High Acceleration Research Articles

Dispersive vacuum as a decoherence amplifier of an Unruh–DeWitt detector

Abstract Recently, interest has been growing in studies on discrete or "pixelated'' space-time that, through modifications in the dispersion relation, can treat the vacuum as a dispersive medium. Discrete spacetime considers that spacetime has a cellular structure on the order of the Planck length, and if this is true we should certainly have observable effects. In this paper, we investigated the effects caused by the dispersive vacuum on the decoherence process of an Unruh-DeWitt detector, our setup consists of a uniformly accelerated detector, initially in a qubit state, which interacts with a massless scalar field during a time interval finite. We use dispersion relations drawn from doubly special relativity and Hořava-Lifshitz gravity, with these modifications the vacuum becomes dispersive and has a corresponding refractive index. We calculate the probability transition rates, the probability of finding the detector in the ground state, and the quantum coherence variation. Our results indicate that the decoherence process occurs more quickly in cases with changes in the dispersion relation in the regime of high accelerations and interaction time. Additionally, the decoherence increases as the vacuum becomes more dispersive due to the increase in the order of modification in the dispersion relation, and this happens because the dispersive vacuum amplifies the effects of quantum fluctuations that are captured by the detector when interacting with the field.

A concurrent learning approach to monocular vision range regulation of leader/follower systems

This paper explores range and bearing angle regulation of a leader–follower using monocular vision. The main challenge is that monocular vision does not directly provide a range measurement. The contribution is a novel concurrent learning (CL) approach, called CL Subtended Angle and Bearing Estimator for Relative pose (CL-SABER), which achieves range regulation without communication, persistency of excitation or known geometry and is demonstrated on a physical, robot platform. A history stack estimates target size which augments the Kalman filter (KF) with a range pseudomeasurement. The target is followed to scale without drift, persistency of excitation requirements, prior knowledge, or additional measurements. Finite excitation is required to achieve parameter convergence and perform steady-state regulation using CL-SABER. Evaluation using simulation and mobile robot experiments in special Euclidean planar space (SE(2)) show that the new method provides stable and consistent range regulation, as demonstrated by the inter-rater reliability, including in noisy and high leader acceleration environments.

ISRSL0: compressed sensing MRI with image smoothness regularized-smoothed \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\ell}_{0}$$\\end{document} norm

The reconstruction of MR images has always been a challenging inverse problem in medical imaging. Acceleration of MR scanning is of great importance for clinical research and cutting-edge applications. One of the primary efforts to achieve this is using compressed sensing (CS) theory. The CS aims to reconstruct MR images using a small number of sampled data in k-space. The CS-MRI techniques face challenges, including the potential loss of fine structure and increased computational complexity. We introduce a novel framework based on a regularized sparse recovery problem and a sharpening step to improve the CS-MRI approaches regarding fine structure loss under high acceleration factors. This problem is solved via the Half Quadratic Splitting (HQS) approach. The inverse problem for reconstructing MR images is converted into two distinct sub-problems, each of which can be solved separately. One key feature of the proposed approach is the replacement of one sub-problem with a denoiser. This regularization assists the optimization of the Smoothed \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\ell}_{0}$$\\end{document} (SL0) norm in escaping local minimums and enhances its precision. The proposed method consists of smoothing, feature modification, and Smoothed \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\ell}_{0}$$\\end{document} cost function optimization. The proposed approach improves the SL0 algorithm for MRI reconstruction without complicating it. The convergence of the proposed approach is illustrated analytically. The experimental results show an acceptable performance of the proposed method compared to the network-based approaches.

A dynamic approach for MR T2-weighted pelvic imaging

T2-weighted 2D fast spin echo sequence serves as the standard sequence in
clinical pelvic MR imaging protocols. However, motion artifacts and blurring caused
by peristalsis present significant challenges. Patient preparation such as administering
antiperistaltic agents is often required before examination to reduce artifacts, which
discomfort the patients. This work introduce a novel dynamic approach for T2
weighted pelvic imaging to address peristalsis-induced motion issue without any patient
preparation.
Approach: A rapid dynamic data acquisition strategy with complementary sampling
trajectory is designed to enable highly undersampled motion-resistant data sampling,
and an unrolling method based on deep equilibrium model is leveraged to reconstruct
images from the dynamic sampled k-space data. Moreover, the fix-point convergence of
the equilibrium model ensures the stability of the reconstruction. The high acceleration
factor in each temporal phase, which is much higher than that in traditional static
imaging, has the potential to effectively freeze pelvic motion, thereby transforming
the imaging problem from conventional motion prevention or removal to motion
reconstruction.
Main results: Experiments on both retrospective and prospective data have
demonstrated the superior performance of the proposed dynamic approach in reducing
motion artifacts and accurately depicting structural details compared to standard static
imaging.
Significance: The proposed dynamic approach effectively captures motion states
through dynamic data acquisition and deep learning-based reconstruction, addressing
motion-related challenges in pelvic imaging.

Design and analysis of mechanical structure for on-board HTS magnets subjected to high acceleration

Design and analysis of mechanical structure for on-board HTS magnets subjected to high acceleration

Accelerating multi-coil MR image reconstruction using weak supervision.

Deep-learning-based MR image reconstruction in settings where large fully sampled dataset collection is infeasible requires methods that effectively use both under-sampled and fully sampled datasets. This paper evaluates a weakly supervised, multi-coil, physics-guided approach to MR image reconstruction, leveraging both dataset types, to improve both the quality and robustness of reconstruction. A physics-guided end-to-end variational network (VarNet) is pretrained in a self-supervised manner using a 4 under-sampled dataset following the self-supervised learning via data undersampling (SSDU) methodology. The pre-trained weights are transferred to another VarNet, which is fine-tuned using a smaller, fully sampled dataset by optimizing multi-scale structural similarity (MS-SSIM) loss in image space. The proposed methodology is compared with fully self-supervised and fully supervised training. Reconstruction quality improvements in SSIM, PSNR, and NRMSE when abundant training data is available (the high-data regime), and enhanced robustness when training data is scarce (the low-data regime) are demonstrated using weak supervision for knee and brain MR image reconstructions at 8 and 10 acceleration, respectively. Multi-coil physics-guided MR image reconstruction using both under-sampled and fully sampled datasets is achievable with transfer learning and fine-tuning. This methodology can provide improved reconstruction quality in the high-data regime and improved robustness in the low-data regime at high acceleration rates.

Embodiment of emotions in adolescents’ musical expression

Music has been actively studied from the perspectives of emotional expression and body movement, but not during adolescence. The current study addressed music as a forum for adolescent embodied emotion expression. Based on prior research, we hypothesised that adolescents would be able to differentiate between emotions in their music-related expressive body movements based on valence and arousal characteristics. Participants ( N = 60, 17 male, mean age 14.72 years) played djembe to express five basic emotions (happiness, tenderness, sadness, anger, fear) while body movements were motion captured. Correlations of movement features with emotion-regulation tendencies, as measured by the Emotion Regulation Questionnaire (ERQ), were additionally explored. Adolescents demonstrated great capacity to use all measured movement features to express emotions: movement speed, variance, area and length all differed significantly between emotions. In particular, the results confirm the hypothesised connection of high arousal to high speed and acceleration and further suggest that positive valence relates to wider area and longer performance. In addition, adolescents scoring high on cognitive reappraisal gave faster and more stable performances. We discuss creative body movement as part of youth emotional development.

An Investigation of Classical Model Predictive Controller Path Tracking Performance of a Two-Wheel and Four-Wheel Steering Vehicle

Studies on self-driving vehicles have become a trend in recent years, and many systems have been developed to enable autonomous manoeuvre. Various methods have been used to improve path-tracking algorithms which increase vehicle performance, including tracking accuracy and stability. Path tracking is one of the primary problems for autonomous vehicles where the vehicle deviates from target paths, which leads to unnecessary counter-correction. Conventional front wheel steering is unable to satisfy the manoeuvre with a high lateral acceleration since the front steering angle is limited in accurately responding to vehicle dynamics. Moreover, the characteristics of front wheel steering vehicles affect handling stability due to the fact that the turning radius is larger than the vehicle itself. This disadvantageous can compromise safety during under-steer and over-steer situations. The main objective of this preliminary study is to investigate the performance of a four-wheel steering system (4WS) in path tracking for autonomous vehicles using a classical model predictive controller (MPC). Conventional two-wheel steering (2WS) tracking performance following the desired driving system with the same MPC controller is compared with 4WS vehicles. The driving system is developed using Driving Scenario Designer to extract the desired yaw angle and lateral position for controller references constructed in Matlab Simulink. Fixed MPC constraint, prediction, control horizon, yaw angle and lateral position weights were used to compare the performance between 2WS and 4WS vehicles. The simulation results show that 4WS is three times better than 2WS vehicles in tracking predetermined paths. 4WS vehicle show 74.55% better performance in lateral position tracking and 68.75% better performance in trailing predetermined yaw angle value. The simulation data from the preliminary study will be used as a guideline to develop an advanced controller of 4WS vehicles.

Noise monitoring of loud vehicles in four Dutch cities

Excessive noise from loud cars, motorcycles, mopeds, trikes and quads is commonplace in Dutch cities and is a major source of annoyance. In order to assess the sources, causes and potential solutions, TNO has performed a series of noise monitoring campaigns in four major cities including vehicle identification. Sound and vehicle data was acquired at 3 locations in each city for a period of five days. Several steps were applied to separate vehicle noise from other noises, and to attribute the noise to the correct vehicles. The resulting analysis provided insight into the types of vehicles and driving conditions causing most noise. A set of characteristic driving conditions were derived from the measurement data, including high acceleration, engine revving, engine backfire, high engine speed and others. Besides driving behaviour, vehicle modifications such as louder exhausts, removed dB-killers and mechanical or electronic engine modifications are common causes of high noise levels. Also an overview of suggested mitigation options and future outlook is given, based on the findings of the analysis.

Scan-Specific Unsupervised Highly Accelerated Non-Cartesian CEST Imaging Using Implicit Neural Representation and Explicit Sparse Prior.

Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique. CEST imaging usually requires a long scan time, and reducing acquisition time is highly desirable for clinical applications. A novel scan-specific unsupervised deep learning algorithm is proposed to accelerate steady-state pulsed CEST imaging with golden-angle stack-of-stars trajectory using hybrid-feature hash encoding implicit neural representation. Additionally, imaging quality is further improved by using the explicit prior knowledge of low rank and weighted joint sparsity in the spatial and Z-spectral domain of CEST data. In the retrospective acceleration experiment, the proposed method outperforms other state-of-the-art algorithms (TDDIP, LRTES, kt-SLR, NeRP, CRNN, and PBCS) for the in vivo human brain dataset under various acceleration rates. In the prospective acceleration experiment, the proposed algorithm can still obtain results close to the fully-sampled images. The hybrid-feature hash encoding implicit neural representation combined with explicit sparse prior (INRESP) can efficiently accelerate CEST imaging. The proposed algorithm achieves reduced error and improved image quality compared to several state-of-the-art algorithms at relatively high acceleration factors. The superior performance and the training database-free characteristic make the proposed algorithm promising for accelerating CEST imaging in various applications.

Dynamic characteristics and damage identification with interface damage of slabs in movable point crossing

As the operating density of China’s high-speed railway increases, the phenomenon of interlayer debonding in movable point crossing (MPC) is becoming more prevalent, which can lead to failure of the frog’s slab track. Meanwhile, vehicle impacts and interlayer debonding can exacerbate abnormal vibration of the vehicle and even lead to derailment. Therefore, based on the theory of rigid-flexible coupling dynamics, a vehicle-turnout-slab coupling dynamics model is established for the first time. In this model, the crossing rails with variable cross sections is considered and the interlayer debonding between turnout slabs is modelled by nonlinear springs. Then, the characteristics of the debonding is identified by applying a wavelet transform to the dynamic response of the vehicle. Finally, different types and sizes of debonding between the layers are considered. The results show that different debonding can lead to two types of contact, partial contact and complete voiding, which can further lead to detrimental effects on vehicle safety. And the limit of the defect size that causes the track slab to void is 1 mm. On this basis, it was found that when MPC’s slab tracks were voided, longer debonding can cause high frequency vibration acceleration of approximately 700 Hz in the vehicle axle box. The new insights obtained by this study could provide guidance for the maintenance period and avoid the development of the damage. This study not only obtained a mechanism for the effect of interlayer debonding on the dynamic response of the vehicle-track system, but also provides a theoretical basis of detecting interlayer debonding for future study.

Computational modelling of cardiovascular pathophysiology to risk stratify commercial spaceflight.

For more than 60 years, humans have travelled into space. Until now, the majority of astronauts have been professional, government agency astronauts selected, in part, for their superlative physical fitness and the absence of disease. Commercial spaceflight is now becoming accessible to members of the public, many of whom would previously have been excluded owing to unsatisfactory fitness or the presence of cardiorespiratory diseases. While data exist on the effects of gravitational and acceleration (G) forces on human physiology, data ontheeffects of the aerospace environment in unselected members of the public, and particularly in those with clinically significant pathology, are limited. Although short in duration, these high acceleration forces can potentiallyeither impair the experience or, more seriously, pose a risk to health in some individuals. Rather than expose individuals with existing pathology to G forces to collect data, computational modelling might be useful to predict the nature and severity of cardiovascular diseases that are of sufficient risk to restrict access, require modification, or suggest further investigation or training before flight. In this Review, we explore state-of-the-art, zero-dimensional, compartmentalized models of human cardiovascular pathophysiology that can be used to simulate the effects of acceleration forces, homeostatic regulation and ventilation-perfusion matching, using data generated by long-arm centrifuge facilities of the US National Aeronautics and Space Administration and the European Space Agency to riskstratify individuals and help to improve safety in commercial suborbital spaceflight.

High Resolution Image Rotation Acceleration System Based on the ZYNQ Platform

High Resolution Image Rotation Acceleration System Based on the ZYNQ Platform

Spurious Accelerations in Finite Element Analysis of Geotechnical Problems: Cause and Remedy

Present study investigates the response of the two distinct geotechnical problems under earthquake loading. These two problems are: 1) nailed soil slope and 2) 1-D site response analysis. The aim of the study is to understand the cause of spurious accelerations and suggest a remedy. Two distinct interface conditions were considered in modelling the nailed soil slope. In first, the soil nail interface was considered to be perfectly bonded. In second case, the sliding at the interface was allowed. The analysis resulted into the physically acceptable deformations. However, the dumbbell shaped spurious acceleration pattern was observed in perfect bonding case. Further, the maximum acceleration was found to span from 4 g to 13 g. Such extremely high accelerations are unphysical and hence, unacceptable. In case of 1-D site response analysis, misleading acceleration spike of magnitude twice that of true acceleration was noted. The introduction of the numerical damping successfully removed the spurious accelerations and resulted in physically acceptable response for both problems.

Effects of the number of ball touches among elite youth players during small-sided soccer games.

This study investigates the effects of manipulating the number of ball touches (free play vs. two touches) on the physical and technical actions of elite male youth soccer players during 5v5 + 2 goalkeepers (GKs) small-sided games (SSGs). Players played in two different SSGs: 1) free play, where the number of touches per possession was not restricted; and 2) two touches, where players were limited to a maximum of two touches per ball possession. A total of 24 male elite youth soccer players (age: 14.79±0.71 years; body mass: 56.02±1.41 kg; body height: 164±2.12 cm) participated in the study. Players' physical metric and technical performance data were captured using a global positioning system and video camera, respectively. A paired-samples t-test or Wilcoxon Signed-Rank Test was employed to examine differences in players' technical performance variables depending on SSGs' ball-touch rules. The results show significantly more unsuccessful passes (t=-3.48; P=0.04; d=1.92) when players were limited to two touches than when there were no pass limits. The physical metrics indicate that total distance covered (Z=-2.90; P=0.001; d=0.07), meters per minute (Z=-3.44; P=0.001; d=0.11), low-speed running (Z=-2.25; P=0.02; d=0.04) and high acceleration (Z=-1.90; P=0.05; d=0.28) were significantly higher when touches were unlimited than when they were not. Soccer coaches should decide the number of touches per ball possession they allow depending on their tactical and/or physical objectives in training.

CRNN-Refined Spatiotemporal Transformer for Dynamic MRI reconstruction

Magnetic Resonance Imaging (MRI) plays a pivotal role in modern clinical practice, providing detailed anatomical visualization with exceptional spatial resolution and soft tissue contrast. Dynamic MRI, aiming to capture both spatial and temporal characteristics, faces challenges related to prolonged acquisition times and susceptibility to motion artifacts. Balancing spatial and temporal resolutions becomes crucial in real-world clinical scenarios. In the realm of dynamic MRI reconstruction, while Convolutional Recurrent Neural Networks (CRNNs) struggle with long-range dependencies, CRNNs require extensive iterations, impacting efficiency. Transformers, known for their effectiveness in high-dimensional imaging, are underexplored in dynamic MRI reconstruction. Additionally, prevailing algorithms fall short of achieving superior results in demanding generative reconstructions at high acceleration rates. This research proposes a novel approach for dynamic MRI reconstruction, named CRNN-Refined Spatiotemporal Transformer Network (CST-Net). The spatiotemporal Transformer initiates reconstruction, modeling temporal and spatial correlations, followed by refinement using the CRNN. This integration mitigates inaccuracies caused by damaged frames and reduces CRNN iterations, enhancing computational efficiency without compromising reconstruction quality. Our study compares the performance of the proposed CST-Net at 6 × and 12 × undersampling rates, showcasing its superiority over existing algorithms. Particularly, in challenging 25× generative reconstructions, the CST-Net outperforms current methods. The comparison includes experiments under both radial and Cartesian undersampling patterns. In conclusion, CST-Net successfully addresses the limitations inherent in existing generative reconstruction algorithms, thereby paving the way for further exploration and optimization of Transformer-based approaches in dynamic MRI reconstruction. Code and Datasets can be available: https://github.com/XWangBin/CST-Net.

Evaluation of Middle Cerebral Artery Culprit Plaque Inflammation in Ischemic Stroke Using CAIPIRINHA-Dixon-TWIST Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Middle cerebral artery (MCA) plaques are a leading cause of ischemic stroke (IS). Plaque inflammation is crucial for plaque stability and urgently needs quantitative detection. To explore the utility of Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA)-Dixon-Time-resolved angiography With Interleaved Stochastic Trajectories (TWIST) (CDT) dynamic contrast-enhanced MRI (DCE-MRI) for evaluating MCA culprit plaque inflammation changes over stroke time and with diabetes mellitus (DM). Prospective. Ninety-four patients (51.6 ± 12.23 years, 32 females, 23 DM) with acute IS (AIS; N = 43) and non-acute IS (non-AIS; 14 days < stroke time ≤ 3 months; N = 51). 3-T, CDT DCE-MRI and three-dimensional (3D) Sampling Perfection with Application optimized Contrast using different flip angle Evolution (3D-SPACE) T1-weighted imaging (T1WI). Stroke time (from initial IS symptoms to MRI) and DM were registered. For 94 MCA culprit plaques, Ktrans from CDT DCE-MRI and enhancement ratio (ER) from 3D-SPACE T1WI were compared between groups with and without AIS and DM. Shapiro-Wilk test, Bland-Altman analysis, Passing and Bablok test, independent t-test, Mann-Whitney U test, Chi-squared test, Fisher's exact test, receiver operating characteristics (ROC) with the area under the curve (AUC), DeLong's test, and Spearman rank correlation test with the P-value significance level of 0.05. Ktrans and ER of MCA culprit plaques were significantly higher in AIS than non-AIS patients (Ktrans = 0.098 s-1 vs. 0.037 s-1; ER = 0.86 vs. 0.55). Ktrans showed better AUC for distinguishing AIS from non-AIS patients (0.87 vs. 0.75) and stronger negative correlation with stroke time than ER (r = -0.60 vs. -0.34). DM patients had significantly higher Ktrans and ER than non-DM patients in IS and AIS groups. Imaging by CDT DCE-MRI may allow to quantitatively evaluate MCA culprit plaques over stroke time and DM. 2 TECHNICAL EFFICACY: Stage 2.

Crack-Based Composite Flexible Sensor with Superhydrophobicity to Detect Strain and Vibration.

Vibration sensors are widely applied in the detection of faults and analysis of operational states in engineering machinery and equipment. However, commercial vibration sensors with a feature of high hardness hinder their usage in some practical applications where the measured objects have irregular surfaces that are difficult to install. Moreover, as the operating environments of machinery become increasingly complex, there is a growing demand for sensors capable of working in wet and humid conditions. Here, we present a flexible, superhydrophobic vibration sensor with parallel microcracks. The sensor is fabricated using a femtosecond laser direct writing ablation strategy to create the parallel cracks on a PDMS film, followed by spray-coating with a conductive ink composed of MWCNTs, CB, and PDMS. The results demonstrate that the developed flexible sensor exhibits a high-frequency response of up to 2000 Hz, a high acceleration response of up to 100 m/s2, a water contact angle as high as 159.61°, and a linearity of 0.9812 between the voltage signal and acceleration. The results indicate that the sensor can be employed for underwater vibration, sound recognition, and vibration monitoring in fields such as shield cutters, holding significant potential for mechanical equipment vibration monitoring and speech-based human-machine interaction.

Elevating electron energy gain and betatron x-ray emission in proton-driven wakefield acceleration

The long proton beams present at CERN have the potential to evolve into a train of microbunches through the self-modulation instability process. The resonant wakefield generated by a periodic train of proton microbunches can establish a high acceleration field within the plasma, facilitating electron acceleration. This paper investigates the impact of plasma density on resonant wakefield excitation, thus influencing the acceleration of a witness electron bunch and its corresponding betatron radiation within the wakefield. Various scenarios involving different plasma densities are explored through particle-in-cell simulations. The peak wakefield in each scenario is calculated by considering a long pre-modulated proton driver with a fixed peak current. Subsequently, the study delves into the witness beam acceleration in the peak wakefield and its radiation emission. Elevated plasma density increases both the number of microbunches and the accelerating gradient of each microbunch, consequently resulting in heightened resonant wakefield. Nevertheless, the scaling is disrupted by the saturation of the resonant wakefield due to the nonlinearities. The simulation results reveal that at high plasma densities, an intense and broadband radiation spectrum extending into the domain of the hard x-rays and gamma rays is generated. Furthermore, in such instances, the energy gain of the witness beam is significantly enhanced. The impact of wakefield on the witness energy gain and the corresponding radiation spectrum is clearly evident at elevated densities.

Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.

To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reconstructed. Twelve-fold accelerated 3D T2-FLAIR images were obtained from a cohort of 62 patients with neurological deficits on 3T MRI. Images were reconstructed offline via CS and the CIRIM. Image quality was assessed in a blinded and randomized manner by two experienced interventional neuroradiologists and one experienced pediatric neuroradiologist on imaging artifacts, perceived spatial resolution (sharpness), anatomic conspicuity, diagnostic confidence, and contrast. The methods were also compared in terms of self-referenced quality metrics, image resolution, patient groups and reconstruction time. In ten scans, the contrast ratio (CR) was determined between lesions and white matter. The effect of acceleration factor was assessed in a publicly available fully sampled dataset, since ground truth data are not available in prospectively accelerated clinical scans. Specifically, 451 FLAIR scans, including scans with white matter lesions, were adopted from the FastMRI database to evaluate structural similarity (SSIM) and the CR of lesions and white matter on ranging acceleration factors from four-fold up to 12-fold. Interventional neuroradiologists significantly preferred the CIRIM for imaging artifacts, anatomic conspicuity, and contrast. One rater significantly preferred the CIRIM in terms of sharpness and diagnostic confidence. The pediatric neuroradiologist preferred CS for imaging artifacts and sharpness. Compared to CS, the CIRIM reconstructions significantly improved in terms of imaging artifacts and anatomic conspicuity (p < 0.01) for higher resolution scans while yielding a 28% higher SNR (p = 0.001) and a 5.8% lower CR (p = 0.04). There were no differences between patient groups. Additionally, CIRIM was five times faster than CS was. An increasing acceleration factor did not lead to changes in CR (p = 0.92), but led to lower SSIM (p = 0.002). Patients with neurological deficits can undergo MRI at a range of moderate to high acceleration. DL reconstruction outperforms CS in terms of image resolution, efficient denoising with a modest reduction in contrast and reduced reconstruction times.