A single-master database has limited update capacity because a single node handles all updates. A multi-master database potentially has higher update capacity because the load is spread across multiple nodes. However, the need to coordinate updates and ensure durability can generate high network traffic. Reducing network load is particularly important in a cloud environment where the network infrastructure is shared among thousands of tenants. In this paper, we present Taurus MM, a shared-storage multi-master database optimized for cloud environments. It implements two novel algorithms aimed at reducing network traffic plus a number of additional optimizations. The first algorithm is a new type of distributed clock that combines the small size of Lamport clocks with the effective support of distributed snapshots of vector clocks. The second algorithm is a new hybrid page and row locking protocol that significantly reduces the number of lock requests sent over the network. Experimental results on a cluster with up to eight masters demonstrate superior performance compared to Aurora multi-master and CockroachDB.

Robotic-assisted catheter insertion is becoming increasingly popular due to its potential applications including cardiac catheterization. Typically, catheters are tracked during insertion procedures to verify the location of the tip relative to anatomy or features of interest. To this end, many catheter tracking systems have been proposed in the literature. Current approaches such as visual servoing are computationally intensive and sometimes require harmful ionizing radiation (X-rays) for tip localization. Conversely, other approaches use 3D ultrasound probes which can be prohibitively expensive. In contrast, we propose an ultrasound-enabled robotic catheter tracking system that uses a 2D ultrasound probe and an active piezoelectric element to track the tip of a catheter. This approach has the potential to guide catheters from initial insertion, in a vein of the groin, to final placement at a target area inside of the heart. During the tracking process, no information from the ultrasound image is necessary; however, this information can be used to help clinicians guide the catheter or to perform diagnostic procedures. In this paper, we outline this procedure by first discussing the individual components of the system and then by describing our methodology for tracking the catheter tip. Next, we simulate the system in ROS to test its effectiveness, and finally we experimentally verify that a robotic arm equipped with a 2D ultrasound probe can track a catheter in a multi-vein phantom. Furthermore, the data collected during tracking can be used to virtually reconstruct the 3D structure of veins while tracking.

Photoacoustic (PA) images utilize pulsed lasers and ultrasound transducers to visualize targets with higher optical absorption than the surrounding medium. However, they are susceptible to acoustic clutter and background noise artifacts that obfuscate biomedical structures of interest. We investigated three spatial-angular compounding methods to improve PA image quality for biomedical applications, implemented by combining multiple images acquired as an ultrasound probe was rotated about the elevational axis with the laser beam and target fixed. Compounding with conventional averaging was based on the pose information of each PA image, while compounding with weighted and selective averaging utilized both the pose and image content information. Weighted-average compounding enhanced PA images with the least distortion of signal size, particularly when there were large (i.e., 2.5 mm and 7 (°)) perturbations from the initial probe position. Selective-average compounding offered the best improvement in image quality with up 181, 1665, and 1568 times higher contrast, CNR, and SNR, respectively, compared to the mean values of individual PA images. The three presented spatial compounding methods have promising potential to enhance image quality in multiple photoacoustic applications.

We are investigating the use of photoacoustic (PA) imaging to detect critical structures, such as the carotid artery, that may be located behind the bone being drilled during robot-assisted endonasal transsphenoidal surgery. In this system, the laser is mounted on the drill (via an optical fiber) and the 2D ultrasound (US) probe is placed elsewhere on the skull. Both the drill and the US probe are tracked relative to the patient reference frame. PA imaging provides two advantages compared to conventional B-mode US: (1) the laser penetrates thin layers of bone, and (2) the PA image displays targets that are in the laser path. Thus, the laser can be used to (non-invasively) extend the drill axis, thereby enabling reliable detection of critical structures that may reside in the drill path. This setup creates a challenging alignment problem, however, because the US probe must be placed so that its image plane intersects the laser line in the neighborhood of the target anatomy (as estimated from preoperative images). This paper reports on a navigation system developed to assist with this task, and the results of phantom experiments that demonstrate that a critical structure can be detected with an accuracy of approximately 1 mm relative to the drill tip.

Radiofrequency ablation (RFA) is an established treatment for liver cancer when resection is not possible. Yet, its optimal delivery is challenged by the presence of large blood vessels and the time-varying thermal conductivity of biological tissue. Incomplete treatment and an increased risk of recurrence are therefore common. A tool that would enable the accurate planning of RFA is hence necessary. This manuscript describes a new method to compute the extent of ablation required based on the Lattice Boltzmann Method (LBM) and patient-specific, pre-operative images. A detailed anatomical model of the liver is obtained from volumetric images. Then a computational model of heat diffusion, cellular necrosis, and blood flow through the vessels and liver is employed to compute the extent of ablated tissue given the probe location, ablation duration and biological parameters. The model was verified against an analytical solution, showing good fidelity. We also evaluated the predictive power of the proposed framework on ten patients who underwent RFA, for whom pre- and post-operative images were available. Comparisons between the computed ablation extent and ground truth, as observed in postoperative images, were promising (DICE index: 42%, sensitivity: 67%, positive predictive value: 38%). The importance of considering liver perfusion while simulating electrical-heating ablation was also highlighted. Implemented on graphics processing units (GPU), our method simulates 1 minute of ablation in 1.14 minutes, allowing near real-time computation.

Ultrasound imaging can be an advantageous imaging modality for image guided surgery. When using ultrasound imaging (or any imaging modality), calibration is important when more advanced forms of guidance, such as augmented reality systems, are used. There are many different methods of calibration, but the goal of each is to recover the rigid body transformation relating the pose of the probe to the ultrasound image frame. This paper presents a unified algorithm that can solve the ultrasound calibration problem for various calibration methodologies. The algorithm uses gradient descent optimization on the Euclidean Group. It can be used in real time, also serving as a way to update the calibration parameters on-line. We also show how filtering, based on the theory of invariants, can further improve the online results. Focusing on two specific calibration methodologies, the AX = XB problem and the BX-1p problem, we demonstrate the efficacy of the algorithm in both simulation and experimentation.

The AX = XB sensor calibration problem must often be solved in image guided therapy systems, such as those used in robotic surgical procedures. In this problem, A, X, and B are homogeneous transformations with A and B acquired from sensor measurements and X being the unknown. It has been known for decades that this problem is solvable for X when a set of exactly measured A's and B's, in a priori correspondence, is given. However, in practical problems, the data streams containing the A' and B's will be asynchronous and may contain gaps (i.e., the correspondence is unknown, or does not exist, for the sensor measurements) and temporal registration is required. For the AX = XB problem, an exact solution can be found when four independent invariant quantities exist between two pairs of A's and B's. We formally define these invariants, reviewing and elaborating results from classical screw theory. We then illustrate how they can be used, with sensor data from multiple sources that contain unknown or missing correspondences, to provide a solution for X.

This paper introduces two real-time elastography techniques based on analytic minimization (AM) of regularized cost functions. The first method (1D AM) produces axial strain and integer lateral displacement, while the second method (2D AM) produces both axial and lateral strains. The cost functions incorporate similarity of radio-frequency (RF) data intensity and displacement continuity, making both AM methods robust to small decorrelations present throughout the image. We also exploit techniques from robust statistics to make the methods resistant to large local decorrelations. We further introduce Kalman filtering for calculating the strain field from the displacement field given by the AM methods. Simulation and phantom experiments show that both methods generate strain images with high SNR, CNR and resolution. Both methods work for strains as high as 10% and run in real-time. We also present in vivo patient trials of ablation monitoring. An implementation of the 2D AM method as well as phantom and clinical RF-data can be downloaded.

This paper introduces a 2-D strain imaging technique based on minimizing a cost function using dynamic programming (DP). The cost function incorporates similarity of echo amplitudes and displacement continuity. Since tissue deformations are smooth, the incorporation of the smoothness into the cost function results in reduced decorrelation noise. As a result, the method generates high-quality strain images of freehand palpation elastography with up to 10% compression, showing that the method is more robust to signal decorrelation (caused by scatterer motion in high axial compression and nonaxial motions of the probe) in comparison to the standard correlation techniques. The method operates in less than 1 s and is thus also potentially suitable for real time elastography.

There has been increased interest in minimally invasive ablative treatments that typically require precise placement of the ablator tool to meet the predefined planning and lead to efficient tumor destruction. Standard ablative procedures involve free hand transcutaneous ultrasonography (TCUS) in conjunction with manual tool positioning. Unfortunately, existing TCUS systems suffer from many limitations and result in failure to identify nearly half of all treatable liver lesions. Freehand manipulation of the ultrasound (US) probe and ablator tool lacks the critical level of control, accuracy, stability, and guaranteed performance required for these procedures. Freehand US results in undefined gap distribution, anatomic deformation due to variable pressure from the sonographer's hand, and severe difficulty in maintaining optimal scanning position. In response to these limitations, we propose the use of a dual robotic arm system that manages both ultrasound manipulation and needle guidance. We report a prototype of the dual arm system and a comparative performance analysis between robotic vs. freehand systems, for both US scanning and needle placement in mechanical and animal tissue phantoms.

Surgical robots, including those with remote center of motion (RCM) mechanisms, have demonstrated utility in image-guided percutaneous needle placement procedures. However, widespread clinical application of these robots is hindered by not only complicated mechanical design but also the need for calibration and registration of the robot to the medical imager prior to each use. In response, we propose a Virtual RCM algorithm that requires only online tracking or registering the surgical tool to the imager, and a five degree-of-freedom (DOF) robot comprised of three prismatic DOF decoupled from two rotational DOF. The robot can be unencoded, uncalibrated, and does not require pre-operative registration. An incremental adaptive motion control cycle both guides the needle to the insertion point and orients it to align with the target. The robot executes RCM motion “virtually” without having a physically constrained fulcrum point. The proof-of-concept prototype system achieved 0.78 mm translation and 1.4 degrees rotational accuracy (within the tracker accuracy), within 17 iterative steps (0.5–1s).