The non-invasive estimation for LV pressure-strain loop area and the global myocardial work indices obtained from LV-PSL strongly correlates with invasive measurements.

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Patients with higher CWtot exhibit a favourable response to CRT. These data encourage further studies for the assessment of the myocardial substrate related to the functional response to CRT.

This paper describes a novel image-based method for tracking robotic mechanisms and interventional devices during Magnetic Resonance Image (MRI)-guided procedures. It takes advantage of the multi-planar imaging capabilities of MRI to optimally image a set of localizing fiducials for passive motion tracking in the image coordinate frame. The imaging system is servoed to adaptively position the scan plane based on automatic detection and localization of fiducial artifacts directly from the acquired image stream. This closed-loop control system has been implemented using an open-source software framework and currently operates with GE MRI scanners. Accuracy and performance were evaluated in experiments, the results of which are presented here. 1 This publication was made possible by NIH grants R01-CA111288 and U41-RR019703. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Abstract. Registration uncertainty may be important information to convey to a surgeon when surgical decisions are taken based on registered image data. However, conventional non-rigid registration methods only provide the most likely deformation. In this paper we show how to determine the registration uncertainty, as well as the most likely deformation, by using an elastic Bayesian registration framework that generates a dense posterior distribution on deformations. We model both the likelihood and the elastic prior on deformations with Boltzmann distributions and characterize the posterior with a Markov Chain Monte Carlo algorithm. We introduce methods that summarize the high-dimensional uncertainty information and show how these summaries can be visualized in a meaningful way. Based on a clinical neurosurgical dataset, we demonstrate the importance that uncertainty information could have on neurosurgical decision making.

Although iMRI is an effective way of imaging residual tumor, this study could not demonstrate an increased efficacy of surgery utilizing this technique for patients harbouring grade IV gliomas compared to more conventional methods. No statistical significance was noted between the two groups (p = 0.14). The complication rate was within the range reported for other series, in both control as well as the study group.

3D Transesophageal Echocardiography is an excellent tool for evaluating the mitral valve and is also well suited for guiding cardiac interventions. We introduce a fully automatic method for mitral annulus segmentation in 3D Transesophageal Echocardiography, which requires no manual input. One hundred eleven multi-frame 3D transesophageal echocardiography recordings were split into training, validation, and test sets. Each 3D recording was decomposed into a set of 2D planes, exploiting the symmetry around the centerline of the left ventricle. A deep 2D convolutional neural network was trained to predict the mitral annulus coordinates, and the predictions from neighboring planes were regularized by enforcing continuity around the annulus. Applying the final model and postprocessing to the test set data gave a mean error of 2.0 mm-with a standard deviation of 1.9 mm. Fully automatic segmentation of the mitral annulus can alleviate the need for manual interaction in the quantification of an array of mitral annular parameters and has the potential to eliminate inter-observer variability.

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