We demonstrate single-breath-hold 3-D CINE imaging in volunteers and three example patient cases, which features fast reconstruction and allows reformatting to arbitrary orientations.

Purpose To develop a bipolar multi‐echo MRI method for the accurate estimation of the adipose tissue fatty acid composition (FAC) using clinically relevant protocols at clinical field strength. Methods The proposed technique jointly estimates confounding factors (field map, R2*, eddy‐current phase) and triglyceride saturation state parameters by fitting multi‐echo gradient echo acquisitions to a complex signal model. The noise propagation behavior was improved by applying a low‐rank enforcing denoising technique and by addressing eddy‐current‐induced phase discrepancies analytically. The impact of the total echo train duration on the FAC parameter map accuracy was analyzed in an oil phantom at 3T. Accuracy and reproducibility assessment was based on in vitro oil phantom measurements at two field strengths (3T and 1.5T) and with two different protocols. Repeatability was assessed in vivo in patients (n = 8) with suspected fatty liver disease using test–retest acquisitions in the abdominal subcutaneous, perirenal and mesenteric fat depots. Results Echo train readout durations of at least five times the conventional in‐phase time were required for accurate FAC estimation in areas of high fat content. In vitro, linear regression and Bland–Altman analyses demonstrated strong (r > 0.94) and significant (P ≪ 0.01) correlations between measured and reference FACs for all acquisitions, with smaller overall intercepts and biases at 3T compared to 1.5T. In vivo, reported mean absolute differences between test and retest were 1.54%, 3.31%, and 2.63% for the saturated, mono‐unsaturated, and poly‐unsaturated fat component, respectively. Conclusions Accurate and reproducible MRI‐based FAC quantification within a breath‐hold is possible at clinical field strengths.

Abstract. Coronary computed tomography angiography (CCTA) allows for non-invasive identification and grading of stenoses by evaluating the degree of narrowing of the blood-filled vessel lumen. Recently, methods have been proposed that simulate coronary blood flow using computational fluid dynamics (CFD) to compute the fractional flow reserve non-invasively. Both grading and CFD rely on a precise segmentation of the vessel lumen from CCTA. We propose a novel, model-guided segmentation approach based on a Markov random field formulation with convex priors which assures the preservation of the tubular structure of the coronary lumen. Allowing for various robust smoothness terms, the approach yields very accurate lumen segmentations even in the presence of calcified and non-calcified plaques. Evaluations on the public Rotterdam segmentation challenge demonstrate the robustness and accuracy of our method: on standardized tests with multi-vendor CCTA from 30 symptomatic patients, we achieve superior accuracies as compared to both state-of-the-art methods and medical experts.

The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the advent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruction of static environments has come into reach. In this paper, we propose a system for real-time point cloud mapping based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). In order to achieve robust mappings at real-time performance, our nearest neighbor search evaluates both geometric and photometric information in a direct manner. For acceleration of the search space traversal, we exploit the inherent computing parallelism of GPUs. In this work, we have investigated the fitness of the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, for 6-D data. In particular, we introduce a scheme that enables both fast RBC construction and queries. The proposed system is validated on an indoor scene modeling scenario. For dense data from the Microsoft Kinect sensor (640×480 px), our implementation achieved ICP runtimes of < 20 ms on an off-the-shelf consumer GPU.

Invasive cardiac angiography (catheterization) is still the standard in clinical practice for diagnosing coronary artery disease (CAD) but it involves a high amount of risk and cost. New generations of CT scanners can acquire high-quality images of coronary arteries which allow for an accurate identification and delineation of stenoses. Recently, computational flow dynamics (CFD) simulation has been applied to coronary blood flow using geometric lumen models extracted from CT angiography (CTA). The computed pressure drop at stenoses proved to be indicative for ischemia-causing lesions, leading to non-invasive Fractional Flow Reserve derived from CTA (FFRCT). Since the diagnostic value of non-invasive procedures for diagnosing CAD relies on an accurate extraction of the lumen, a precise segmentation of the coronary arteries is crucial. As manual segmentation is tedious, time-consuming and subjective, automatic procedures are desirable. We present a novel fully-automatic method to accurately segment the lumen of coronary arteries in the presence of calcified and noncalcified plaque. Our segmentation framework is based on three main steps: boundary detection, calcium exclusion and surface optimization. A learning-based boundary detector enables a robust lumen contour detection via dense ray-casting. The exclusion of calcified plaque is assured through a novel calcium exclusion technique which allows us to accurately capture stenoses of diseased arteries. The boundary detection results are incorporated into a closed set formulation whose minimization yields an optimized lumen surface. On standardized tests with clinical data, a segmentation accuracy is achieved which is comparable to clinical experts and superior to current automatic methods.

A novel reconstruction using both spatial and spectral regularization allows obtaining accurate FF and [Formula: see text] maps for prospectively highly accelerated acquisitions.

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In this chapter, we present a system for real-time point cloud mapping and scene reconstruction based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). Compared to state-ofthe-art approaches that achieve real-time performance using projective data association schemes which operate on the 3-D scene geometry solely, our method allows to incorporate additional complementary information to guide the registration process. In this work, the ICP's nearest neighbor search evaluates both geometric and photometric information in a direct manner, achieving robust mappings in real-time. In order to overcome the performance bottleneck in nearest neighbor search space traversal, we exploit the inherent computation parallelism of GPUs. In particular, we have adapted the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, to low-dimensional RGB-D data. The system is validated on scene and object reconstruction scenarios. Our implementation achieves frame-to-frame registration runtimes of less than 20 ms on an off-the-shelf consumer GPU.