MR image reconstruction using deep density priors

Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this article, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically Variational Autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multi-coil complex images acquired from healthy volunteers (N=8) and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions. Keywords: Reconstruction, MRI, prior probability, machine learning, deep learning, unsupervised learning, density estimationComment: Published in IEEE TMI. Main text and supplementary material, 19 pages tota

Quantum Spin Lenses in Atomic Arrays

We propose and discuss `quantum spin lenses', where quantum states of delocalized spin excitations in an atomic medium are `focused' in space in a coherent quantum process down to (essentially) single atoms. These can be employed to create controlled interactions in a quantum light-matter interface, where photonic qubits stored in an atomic ensemble are mapped to a quantum register represented by single atoms. We propose Hamiltonians for quantum spin lenses as inhomogeneous spin models on lattices, which can be realized with Rydberg atoms in 1D, 2D and 3D, and with strings of trapped ions. We discuss both linear and non-linear quantum spin lenses: in a non-linear lens, repulsive spin-spin interactions lead to focusing dynamics conditional to the number of spin excitations. This allows the mapping of quantum superpositions of delocalized spin excitations to superpositions of spatial spin patterns, which can be addressed by light fields and manipulated. Finally, we propose multifocal quantum spin lenses as a way to generate and distribute entanglement between distant atoms in an atomic lattice array.Comment: 13 pages, 9 figure

Computer‐assisted bone augmentation, implant planning and placement: An in vitro investigation

Aim To assess in vitro the workflow for alveolar ridge augmentation with customised 3D printed block grafts and simultaneous computer-assisted implant planning and placement. Methods Twenty resin mandible models with an edentulous area and horizontal ridge defect in the region 34–36 were scanned with cone beam computed tomography (CBCT). A block graft for horizontal ridge augmentation in the region 34–36 and an implant in the position 35 were digitally planned. Twenty block grafts were 3D printed out of resin and one template for guided implant placement were stereolithographically produced. The resin block grafts were positioned onto the ridge defects and stabilised with two fixation screws each. Subsequently, one implant was inserted in the position 35 through the corresponding template for guided implant placement. Optical scans of the study models together with the fixated block graft were performed prior to and after implant placement. The scans taken after block grafting were superimposed with the virtual block grafting plan through a best-fit algorithm, and the linear deviation between the planned and the achieved block positions was calculated. The precision of the block fixation was obtained by superimposing the 20 scans taken after grafting and calculating the deviation between the corresponding resin blocks. The superimposition between the scans taken after and prior to implant placement was performed to measure a possible displacement in the block position induced by guided implant placement. The (98–2%)/2 percentile value was determined as a parameter for surface deviation. Results The mean deviation in the position of the block graft compared to the virtual plan amounted to 0.79 ± 0.13 mm. The mean deviation between the positions of the 20 block grafts measured 0.47 ± 0.2 mm, indicating a clinically acceptable precision. Guided implant placement induced a mean shift of 0.16 ± 0.06 mm in the position of the block graft. Conclusions Within the limitations of this in vitro study, it can be concluded that customised block grafts fabricated through CBCT, computer-assisted design and 3D printing allow alveolar ridge augmentation with clinically acceptable predictability and reproducibility. Computer-assisted implant planning and placement can be performed simultaneously with computer-assisted block grafting leading to clinically non-relevant dislocation of block grafts

NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology

Understanding how neural networks generate activity patterns and communicate with each other requires monitoring the electrical activity from many neurons simultaneously. Perfectly suited tools for addressing this challenge are genetically encoded voltage indicators (GEVIs) because they can be targeted to specific cell types and optically report the electrical activity of individual, or populations of neurons. However, analyzing and interpreting the data from voltage imaging experiments is challenging because high recording speeds and properties of current GEVIs yield only low signal-to-noise ratios, making it necessary to apply specific analytical tools. Here, we present NOSA (Neuro-Optical Signal Analysis), a novel open source software designed for analyzing voltage imaging data and identifying temporal interactions between electrical activity patterns of different origin. In this work, we explain the challenges that arise during voltage imaging experiments and provide hands-on analytical solutions. We demonstrate how NOSA’s baseline fitting, filtering algorithms and movement correction can compensate for shifts in baseline fluorescence and extract electrical patterns from low signal-to-noise recordings. NOSA allows to efficiently identify oscillatory frequencies in electrical patterns, quantify neuronal response parameters and moreover provides an option for analyzing simultaneously recorded optical and electrical data derived from patch-clamp or other electrode-based recordings. To identify temporal relations between electrical activity patterns we implemented different options to perform cross correlation analysis, demonstrating their utility during voltage imaging in Drosophila and mice. All features combined, NOSA will facilitate the first steps into using GEVIs and help to realize their full potential for revealing cell-type specific connectivity and functional interactions

Influence of the use of Renewable Compatibility Agent Wood Plastic Composite (WPC)

The growing interest in using recycled and natural materials in the application of new composites in recent years implies ecological, economic and versatility benefits. Wood plastic composite (WPC) are considered very attractive materials, as they allow the use of polymers of recycled or virgin origin, associated with forestry by-products. The present work aims to investigate the influence on the mechanical, thermal and morphological resistance of WPC, using oleic acid and glycerol as renewable coupling agents. Composites were also prepared with a commercial compatibility agent in its formulation - maleic anhydride grafted polypropylene (MAPP) - under the same conditions. The composites were prepared in a single-screw extruder, with fixed contents of 5% sawdust with 95% virgin polymer, of this total, 2% were coupling agents: MAPP, oleic acid or glycerol, according to the desired composition. To be evaluated as changes in mechanical properties, tensile and impact strength tests were performed on specimens obtained through the injection molding process. The fracture surfaces of specimens tested in tensile tests were examined using images generated by scanning electron microscopy. The thermal stability of the composites was also investigated by thermogravimetric analysis. The use of glycerol and oleic acid improved the mechanical properties of the composite. An increase in tensile strength is observed when glycerol is added in composite. As for impact strength, the addition of glycerol or oleic acid was around 58% higher in impact strength when compared to without coupling agent. Glycerol and oleic acid are renewable, low-cost alternative to be a potential substitute for the commercial coupling agent MAPP, especially when the main requirement is to obtain better impact resistance properties

Detection of Seagrass Scars Using Sparse Coding and Morphological Filter

We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final scar detection map. We applied the algorithm to a panchromatic image captured at the Deckle Beach, Florida using the WorldView2 orbiting satellite. Our results show that the proposed method can achieve \u3e90% accuracy on the detection of seagrass scars

Reducing cybersickness in 360-degree virtual reality

Despite the technological advancements in Virtual Reality (VR), users are constantly combating feelings of nausea and disorientation, the so-called cybersickness. Cybersickness symptoms cause severe discomfort and hinder the immersive VR experience. Here we investigated cybersickness in 360-degree head-mounted display VR. In traditional 360-degree VR experiences, translational movement in the real world is not reflected in the virtual world, and therefore self-motion information is not corroborated by matching visual and vestibular cues, which may trigger symptoms of cybersickness. We evaluated whether a new Artificial Intelligence (AI) software designed to supplement the 360-degree VR experience with artificial six-degrees-of-freedom motion may reduce cybersickness. Explicit (simulator sickness questionnaire and Fast Motion Sickness (FMS) rating) and implicit (heart rate) measurements were used to evaluate cybersickness symptoms during and after 360-degree VR exposure. Simulator sickness scores showed a significant reduction in feelings of nausea during the AI-supplemented six-degrees-of-freedom motion VR compared to traditional 360-degree VR. However, six-degrees-of-freedom motion VR did not reduce oculomotor or disorientation measures of sickness. No changes were observed in FMS and heart rate measures. Improving the congruency between visual and vestibular cues in 360-degree VR, as provided by the AI-supplemented six-degrees-of-freedom motion system considered, is essential for a more engaging, immersive and safe VR experience, which is critical for educational, cultural and entertainment applications