Automatic detection of the end-diastolic and end-systolic from 4D echocardiographics images

Accurate detection of the End-Diastolic (ED) and End-Systolic (ES) frames of a cardiac cycle are significant factors that may affect the accuracy of abnormality assessment of a ventricle. This process is a routine step of the ventricle assessment procedure as most of the time in clinical reports many parameters are measured in these two frames to help in diagnosing and dissection making. According to the previous works the process of detecting the ED and ES remains a challenge in that the ED and ES frames for the cavity are usually determined manually by review of individual image phases of the cavity and/or tracking the tricuspid valve. The proposed algorithm aims to automatically determine the ED and ES frames from the four Dimensional Echocardiographic images (4DE) of the Right Ventricle (RV) from one cardiac cycle. By computing the area of three slices along one cardiac cycle and selecting the maximum area as the ED frame and the minimum area as the ES frame. This method gives an accurate determination for the ED and ES frames, hence avoid the need for time consuming, expert contributions during the process of computing the cavity stroke volume

Segmenting the right ventricle cavity from 4D echocardiography images for stroke volume measurement

Quantitative measurement is an important indicator for assessment, diagnosing and decision making by the specialists. Therefore, Computer Aided (or assisted) Diagnosis systems (CAD) are increasingly affordable; it has been incorporated in routine clinical practice. In this research we address right ventricle (RV) assessment by measuring the stroke volume of the ventricle. This can be done through, segmenting RV cavity, and determining the End-Diastolic (ED) and End- Systolic (ES) stages of the cardiac cycle. Then measure the volume at ED and ES stages to compute the stroke volume. Viewing the whole RV cavity structure is also may give initial assessment for the cavity abnormality. By reviewing most of the previous work in the literature, there are different methods used to segment the right ventricle (RV) cavity, such as boundary based detection, texture, and regional segmentation methods. All of these methods focused on a manual or semi-automatic extraction of the RV structure. It is obvious that there is a lack of concentration on the multifarious structure of the RV cavity (apex, moderator band, trabecular, and Inflow-Outflow regions). The ED and ES stages for the cavity are determined manually by review of individual image phases of the cavity, and/or tracking the tricuspid valve. In the other hand, the current 3D reconstruction method of the RV structure is built by original for the left ventricle. Thus it doesn’t represent the actual structure of the RV cavity. New algorithms are needed to assess the abnormality of the right ventricle. This process can be done by accurate segmentation of the cavity, determination of the End-Diastolic and End-Systolic stages of the cardiac cycle, measuring the stroke volume, and reconstructing the three dimension model of the segmented region of the cavity for initial assessment of the abnormality. In this work, we propose a method for semi-automatic segmentation of the right ventricle to measure the stroke volume from four dimensions (4D) echocardiography, based on a novel analysing for the complex geometrical structure and function of the right ventricle. The right ventricle structure is simplified by slicing the right ventricle in 4D echocardiography images. Region growing technique is deployed to segment the cavity in each slice. This technique works automatically by detecting a seed point inside the region of interest (ROI), independently utilizing pre-knowledge of the region feature. Then start the iterative region growing segmentation process.Automatic detection for the End-Diastolic and End-Systolic stages of the cardiac cycle is introduced, by tracking the changes area of the segmented region of the cavity along one cardiac cycle. Disk summation principle is used to compute the volume of the segmented region in each slice. The resolution of the xMATREX array TEE transducer (X7-2t) is also estimated to measure the volume in millilitre unit. Then compute the cavity stroke volume by finding difference between the volume of the cavity at the ED and ES stages. The contours of the segmented region are extracted to generate the cloud of points (ℝ 3 ). Finally, generate three dimensions modelling for the segmented cavity by developing the normal feature approximation method for the cloud point, in order to accurately delineate the required object. The proposed method requires minimal user-initialization to determine the ROI and executions, which requires only few seconds for one time along the processing time. Comparisons of the segmentation, End-Diastolic and End-Systolic stages, stroke volume and the reconstructed 3D model; are provided with currently available software for left ventricle volume, function assessment and 3D modelling to validate the merit of the proposed work. The results of each step of process are satisfied high acceptance from the cardiologist experts in the qualitative validation, and a good accuracy in quantitative validation regarding to techniques

A method for determining right ventricle stroke volume

The present invention relates to a method for determining right ventricle stroke volume comprising the steps of: providing a plurality of three-dimensional images of right cardiac ventricle of a patient over a cardiac cycle; identically slicing each of the plurality of images; selecting an image from the plurality of images, for each time unit of the cardiac cycle; determining a region of interest in each slice of the selected images, wherein the region of interest shows the right ventricle of the patient; determining area of the region of interest; determining volume of the region of interest; determining total volume of the region of interest for each time unit, wherein the maximum total volume is end diastolic volume and the minimum total volume is end systolic volume; and determining the right ventricle stroke volume using an equation: Right Ventricle Stroke Volume = end diastolic volume - end systolic volume (3)

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population