3-D retinal surfaces to quantify posterior globe flattening, and tissue velocity waveforms of the optic nerve were analyzed to assess intracranial dynamics.RESULTS:New 3-D structural measurements were evaluated in a head-down tilt study. The response of optic nerve sheath and globe flattening metrics were quantified in 11 healthy volunteers from baseline to moderately elevated ICP. The optic nerve measurements showed good correlation with existing two-dimensional (2-D) methods and an acute response to increased ICP, while globe flattening did not show an acute response. The tissue velocities were evaluated in a porcine model from baseline to significantly elevated ICP and correlated with invasive ICP readings in four animals.CONCLUSIONS:Volumetric ophthalmic imaging was demonstrated on a portable ultrasound system and structural measurements validated with existing methods. New 3-D structural measurements and dynamic measurements were evaluation during in vivo studies. Further investigations are needed to evaluate improvements in performance for non-experts and application to clinically relevant conditions.

The detection and quantification of carotid artery stenosis guides the decision of the need for surgical intervention such as endarterectomy or stenting of a patient. Contrast enhanced ultrasound highlights detailed information on hemodynamics and even on pathophysiology, and 3D scans yield full in-situ information of the complete anatomy. We introduce 3D image analysis algorithms that enable quantification and visualization of the carotid lumen in these scans. Our method enhances the lumen intensity contrast, extracts lumen centerlines using a gradient concentration calculation, and then uses a graph search method to obtain a coarse lumen segmentation, which is refined through a level set method applied on an intensity-corrected image. We processed 35 images acquired from 7 patients, and demonstrated quantitative comparisons with MR/CT lumen segmentations. We obtained a segmentation correlation score of R-2 = 0.94 and a coefficient of 0.99 for US versus MR/CT through a linear regression on effective diameters extracted from slices in the XY-plane.

Introduction: Vulnerable plaque is marked by increased intra-plaque vascularity, hemorrhage and is associated with cerebrovascular (CV) events. Current use of 2D CEUS carotid imaging does not provide a true volumetric representation of the intra-plaque angiogenesis. Therefore, this is the first clinical trial using 3D CEUS to assess intra-plaque angiogenesis, in patients with clinically significant plaques prior to carotid endarterectomy (CEA). Methods: Data were acquired on 11 patients that were asked to participate in this pilot study prior to CEA. All patients had MR and/or CT angiogram as part of their standard care and all carotid artery plaque specimens were collected and sent for histologic evaluation. CEUS volumes were acquired using a GE LOGIQ E9, RSP6-16 4D probe and Optison. In order to assess plaque vulnerability, semi-automatic image analysis algorithms were developed to segment the lumen, plaque, and intra-plaque vascularity. Measurements derived from 3D CEUS images were validated against CT, MR, and histology. Results: Preliminary results from 3 patients were analyzed and lumen volumes were segmented from all available modalities (figure shows CEUS example). Lumen measurements of the CCA, ICA, and ECA from 3D CEUS were compared to MR or CT. High correlation is shown in the figure for these patients (R 2 =0.95). Further analysis of the 3D CEUS images demonstrates that we can identify intra-plaque vascularity (see segmentation from one patient). These CEUS results estimated an average cross-sectional vascular density of 7-11% in the plaque, matching histology estimates of 10% from a single slice. Conclusion: These results demonstrate that it is feasible to use 3D CEUS for quantitative, volumetric imaging of the carotid artery and intra-plaque angiogenesis in patients scheduled for CEA. Future work will extend this analysis to all patients and make a quantitative comparison of intra-plaque vascular density from 3D CEUS vs. multiple histology slices.

Objectives The primary objective was to test in vivo for the first time the general operation of a new multifunctional intracardiac echocardiography (ICE) catheter constructed with a microlinear capacitive micromachined ultrasound transducer (ML‐CMUT) imaging array. Secondarily, we examined the compatibility of this catheter with electroanatomic mapping (EAM) guidance and also as a radiofrequency ablation (RFA) catheter. Preliminary thermal strain imaging (TSI)‐derived temperature data were obtained from within the endocardium simultaneously during RFA to show the feasibility of direct ablation guidance procedures. Methods The new 9F forward‐looking ICE catheter was constructed with 3 complementary technologies: a CMUT imaging array with a custom electronic array buffer, catheter surface electrodes for EAM guidance, and a special ablation tip, that permits simultaneous TSI and RFA. In vivo imaging studies of 5 anesthetized porcine models with 5 CMUT catheters were performed. Results The ML‐CMUT ICE catheter provided high‐resolution real‐time wideband 2‐dimensional (2D) images at greater than 8 MHz and is capable of both RFA and EAM guidance. Although the 24‐element array aperture dimension is only 1.5 mm, the imaging depth of penetration is greater than 30 mm. The specially designed ultrasound‐compatible metalized plastic tip allowed simultaneous imaging during ablation and direct acquisition of TSI data for tissue ablation temperatures. Postprocessing analysis showed a first‐order correlation between TSI and temperature, permitting early development temperature‐time relationships at specific myocardial ablation sites. Conclusions Multifunctional forward‐looking ML‐CMUT ICE catheters, with simultaneous intracardiac guidance, ultrasound imaging, and RFA, may offer a new means to improve interventional ablation procedures.

In this paper we describe a fast method to segment and track a vessel of interest in 4D (i.e. 3D + time) Ultrasound images. An initial 2D seed is used to initialize a single spatial Kalman-Filter tracker which tracks the vessel center-line in 3D. The 3D vessel is then segmented using a fast area weighted active contour method. This segmented 3D vessel is then tracked across multiple time-points using a set of temporal Kalman- Filters which track the movement of the center of the vessel in each 2D slice. The vessel boundaries are estimated by growing an area weighted active contour outward from the centerline. Based on qualitative as well as quantitative performance measures, the proposed method shows promising tracking results on numerous phantom as well as patient datasets.

Background: The standard approach for the catheter ablation of arrhythmias and the accompanying guidance of intracardiac echocardiography (ICE) is performed from the endocardial aspect of the heart. This approach may now also include percutaneous epicardial mapping which has been developed and utilized in several electrophysiology (EP) laboratories. Previous research using percutaneous pericardial procedures has been safely and effectively applied to a range of supraventricular arrhythmia substrates. Methods: Our family of ICE devices now includes a new 9F forward-looking catheter which is constructed with a silicon based capacitive micromachined ultrasonic transducer (CMUT) technology. Several CMUT ML catheters have been tested in 3 porcine experiments. The percutaneous epicardial approach was tested with successful imaging of heart chambers from the oblique sinus of the pericardial space. Results: This micro-linear (ML) catheter provides high-resolution, real-time, 2D ultrasound images at 14 MHz, and is capable of both electrical mapping and electroanatomical guidance. Although the 24 element array aperture is small (1.6 mm), the imaging depth of penetration is considerable (> 3 cm). A thin metalized cap can be placed over the array for performing radiofrequency ablation without distorting imaging. Conclusions: This silicon based technology has the unique prospect of enhancing both miniaturization and multiple modalities of operation.

Atrial fibrillation, the most common type of cardiac arrhythmia, now affects more than 2.2 million adults in the US alone. Currently, electrophysiological interventions are performed under fluoroscopy guidance, a procedure that introduces harmful ionizing radiation without providing adequate soft-tissue resolution. Intracardiac echocardiography (ICE) provides real-time, high-resolution anatomical information, reduces fluoroscopy time, and enhances procedural success. We have previously developed a forward-looking, volumetric ICE catheter using a ring-shaped, 64-element capacitive micromachined ultrasonic transducer (CMUT) array with a 10MHz center frequency. The Ring array was flip-chip bonded to a flexible PCB along with 8 identical custom ASICs providing a total of 64 dedicated preamplifiers. The flex was then reshaped for integration with the catheter shaft. In the second-generation catheter, 72 micro-coaxial cables (reduced from 100) are terminated on a newly designed flex to provide the connection between the array electronics and the imaging system. The reduced number of cables enhances the catheter's steerability. Furthermore, the new flex allows grounding of the top CMUT electrode through proper level-shifting of the ASIC supplies without additional circuitry. This feature enables complete ground shielding of the catheter, which improves its noise susceptibility and is an important safety measure for its clinical use. Beyond real-time, forward-looking imaging capability, the Ring catheter provides a continuous central lumen, enabling convenient delivery of other devices such as HIFU transducers, RF ablation catheters, etc. Using a PC-based imaging platform from Verasonics and a commercial Vivid7 imaging system from GE, we have demonstrated the in vivo, volumetric, real-time imaging capability of the finalized Ring catheter in a pig heart.

The apparent arterial compliance observed from noninvasive measurements of arterial blood pressure and cross-sectional area does not directly correspond to the static compliance curve or dynamic pressure-area loop. The accuracy of continuous, non-invasive blood pressure estimates based on calibrated arterial compliance models to convert area to pressure is affected by both changes in the viscoelastic properties of the artery and in the pressure waveform. The relationship of the apparent compliance curve to static compliance and to dynamic pressure-area loop is demonstrated using a simulated linear viscoelastic model, and the sensitivity of blood pressure estimates to changes in the viscoelastic parameters and pressure waveform is investigated. Changes in the elastic component of the viscoelastic model had the largest effect with a 25% change in the modulus of elasticity resulting in a 5.5% error in the systolic blood pressure estimate.