Automating the detection and localization of segmental (regional) left ventricle (LV) abnormalities in magnetic resonance imaging (MRI) has recently sparked an impressive research effort, with promising performances and a breadth of techniques. However, despite such an effort, the problem is still acknowledged to be challenging, with much room for improvements in regard to accuracy. Furthermore, most of the existing techniques are labor intensive, requiring delineations of the endo- and/or epi-cardial boundaries in all frames of a cardiac sequence. The purpose of this study is to investigate a real-time machine-learning approach which uses some image features that can be easily computed, but that nevertheless correlate well with the segmental cardiac function. Starting from a minimum user input in only one frame in a subject dataset, we build for all the regional segments and all subsequent frames a set of statistical MRI features based on a measure of similarity between distributions. We demonstrate that, over a cardiac cycle, the statistical features are related to the proportion of blood within each segment. Therefore, they can characterize segmental contraction without the need for delineating the LV boundaries in all the frames. We first seek the optimal direction along which the proposed image features are most descriptive via a linear discriminant analysis. Then, using the results as inputs to a linear support vector machine classifier, we obtain an abnormality assessment of each of the standard cardiac segments in real-time. We report a comprehensive experimental evaluation of the proposed algorithm over 928 cardiac segments obtained from 58 subjects. Compared against ground-truth evaluations by experienced radiologists, the proposed algorithm performed competitively, with an overall classification accuracy of 86.09% and a kappa measure of 0.73.

Abstract. The cardiac ejection fraction (EF) depends on the volume variation of the left ventricle (LV) cavity during a cardiac cycle, and is an essential measure in the diagnosis of cardiovascular diseases. It is often estimated via manual segmentation of several images in a cardiac sequence, which is prohibitively time consuming, or via automatic segmentation, which is a challenging and computationally expensive task that may result in high estimation errors. In this study, we propose to estimate the EF in real-time directly from image statistics using machine learning technique. From a simple user input in only one image, we build for all the images in a subject dataset (200 images) a statistic based on the Bhattacharyya coefficient of similarity between image distributions. We demonstrate that these statistics are non-linearly related to the LV cavity areas and, therefore, can be used to estimate the EF via an Artificial Neural Network (ANN) directly. A comprehensive evaluation over 20 subjects demonstrated that the estimated EFs correlate very well with those obtained from independent manual segmentations.

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Purpose Secondary usage of patient data has recently become of increasing interest for the development and application of computer analytic techniques. Strict oversight of these data is required and the individual patients themselves are integral to providing guidance. We sought to understand patients' attitudes to sharing their imaging data for research purposes. These images could provide a great wealth of information for researchers. Methods Patients from the Greater Toronto Area attending Sunnybrook Health Sciences Centre for imaging (magnetic resonance imagining, computed tomography, or ultrasound) examination areas were invited to participate in an electronic survey. Results Of the 1083 patients who were approached (computed tomography 609, ultrasound 314, and magnetic resonance imaging 160), 798 (74%) agreed to take the survey. Overall median age was 60 (interquartile range = 18, Q1 = 52, Q3 = 70), 52% were women, 42% had a university degree, and 7% had no high school diploma. In terms of willingness to share de-identified medical images for research, 76% were willing (agreed and strongly agreed), while 7% refused. Most participants gave their family physicians (73%) and other physicians (57%) unconditional data access. Participants chose hospitals/research institutions to regulate electronic images databases (70%), 89% wanted safeguards against unauthorized access to their data, and over 70% wanted control over who will be permitted, for how long, and the ability to revoke that permission. Conclusions Our study found that people are willing to share their clinically acquired de-identified medical images for research studies provided that they have control over permissions and duration of access.