Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

An image-based method to synchronize cone-beam CT and optical surface tracking

open5siThe integration of in-room X-ray imaging and optical surface tracking has gained increasing importance in the field of image guided radiotherapy (IGRT). An essential step for this integration consists of temporally synchronizing the acquisition of X-ray projections and surface data. We present an image-based method for the synchronization of cone-beam computed tomography (CBCT) and optical surface systems, which does not require the use of additional hardware. The method is based on optically tracking the motion of a component of the CBCT/gantry unit, which rotates during the acquisition of the CBCT scan. A calibration procedure was implemented to relate the position of the rotating component identified by the optical system with the time elapsed since the beginning of the CBCT scan, thus obtaining the temporal correspondence between the acquisition of X-ray projections and surface data. The accuracy of the proposed synchronization method was evaluated on a motorized moving phantom, performing eight simultaneous acquisitions with an Elekta Synergy CBCT machine and the AlignRT optical device. The median time difference between the sinusoidal peaks of phantom motion signals extracted from the synchronized CBCT and AlignRT systems ranged between -3.1 and 12.9 msec, with a maximum interquartile range of 14.4 msec. The method was also applied to clinical data acquired from seven lung cancer patients, demonstrating the potential of the proposed approach in estimating the individual and daily variations in respiratory parameters and motion correlation of internal and external structures. The presented synchronization method can be particularly useful for tumor tracking applications in extracranial radiation treatments, especially in the field of patient-specific breathing models, based on the correlation between internal tumor motion and external surface surrogates.Fassi, Aurora; Schaerer, Joël; Riboldi, Marco; Sarrut, David; Baroni, GuidoFassi, Aurora; Schaerer, Joël; Riboldi, Marco; Sarrut, David; Baroni, Guid

Load-Independent And Regional Measures Of Cardiac Function Via Real-Time Mri

LOAD-INDEPENDENT AND REGIONAL MEASURES OF CARDIAC FUNCTION VIA REAL-TIME MRI Francisco Jose Contijoch Robert C Gorman, MD Expansion of infarcted tissue during left ventricular (LV) remodeling after a myocardial infarction is associated with poor long-term prognosis. Several interventions have been developed to limit infarct expansion by modifying the material properties of the infarcted or surrounding borderzone tissue. Measures of myocardial function and material properties can be obtained non-invasively via imaging. However, these measures are sensitive to variations in loading conditions and acquisition of load-independent measures have been limited by surgically invasive procedures and limited spatial resolution. In this dissertation, a real-time magnetic resonance imaging (MRI) technique was validated in clinical patients and instrumented animals, several technical improvements in MRI acquisition and reconstruction were presented for improved imaging resolution, load-independent measures were obtained in animal studies via non-invasive imaging, and regional variations in function were measured in both na�ve and post-infarction animals. Specifically, a golden-angle radial MRI acquisition with non-Cartesian SENSE-based reconstruction with an exposure time less than 95 ms and a frame rate above 89 fps allows for accurate estimation of LV slice volume in clinical patients and instrumented animals. Two technical developments were pursued to improve image quality and spatial resolution. First, the slice volume obtained can be used as a self-navigator signal to generate retrospectively-gated, high-resolution datasets of multiple beat morphologies. Second, cross-correlation of the ECG with previously observed values resulted in accurate interpretation of cardiac phase in patients with arrhythmias and allowed for multi-shot imaging of dynamic scenarios. Synchronizing the measured LV slice volume with an LV pressure signal allowed for pressure-volume loops and corresponding load-independent measures of function to be obtained in instrumented animals. Acquiring LV slice volume at multiple slice locations revealed regional differences in contractile function. Motion-tracking of the myocardium during real-time imaging allowed for differences in contractile function between normal, borderzone, and infarcted myocardium to be measured. Lastly, application of real-time imaging to patients with arrhythmias revealed the variable impact of ectopic beats on global hemodynamic function, depending on frequency and ectopic pattern. This work established the feasibility of obtaining load-independent measures of function via real-time MRI and illustrated regional variations in cardiac function

Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table