Fast volume reconstruction from motion corrupted stacks of 2D slices

Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available

Automated organ localisation in fetal Magnetic Resonance Imaging

Fetal Magnetic Resonance Imaging (MRI) provides an invaluable diagnostic tool complementary to ultrasound due to its high resolution and tissue contrast. In order to accommodate fetal and maternal motion, MR images of the fetus are typically acquired as stacks of two-dimensional (2D) slices that freeze in-plane motion, but may form an inconsistent three-dimensional (3D) volume. Motion correction methods, which reconstruct a high-resolution 3D volume from such motion corrupted stacks of 2D slices, have revolutionised fetal MRI, enabling detailed studies of the fetal brain development. However, such motion correction and reconstruction procedures require a substantial amount of manual data preprocessing in order to isolate fetal tissues from the rest of the image. Beside the presence of motion artefacts, the main challenges when automating the processing of fetal MRI are the unpredictable position and orientation of the fetus, as well as the variability in anatomy due to fetal development. This thesis presents novel methods based on machine learning and prior knowledge of fetal development to localise automatically organs in fetal MRI in order to automate the preprocessing step of motion correction. This localisation can also be used to initialise a segmentation, or orient images based on the fetal anatomy to facilitate clinical examination. The fetal brain is first localised independently of the orientation of the fetus, and then used as an anchor point to steer features used in the subsequent localisation of the heart, lungs and liver. The localisation results are used to segment fetal tissues in each 2D slice and this segmentation can be further refined throughout the motion correction procedure. The proposed method to segment the fetal brain is shown to perform as well as a manual preprocessing. Preliminary results on a similar application to the motion correction of the fetal thorax are also presented.Open Acces

Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors

© 2014 IEEE.Automatic detection of the fetal brain in Magnetic Resonance (MR) Images is especially difficult due to arbitrary orientation of the fetus and possible movements during the scan. In this paper, we propose a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors. We calculate features for a set of 50 prenatal fast spin echo T2 volumes of the uterus and learn the appearance of the fetal brain in the feature space. We evaluate our novel classification method and show that we can localize the fetal brain with an accuracy of 100% and classify fetal brain voxels with an accuracy above 97%. Furthermore, we show how the classification process can be used for a direct segmentation of the brain by simple refinement methods within the raw MR scan data leading to a final segmentation with a Dice score above 0.90

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions