Aortic valve prosthesis tracking for transapical aortic valve implantation

Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost

Introduction

In late 2019 and after the COVID-19 pandemic in the world, many researchers and scholars tried to provide methods for detecting COVID-19 cases. Accordingly, this study focused on identifying patients with COVID-19 from chest X-ray images.

Methods

In this paper, a method for diagnosing coronavirus disease from X-ray images was developed. In this method, DenseNet169 Deep Neural Network (DNN) was used to extract the features of X-ray images taken from the patients’ chests. The extracted features were then given as input to the Extreme Gradient Boosting (XGBoost) algorithm to perform the classification task.

Results

Evaluation of the proposed approach and its comparison with the methods presented in recent years revealed that this method was more accurate and faster than the existing ones and had an acceptable performance for detecting COVID-19 cases from X-ray images. The experiments showed 98.23% and 89.70% accuracy, 99.78% and 100% specificity, 92.08% and 95.20% sensitivity in two and three-class problems, respectively.

Conclusion

This study aimed to detect people with COVID-19, focusing on non-clinical approaches. The developed method could be employed as an initial detection tool to assist the radiologists in more accurate and faster diagnosing the disease.

Implication for practice

The proposed method's simple implementation, along with its acceptable accuracy, allows it to be used in COVID-19 diagnosis. Moreover, the gradient-based class activation mapping (Grad-CAM) can be used to represent the deep neural network's decision area on a heatmap. Radiologists might use this heatmap to evaluate the chest area more accurately.

Fully tuned RBF neural network controller for ultrasound hyperthermia cancer tumour therapy

Thermal dose is an important clinical efficacy index for hyperthermia cancer treatment. This paper presents a new direct radial basis function (RBF) neural network controller for high-temperature hyperthermia thermal dose during the therapeutic procedure of cancer tumours by short-time pulses of high-intensity focused ultrasound (HIFU). The developed controller is stabilized and automatically tuned based on Lyapunov functions and ant colony optimization (ACO) algorithm, respectively. In addition, this thermal dose control system has been validated using one-dimensional (1-D) biothermal tissue model. Simulation results showed that the fully tuned RBF neural network controller outperforms other controllers in the previous studies by achieving targeted thermal dose with shortest treatment times less than 13.5 min, avoiding the tissue cavitation during the thermal therapy. Moreover, the maximum value of its mean integral time absolute error (MTAE) is 98.64, which is significantly less than the resulted errors for the manual-tuned controller under the same treatment conditions of all tested cases. In this study, integrated ACO method with robust RBF neural network controller provides a successful and improved performance to deliver accurate thermal dose of hyperthermia cancer tumour treatment using the focused ultrasound transducer without external cooling effect.

Dynamic tracking of prosthetic valve motion and deformation from bi-plane x-ray views: feasibility study

Transcatheter aortic valve replacement (TAVR) requires navigation and deployment of a prosthetic valve within the aortic annulus under fluoroscopic guidance. To support improved device visualization in this procedure, this study investigates the feasibility of frame-by-frame 3D reconstruction of a moving and expanding prosthetic valve structure from simultaneous bi-plane x-ray views. In the proposed method, a dynamic 3D model of the valve is used in a 2D/3D registration framework to obtain a reconstruction of the valve. For each frame, valve model parameters describing position, orientation, expansion state, and deformation are iteratively adjusted until forward projections of the model match both bi-plane views. Simulated bi-plane imaging of a valve at different signal-difference-to-noise ratio (SDNR) levels was performed to test the approach. 20 image sequences with 50 frames of valve deployment were simulated at each SDNR. The simulation achieved a target registration error (TRE) of the estimated valve model of 0.93 ± 2.6 mm (mean ± S.D.) for the lowest SDNR of 2. For higher SDNRs (5 to 50) a TRE of 0.04 mm ± 0.23 mm was achieved. A tabletop phantom study was then conducted using a TAVR valve. The dynamic 3D model was constructed from high resolution CT scans and a simple expansion model. TRE was 1.22 ± 0.35 mm for expansion states varying from undeployed to fully deployed, and for moderate amounts of inter-frame motion. Results indicate that it is feasible to use bi-plane imaging to recover the 3D structure of deformable catheter devices.

A simple and accurate method for computer-aided transapical aortic valve replacement

BACKGROUND AND PURPOSE

Transapical aortic valve replacement (TAVR) is a recent minimally invasive surgical treatment technique for elderly and high-risk patients with severe aortic stenosis. In this paper, a simple and accurate image-based method is introduced to aid the intra-operative guidance of TAVR procedure under 2-D X-ray fluoroscopy.

METHODS

The proposed method fuses a 3-D aortic mesh model and anatomical valve landmarks with live 2-D fluoroscopic images. The 3-D aortic mesh model and landmarks are reconstructed from interventional X-ray C-arm CT system, and a target area for valve implantation is automatically estimated using these aortic mesh models. Based on template-based tracking approach, the overlay of visualized 3-D aortic mesh model, landmarks and target area of implantation is updated onto fluoroscopic images by approximating the aortic root motion from a pigtail catheter motion without contrast agent. Also, a rigid intensity-based registration algorithm is used to track continuously the aortic root motion in the presence of contrast agent. Furthermore, a sensorless tracking of the aortic valve prosthesis is provided to guide the physician to perform the appropriate placement of prosthesis into the estimated target area of implantation.

RESULTS

Retrospective experiments were carried out on fifteen patient datasets from the clinical routine of the TAVR. The maximum displacement errors were less than 2.0mm for both the dynamic overlay of aortic mesh models and image-based tracking of the prosthesis, and within the clinically accepted ranges. Moreover, high success rates of the proposed method were obtained above 91.0% for all tested patient datasets.

CONCLUSION

The results showed that the proposed method for computer-aided TAVR is potentially a helpful tool for physicians by automatically defining the accurate placement position of the prosthesis during the surgical procedure.