Undersampling to accelerate time-resolved MRI velocity measurement of carotid blood flow

Time-resolved velocity information of carotid blood flow can be used to estimate haemodynamic conditions associated with carotid artery disease leading to stroke. MRI provides high-resolution measurement of such information but long scan time limits its clinical application in this area. In order to reduce scan time the MRI signal is often undersampled by skipping part of the signal during data acquisition. The aim of this work is to implement and evaluate different undersampling techniques for carotid velocity measurement on a 1.5 T clinical scanner. Most recent undersampling techniques assume spatial and temporal redundancies of real time-resolved MRI signal. In these techniques different undersampling strategies were proposed. Prior information or different assumptions of the nature of true signal were used in signal reconstruction. A brief review of these techniques and details of a representative technique, known as k-t BLAST, are presented. Another undersampling scheme, termed ktVD, is proposed to use predesigned undersampling patterns with variable sampling densities in both temporal and spatial dimensions. It aims to collect enough signal content at the signal acquisition stage and simplify signal reconstruction. Fidelity of the results from undersampled data is affected by many factors, such as signal dynamic content, degree of signal redundancy, noise level, degree of undersampling, undersampling patterns, and parameters of post-processing algorithms. Simulations and in vivo scans were conducted to investigate the effects of these factors in time-resolved 2D scans and time-resolved 3D scans. The results suggested velocity measurement became less reliable when they were obtained from less than 25% of the full signal. In time-resolved 3D scans the signal can be undersampled in either one or two spatial dimensions in addition to the temporal dimension. This allows more options in the design of undersampling patterns, which were tested in vivo. In order to test undersampling in three dimensions in high resolution 3D scans and measure velocity in three dimensions, a flow phantom was also scanned at high degrees of undersampling to test the proposed method

Validation and Safety Approval of a Dual-Mode Head Coil for pTx Applications In Vivo at 7 Tesla

Following regulatory approval of single-transmit MRI at 7 tesla, there is a rapidly growing interest in clinical MRI at this field strength. However, the wider use of diagnostic MRI at 7T will require imaging in parallel-transmit (pTx) mode to reduce B1+ inhomogeneity. Previous work introduced a dual-mode head coil that operates in both transmit modes and this study investigates the use of this coil for the pTx case. It also describes the safety procedure that was followed to ensure safe operation for human scanning using the real-time SAR supervision on a commercial scanner

Integrating audio and visual modalities for multimodal personality trait recognition via hybrid deep learning

Recently, personality trait recognition, which aims to identify people’s first impression behavior data and analyze people’s psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods

First In Vivo Images from an InHouse Parallel Transmit (pTx) Coil for MRI at 7 Tesla

No abstract available

Large animal models in the study of gynecological diseases

Gynecological diseases are a series of diseases caused by abnormalities in the female reproductive organs or breast, which endanger women’s fertility and even their lives. Therefore, it is important to investigate the mechanism of occurrence and treatment of gynecological diseases. Animal models are the main objects for people to study the development of diseases and explore treatment options. Large animals, compared to small rodents, have reproductive organs with structural and physiological characteristics closer to those of humans, and are also better suited for long-term serial examinations for gynecological disease studies. This review gives examples of large animal models in gynecological diseases and provides a reference for the selection of animal models for gynecological diseases

Unveiling the Importance of Magnetic Fields in the Evolution of Dense Clumps Formed at the Waist of Bipolar H ii Regions: A Case Study of Sh 2-201 with JCMT SCUBA-2/POL-2

Abstract: We present the properties of magnetic fields (B fields) in two clumps (clump 1 and clump 2), located at the waist of the bipolar H ii region Sh 2-201, based on James Clerk Maxwell Telescope SCUBA-2/POL-2 observations of 850 μm polarized dust emission. We find that B fields in the direction of the clumps are bent and compressed, showing bow-like morphologies, which we attribute to the feedback effect of the H ii region on the surface of the clumps. Using the modified Davis–Chandrasekhar–Fermi method, we estimate B-field strengths of 266 and 65 μG for clump 1 and clump 2, respectively. From virial analyses and critical mass ratio estimates, we argue that clump 1 is gravitationally bound and could be undergoing collapse, whereas clump 2 is unbound and stable. We hypothesize that the interplay of the thermal pressure imparted by the H ii region, the B-field morphologies, and the various internal pressures of the clumps (such as magnetic, turbulent, and gas thermal pressures) has the following consequences: (a) formation of clumps at the waist of the H ii region; (b) progressive compression and enhancement of the B fields in the clumps; (c) stronger B fields that will shield the clumps from erosion by the H ii region and cause pressure equilibrium between the clumps and the H ii region, thereby allowing expanding ionization fronts to blow away from the filament ridge, forming bipolar H ii regions; and (d) stronger B fields and turbulence that will be able to stabilize the clumps. A study of a larger sample of bipolar H ii regions would help to determine whether our hypotheses are widely applicable