With a growing geriatric population estimated to triple by 2050, minimally invasive procedures that are image-guided are becoming both more popular and necessary for treating a variety of diseases. To lower the learning curve for new procedures, it is necessary to develop better guidance systems and methods to analyze procedure performance. Since fluoroscopy remains the primary mode of visualizations, the ability to perform catheter tracking from fluoroscopic images is an important part of this endeavor. This paper explores the use of deep learning to perform the landmark detection of a catheter from fluoroscopic images in 3D-printed heart models. We show that a two-stage deep-convolutional-neural-network-based model architecture can provide improved performance by initially locating a region of interest before determining the coordinates of the catheter tip within the image. This model has an average error of less than 2% of the image resolution and can be performed within 4 milliseconds, allowing for its potential use for real-time intraprocedural tracking. Coordinate regression models have the advantage of directly outputting values that can be used for quantitative tracking in future applications and are easier to create ground truth values (~50× faster), as compared to semantic segmentation models that require entire masks to be made. Therefore, we believe this work has better long-term potential to be used for a broader class of cardiac devices, catheters, and guidewires.

The increasing rate of minimally invasive procedures and the growing prevalence of cardiovascular disease have led to a demand for higher-quality guidance systems for catheter tracking. Traditional methods for catheter tracking, such as detection based on single points and applying masking techniques, have been limited in their ability to provide accurate pose information. In this paper, we propose a novel deep learning-based method for catheter tracking and pose detection. Our method uses a Yolov5 bounding box neural network with postprocessing to perform landmark detection in four regions of the catheter: the tip, radio-opaque marker, bend, and entry point. This allows us to track the catheter’s position and orientation in real time, without the need for additional masking or segmentation techniques. We evaluated our method on a dataset of fluoroscopic images from two distinct datasets and achieved state-of-the-art results in terms of accuracy and robustness. Our model was able to detect all four landmark features (tip, marker, bend, and entry) used to generate a pose for a catheter with 0.285 ± 0.143 mm, 0.261 ± 0.138 mm, 0.424 ± 0.361 mm, and 0.235 ± 0.085 mm accuracy. We believe that our method has the potential to significantly improve the accuracy and efficiency of catheter tracking in medical procedures that utilize bi-plane fluoroscopy guidance.

Background: Effective preoperative planning is essential for reducing pain, fatigue, and joint damage following total hip arthroplasty (THA). This study investigates the effects of key geometric parameters – neck-shaft angle (NSA), anteversion angle (AVA), and hip offset (HO) – on muscle mechanics and joint reaction forces during normal walking gait using musculoskeletal simulation. Methods: A MATLAB-based HO tool combined with a femoral torsion tool was used to generate 45 OpenSim models with systematic variations in NSA, AVA, and HO. All models were evaluated using identical gait kinematics and ground reaction forces to isolate the biomechanical influence of geometric changes. Results: Increases in femoral neck length and HO substantially affect muscle lever arms and joint reaction forces, with peak hip and knee joint loading and associated muscle mechanics increasing by up to approximately 2.5-fold compared to the reference model. Changes in NSA, AVA, and HO significantly altered muscle moment arms, muscle activations, and muscle forces, particularly in hip abductors and flexors. Simultaneous variation of all 3 parameters produced markedly larger effects than isolated changes. Conclusions: This study highlights the importance of considering prosthesis geometry and implantation parameters during THA planning and biomechanical modelling to optimise gait mechanics and reduce postoperative joint loading.