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
Highlights
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
H IGH resolution 3D volumetric images are routinely used for clinical examinations but are vulnerable to artefacts caused by subject movement during acquisition, which may take several minutes for modalities such as Magnetic Resonance Imaging (MRI)
Section IV.E briefly discusses the final step of slice-to-volume registration, which is methodologically similar to all SVR approaches
Summary
H IGH resolution 3D volumetric images are routinely used for clinical examinations but are vulnerable to artefacts caused by subject movement during acquisition, which may take several minutes for modalities such as Magnetic Resonance Imaging (MRI). Approaches for real-time compounding are starting to find application in MRI, allowing snapshot images of single slices which can be acquired fast enough to ‘freeze’ subject movement, (i.e., where the effects of motion are negligible in any individual slice). Such images may be realigned and combined to provide motion corrected volumetric data. The resulting volume is usually corrupted by inconsistent probe pressure and natural patient movements [10] This necessitates motion modelling as well as image reconstruction techniques in order to obtain volumetrically consistent image data. Image-based motion correction, especially without contextual information from other modalities remains a challenging problem [12] and is not performed during examination due to the high computational demands
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