1,537 research outputs found

    Three-Dimensional Variable Slab-Selective Projection Acquisition Imaging

    Full text link
    Three-dimensional (3D) projection acquisition (PA) imaging has recently gained attention because of its advantages, such as achievability of very short echo time, less sensitivity to motion, and undersampled acquisition of projections without sacrificing spatial resolution. However, larger subjects require a stronger Nyquist criterion and are more likely to be affected by outer-volume signals outside the field of view (FOV), which significantly degrades the image quality. Here, we proposed a variable slab-selective projection acquisition (VSS-PA) method to mitigate the Nyquist criterion and effectively suppress aliasing streak artifacts in 3D PA imaging. The proposed method involves maintaining the vertical orientation of the slab-selective gradient for frequency-selective spin excitation and the readout gradient for data acquisition. As VSS-PA can selectively excite spins only in the width of the desired FOV in the projection direction during data acquisition, the effective size of the scanned object that determines the Nyquist criterion can be reduced. Additionally, unwanted signals originating from outside the FOV (e.g., aliasing streak artifacts) can be effectively avoided. The mitigation of the Nyquist criterion owing to VSS-PA was theoretically described and confirmed through numerical simulations and phantom and human lung experiments. These experiments further showed that the aliasing streak artifacts were nearly suppressed.Comment: 9 Pages, 6 figures, 28 referenc

    k-Space Deep Learning for Reference-free EPI Ghost Correction

    Full text link
    Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the non-linear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k-space interpolation problem that can be solved using structured low-rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k-space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non-accelerated EPI acquisitions. To take advantage of the even and odd-phase directional redundancy, the k-space data is divided into two channels configured with even and odd phase encodings. The redundancies between coils are also exploited by stacking the multi-coil k-space data into additional input channels. Then, our k-space ghost correction network is trained to learn the interpolation kernel to estimate the missing virtual k-space data. For the accelerated EPI data, the same neural network is trained to directly estimate the interpolation kernels for missing k-space data from both ghost and subsampling. Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster.The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.Comment: To appear in Magnetic Resonance in Medicin

    Temporal Interpolation Is All You Need for Dynamic Neural Radiance Fields

    Full text link
    Temporal interpolation often plays a crucial role to learn meaningful representations in dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural radiance fields of dynamic scenes based on temporal interpolation of feature vectors. Two feature interpolation methods are suggested depending on underlying representations, neural networks or grids. In the neural representation, we extract features from space-time inputs via multiple neural network modules and interpolate them based on time frames. The proposed multi-level feature interpolation network effectively captures features of both short-term and long-term time ranges. In the grid representation, space-time features are learned via four-dimensional hash grids, which remarkably reduces training time. The grid representation shows more than 100 times faster training speed than the previous neural-net-based methods while maintaining the rendering quality. Concatenating static and dynamic features and adding a simple smoothness term further improve the performance of our proposed models. Despite the simplicity of the model architectures, our method achieved state-of-the-art performance both in rendering quality for the neural representation and in training speed for the grid representation.Comment: CVPR 2023. Project page: https://sungheonpark.github.io/tempinterpner

    A Statistical Verification Method of Random Permutations for Hiding Countermeasure Against Side-Channel Attacks

    Full text link
    As NIST is putting the final touches on the standardization of PQC (Post Quantum Cryptography) public key algorithms, it is a racing certainty that peskier cryptographic attacks undeterred by those new PQC algorithms will surface. Such a trend in turn will prompt more follow-up studies of attacks and countermeasures. As things stand, from the attackers' perspective, one viable form of attack that can be implemented thereupon is the so-called "side-channel attack". Two best-known countermeasures heralded to be durable against side-channel attacks are: "masking" and "hiding". In that dichotomous picture, of particular note are successful single-trace attacks on some of the NIST's PQC then-candidates, which worked to the detriment of the former: "masking". In this paper, we cast an eye over the latter: "hiding". Hiding proves to be durable against both side-channel attacks and another equally robust type of attacks called "fault injection attacks", and hence is deemed an auspicious countermeasure to be implemented. Mathematically, the hiding method is fundamentally based on random permutations. There has been a cornucopia of studies on generating random permutations. However, those are not tied to implementation of the hiding method. In this paper, we propose a reliable and efficient verification of permutation implementation, through employing Fisher-Yates' shuffling method. We introduce the concept of an n-th order permutation and explain how it can be used to verify that our implementation is more efficient than its previous-gen counterparts for hiding countermeasures.Comment: 29 pages, 6 figure

    Neural correlates of tactile hardness intensity perception during active grasping

    Get PDF
    While tactile sensation plays an essential role in interactions with the surroundings, relatively little is known about the neural processes involved in the perception of tactile information. In particular, it remains unclear how different intensities of tactile hardness are represented in the human brain during object manipulation. This study aims to investigate neural responses to various levels of tactile hardness using functional magnetic resonance imaging while people grasp objects to perceive hardness intensity. We used four items with different hardness levels but otherwise identical in shape and texture. A total of Twenty-five healthy volunteers participated in this study. Before scanning, participants performed a behavioral task in which they received a pair of stimuli and they were to report the perceived difference of hardness between them. During scanning, without any visual information, they were randomly given one of the four objects and asked to grasp it. We found significant blood oxygen-level-dependent (BOLD) responses in the posterior insula in the right hemisphere (rpIns) and the right posterior lobe of the cerebellum (rpCerebellum), which parametrically tracked hardness intensity. These responses were supported by BOLD signal changes in the rpCerebellum and rpIns correlating with tactile hardness intensity. Multidimensional scaling analysis showed similar representations of hardness intensity among physical, perceptual, and neural information. Our findings demonstrate the engagement of the rpCerebellum and rpIns in perceiving tactile hardness intensity during active object manipulation

    Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

    Get PDF
    Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra-and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness

    Diagnostic Value of Galectin-3, HBME-1, Cytokeratin 19, High Molecular Weight Cytokeratin, Cyclin D1 and p27kip1 in the Differential Diagnosis of Thyroid Nodules

    Get PDF
    The distinction between benign and malignant thyroid tumors is critical for the management of patients with thyroid nodules. We applied immunohistochemical staining for galectin-3, HBME-1, cytokeratin 19 (CK19), high molecular weight cytokeratin (HMWCK), cyclin D1 and p27kip1 in 295 thyroid lesions to determine their diagnostic accuracy. The expression of all markers was significantly associated with differentiated thyroid carcinoma (DTC).The sensitivity for the diagnosis of DTC was 94.7% with galectin-3, 91.3% with HBME-1, and 90.3% with CK19. The specificities of these markers were 95.5%, 69.7%, and 83.1%, respectively. Combining these markers, co-expression of galectin-3 and CK19 or galectin-3 and HBME-1 was seen in 93.2% of carcinomas but in none of the benign nodules. Comparing follicular variant of papillary carcinoma (FVPC) with follicular carcinoma (FC), the expression of galectin-3, CK19, and HMWCK was significantly higher in FVPC. When comparing FC with FA, the expression of galectin-3 and HBME-1 was significantly higher in FC. These results suggest that 1) galectin-3 is a useful marker in the distinction between benign and malignant thyroid tumors, 2) the combined use of HBME-1 and CK19 can increase the diagnostic accuracy, and 3) the use of CK19 and HMWCK can aid in the differential diagnosis between PC and FC
    corecore