Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices
Bernhard Kainz,Markus Steinberger,Wolfgang Wein,Maria Kuklisova-Murgasova,Christina Malamateniou,Kevin Keraudren,Thomas Torsney-Weir,Mary A. Rutherford,Paul Aljabar,Joseph V. Hajnal,Daniel Rueckert +10 more
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TLDR
A novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target and ensures 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.Abstract:
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.read more
Citations
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Journal ArticleDOI
A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth
Ali Gholipour,Caitlin K. Rollins,Clemente Velasco-Annis,Abdelhakim Ouaalam,Alireza Akhondi-Asl,Onur Afacan,Cynthia M. Ortinau,Sean Clancy,Catherine Limperopoulos,Edward Yang,Judy A. Estroff,Simon K. Warfield +11 more
TL;DR: An algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain is developed by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age and is available online as a reference for anatomy and for registration and segmentation.
Proceedings ArticleDOI
Brain MRI super-resolution using deep 3D convolutional networks
TL;DR: A three-dimensional convolutional neural network is proposed to generate high-resolution brain image from its input low-resolution (LR) with the help of patches of other HR brain images to demonstrate the need of fitting data and network parameters for 3D brain MRI.
Journal ArticleDOI
Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging
TL;DR: This paper presents a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes, and evaluates the performance of the algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets.
Journal ArticleDOI
An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI.
Michael Ebner,Michael Ebner,Guotai Wang,Guotai Wang,Guotai Wang,Wenqi Li,Wenqi Li,Michael Aertsen,Premal A. Patel,Premal A. Patel,Rosalind Aughwane,Andrew Melbourne,Andrew Melbourne,Tom Doel,Steven Dymarkowski,Paolo De Coppi,Anna L. David,Anna L. David,Jan Deprest,Jan Deprest,Sebastien Ourselin,Tom Vercauteren,Tom Vercauteren,Tom Vercauteren +23 more
TL;DR: A fully automatic framework for fetal brain reconstruction that consists of four stages that outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments is proposed.
Journal ArticleDOI
Slice-to-volume medical image registration: A survey
TL;DR: This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category.
References
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