Automatic evaluation of vessel diameter variation from 2D X-ray angiography

Identification of the Position of a Tethered Delivery Catheter to Retrieve an Untethered Magnetic Robot in a Vascular Environment

In this paper, we propose a method of identifying the position of a tethered delivery catheter in a vascular environment, recombining an untethered magnetic robot (UMR) to the tethered delivery catheter, and safely retrieving them from the vascular environment in an endovascular intervention by utilizing a separable and recombinable magnetic robot (SRMR) and a magnetic navigation system (MNS). From images of a blood vessel and a tethered delivery catheter taken from two different angles, we developed a method of extracting the position of the delivery catheter in the blood vessel by introducing dimensionless cross-sectional coordinates. Then, we propose a retrieval method for the UMR by using the magnetic force considering the delivery catheter's position, suction force, and rotating magnetic field. We used thane MNS and feeding robot to simultaneously apply magnetic force and suction force to the UMR. In this process, we determined a current solution for generating magnetic force by using a linear optimization method. Finally, we conducted in vitro and in vivo experiments to verify the proposed method. In the in vitro experiment, which was in a glass tube environment, by using an RGB camera, we confirmed that the location of the delivery catheter in the glass tube could be recognized within an average error of 0.05 mm in each of the X- and Z-coordinates and that the retrieval success rate was greatly improved in comparison with that in the case without the use of magnetic force. In an in vivo experiment, we successfully retrieved the UMR in the femoral arteries of pigs.

FlowMorph: Morphological Segmentation of Ultrasound-Monitored Spinal Cord Microcirculation

Imaging of spinal cord microvasculature holds great potential in directing critical care management of spinal cord injury (SCI). Traditionally, contrast agents are preferred for imaging of the spinal cord vasculature, which is disadvantageous for long-term monitoring of injury. Here, we present FlowMorph, an algorithm that uses mathematical morphology techniques to segment non-contrast Doppler-based videos of rat spinal cord. Using the segmentation, it measures single-vessel parameters such as flow velocity, rate, and radius, with visible cardiac cycles in individual vessels showcasing the spatiotemporal resolution. The segmentation outlines vessels well with little extraneous labeling, and outlines are smooth through time. Radius measurements of perforating vessels are similar to what is seen in the literature through other methods. Verification of the algorithm through comparison to manual measurement and in vitro microphantom standards highlights points of future improvement. This method will be vital for future work studying the vascular effects of SCI and can be adopted to other species as well.

Stenosis-DetNet: Sequence consistency-based stenosis detection for X-ray coronary angiography

BACKGROUND

The automatic detection of coronary artery stenosis on X-ray images is important in coronary heart disease diagnosis. Conventional methods cannot accurately detect all stenosis areas because of heartbeat, respiratory movements and weak vascular features in single-frame contrast images.

METHOD

This paper proposes the use of Stenosis-DetNet, which is a method based on object detection networks. A sequence feature fusion module and a sequence consistency alignment module are designed to maximize temporal information to achieve accurate detection results. The sequence feature fusion module fuses all candidate box features and uses the temporal information to enhance these features. The sequence consistency alignment module optimizes the initial results by using the coronary artery displacement information and image features of the adjacent images and leads to the final detection of coronary artery stenosis.

RESULTS

In the experiment, 166 X-ray image sequences were used for training and testing. Compared with the three existing stenosis detection methods, the precision and sensitivity of Stensis-DetNet were 94.87 % and 82.22 %, respectively, which were better than those of the other three methods.

CONCLUSION

Our proposed method effectively suppressed the false positive and false negative results of stenosis detection in sequence angiography images. It was superior to the state-of-art methods.