Randomized trial of conventional transseptal needle versus radiofrequency energy needle puncture for left atrial access (the TRAVERSE-LA study).

BackgroundTransseptal puncture is a critical step in achieving left atrial (LA) access for a variety of cardiac procedures. Although the mechanical Brockenbrough needle has historically been used for this procedure, a needle employing radiofrequency (RF) energy has more recently been approved for clinical use. We sought to investigate the comparative effectiveness of an RF versus conventional needle for transseptal LA access.Methods and resultsIn this prospective, single-blinded, controlled trial, 72 patients were randomized in a 1:1 fashion to an RF versus conventional (BRK-1) transseptal needle. In an intention-to-treat analysis, the primary outcome was time required for transseptal LA access. Secondary outcomes included failure of the assigned needle, visible plastic dilator shavings from needle introduction, and any procedural complication. The median transseptal puncture time was 68% shorter using the RF needle compared with the conventional needle (2.3 minutes [interquartile range {IQR}, 1.7 to 3.8 minutes] versus 7.3 minutes [IQR, 2.7 to 14.1 minutes], P = 0.005). Failure to achieve transseptal LA access with the assigned needle was less common using the RF versus conventional needle (0/36 [0%] versus 10/36 [27.8%], P < 0.001). Plastic shavings were grossly visible after needle advancement through the dilator and sheath in 0 (0%) RF needle cases and 12 (33.3%) conventional needle cases (P < 0.001). There were no differences in procedural complications (1/36 [2.8%] versus 1/36 [2.8%]).ConclusionsUse of an RF needle resulted in shorter time to transseptal LA access, less failure in achieving transseptal LA access, and fewer visible plastic shavings

Observations and models for needle-tissue interactions

The asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. In this study we present a mechanics-based model that calculates the deflection of the needle embedded in an elastic medium. Microscopic observations for several needle- gel interactions were used to characterize the interactions at the bevel tip and along the needle shaft. The model design was guided by microscopic observations of several needle- gel interactions. The energy-based model formulation incor- porates tissue-specific parameters such as rupture toughness, nonlinear material elasticity, and interaction stiffness, and needle geometric and material properties. Simulation results follow similar trends (deflection and radius of curvature) to those observed in macroscopic experimental studies of a robot- driven needle interacting with different kinds of gels. These results contribute to a mechanics-based model of robotic needle steering, extending previous work on kinematic models

Microscopic observations of needle and soft-tissue simulant interactions

Currently, physicians have no means of correctly estimating the needle tip location during percutaneous needle insertion. A model of needle-tissue interaction that predicts the needle tip location would assist physicians in pre-operative planning and hence improve needle targeting accuracy. This study is aimed to investigate the interactions of bevel-tipped needles and soft tissue in situ, using agarose gel as a soft-tissue simulant. An experimental setup is designed to record the needle-gel interaction forces and torques during needle insertion. Gel rupture during needle insertion is observed using a Laser Scanning Confocal Microscope and recorded in time series and three-dimensional images (Figure). Experimental results show the possibility of observing in situ gel rupture during needle insertion and relating them to the needle-gel interaction forces and torques. Moreover, it is seen that the maximum force along the insertion axis, |Fz max|, is proportional to bevel angle and inversely proportional to insertion speed. The maximum resultant torque, ||Tr max||, is found to be inversely proportional to bevel angle and proportional to insertion speed. However, the influence of the increase in insertion speed in |Fz max| and ||Tr max|| diminishes as insertion speed increases. These results concur with observations noted in gel rupture images

New constraints on data-closeness and needle map consistency for shape-from-shading

This paper makes two contributions to the problem of needle-map recovery using shape-from-shading. First, we provide a geometric update procedure which allows the image irradiance equation to be satisfied as a hard constraint. This not only improves the data closeness of the recovered needle-map, but also removes the necessity for extensive parameter tuning. Second, we exploit the improved ease of control of the new shape-from-shading process to investigate various types of needle-map consistency constraint. The first set of constraints are based on needle-map smoothness. The second avenue of investigation is to use curvature information to impose topographic constraints. Third, we explore ways in which the needle-map is recovered so as to be consistent with the image gradient field. In each case we explore a variety of robust error measures and consistency weighting schemes that can be used to impose the desired constraints on the recovered needle-map. We provide an experimental assessment of the new shape-from-shading framework on both real world images and synthetic images with known ground truth surface normals. The main conclusion drawn from our analysis is that the data-closeness constraint improves the efficiency of shape-from-shading and that both the topographic and gradient consistency constraints improve the fidelity of the recovered needle-map

Convolutional Neural Networks for Counting Fish in Fisheries Surveillance Video

We present a computer vision tool that analyses video from a CCTV system installed on fishing trawlers to monitor discarded fish catch. The system aims to support expert observers who review the footage and verify numbers, species and sizes of discarded fish. The operational environment presents a significant challenge for these tasks. Fish are processed below deck under fluorescent lights, they are randomly oriented and there are multiple occlusions. The scene is unstructured and complicated by the presence of fishermen processing the catch. We describe an approach to segmenting the scene and counting fish that exploits the $N^4$-Fields algorithm. We performed extensive tests of the algorithm on a data set comprising 443 frames from 6 belts. Results indicate the relative count error (for individual fish) ranges from 2\% to 16\%. We believe this is the first system that is able to handle footage from operational trawlers

Spatio-Temporal Deep Learning Models for Tip Force Estimation During Needle Insertion

Purpose. Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and needle deflection which in turn may lead to misplacement or injury. Hence, a number of approaches to estimate the forces at the needle have been proposed. Yet, integrating sensors into the needle tip is challenging and a careful calibration is required to obtain good force estimates. Methods. We describe a fiber-optical needle tip force sensor design using a single OCT fiber for measurement. The fiber images the deformation of an epoxy layer placed below the needle tip which results in a stream of 1D depth profiles. We study different deep learning approaches to facilitate calibration between this spatio-temporal image data and the related forces. In particular, we propose a novel convGRU-CNN architecture for simultaneous spatial and temporal data processing. Results. The needle can be adapted to different operating ranges by changing the stiffness of the epoxy layer. Likewise, calibration can be adapted by training the deep learning models. Our novel convGRU-CNN architecture results in the lowest mean absolute error of 1.59 +- 1.3 mN and a cross-correlation coefficient of 0.9997, and clearly outperforms the other methods. Ex vivo experiments in human prostate tissue demonstrate the needle's application. Conclusions. Our OCT-based fiber-optical sensor presents a viable alternative for needle tip force estimation. The results indicate that the rich spatio-temporal information included in the stream of images showing the deformation throughout the epoxy layer can be effectively used by deep learning models. Particularly, we demonstrate that the convGRU-CNN architecture performs favorably, making it a promising approach for other spatio-temporal learning problems.Comment: Accepted for publication in the International Journal of Computer Assisted Radiology and Surger