Parallel Imaging Research Articles

Low-rank iterative infilling for zero echo-time (ZTE) imaging.

A new referenceless low-rank reconstruction technique has been introduced to address the issue of missing samples within the Zero Echo Time (ZTE) dead-time gap. The proposed method reformulates the in-filling of the missing samples as an inverse problem subject to low-rank constraints. Its performance and robustness are evaluated through a comparative analysis that combines Monte Carlo computational simulations and data obtained from in vivo experiments. The proposed method is tested for dead-time gaps ranging up to 4.5 Nyquist dwells, across signal-to-noise ratio levels of 5, 10, 15, and 20 dB. Consistently superior performance is observed across all cases compared to algebraic and parallel imaging methods. The speed for convergence decreases exponentially as the dead-time gap expands. The proposed method enables artifact-free reconstruction up to dead-time gap of 4 Nyquist dwells and thereby supports ZTE imaging up to an imaging bandwidth of kHz (assuming transmit and receive switching less than 30 s). It demonstrates superior performance compared to algebraic and parallel imaging methods.

Scalable Synaptic Transistor Memory from Solution-Processed Carbon Nanotubes for High-Speed Neuromorphic Data Processing.

Neural networks as a core information processing technology in machine learning and artificial intelligence demand substantial computational resources to deal with the extensive multiply-accumulate operations. Neuromorphic computing is an emergent solution to address this problem, allowing the computation performed in memory arrays in parallel with high efficiencies conforming to the neural networks. Here, scalable synaptic transistor memories are developed from solution-sorted carbon nanotubes. The transistors exhibit a large switching ratio of over 105, a significant memory window of ≈12V arising from charge trapping, and low response delays down to tens of nanoseconds. These device characteristics endow highly stabilized reconfigurable conductance states, successful emulation of synaptic functions, and a high data processing speed. Importantly, the devices exhibit uniform characteristic metrics, e.g., with a 1.8% variation in the memory window, suggesting an industrial-scale manufacturing capability of the fabrication. Using the memories, a hardware convolution kernel is designed and parallel image processing is demonstrated at a speed of 1M bit per second per input channel. Given the efficacy of the convolution kernel, a promising prospect of the memories in implementing neuromorphic computing is envisaged. To explore the potential, large-scale convolution kernels are simulated and high-speed video processing is realized for autonomous driving.

Self-Supervised Image Aesthetic Assessment Based on Transformer

Visual aesthetics has always been an important area of computational vision, and researchers have continued exploring it. To further improve the performance of the image aesthetic evaluation task, we introduce a Transformer into the image aesthetic evaluation task. This paper pioneers a novel self-supervised image aesthetic evaluation model founded upon Transformers. Meanwhile, we expand the pretext task to capture rich visual representations, adding a branch for inpainting the masked images in parallel with the tasks related to aesthetic quality degradation operations. Our model’s refinement employs the innovative uncertainty weighting method, seamlessly amalgamating three distinct losses into a unified objective. On the AVA dataset, our approach surpasses the efficacy of prevailing self-supervised image aesthetic assessment methods. Remarkably, we attain results approaching those of supervised methods, even while operating with a limited dataset. On the AADB dataset, our approach improves the aesthetic binary classification accuracy by roughly 16% compared to other self-supervised image aesthetic assessment methods and improves the prediction of aesthetic attributes.

High dynamic range mapping for the evaluation of parallel transmit arrays.

Demonstration of a high dynamic-range and high SNR method for acquiring absolute maps from a combination of gradient echo and actual-flip-angle measurements that is especially useful during the construction of parallel-transmit arrays. Low flip angle gradient echo images, acquired when transmitting with each channel individually, are used to compute relative maps. Instead of computing these in a conventional manner, the equivalence of the problem to the ESPIRiT parallel image reconstruction method is used to compute maps with a higher SNR. Absolute maps are generated by calibration against a single actual flip-angle acquisition when transmitting on all channels simultaneously. Depending on the number of receiver channels and the location of the receive elements with respect to the subject being investigated, moderate to high gains in the SNR of the acquired maps can be achieved. The proposed method is especially suited for the acquisition of maps during the construction of transceiver arrays. Compared to the original method, maps with higher SNR can be computed without the need for additional measurements, and maps can also be generated using previously acquired data. Furthermore, easy adoption and fast estimation of receiver channels is possible because of existing highly optimized open-source implementations of ESPIRiT, such as in the BART toolbox.

Macroscopic Monograin Nano-Gyroid Thin Films in 4-inch Scale Through Shear-Rolling and Subsequent Solvent Annealing.

The gyroid structure from self-assembly is highly attractive for optical applications such as photonic crystals and metamaterials. However, due to the direction-dependent nature of these applications, achieving a monograin-level structure over a large area has remained challenging. In this study, the fabrication of unidirectionally oriented anisotropic nanocylinders of polystyrene-block-polydimethylsiloxane (PS-b-PDMS) is reported block copolymer thin films up to a 4-inch scale via a shear-rolling process, followed by solvent annealing to induce phase transition to nearly monograin gyroid structures. Grazing-incidence small-angle X-ray scattering (GISAXS) analysis and cross-sectional scanning electron microscope (SEM) images in the direction parallel to the shear-rolling direction reveal the preferential orientation with the (111) plane onto the YZ axis of the film, while only the (110) plane on the YZ axis of the film is observed in the perpendicular direction, with grain sizes approaching single-grain levels. For metamaterial applications, the PDMS domain is selectively removed, and gold is electroplated to produce monograin gold gyroid films.

Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings

BackgroundConventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging.MethodsThis study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen’s kappa correlation coefficient (k) was employed to measure radiologists’ inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS.ResultsThe qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study’s findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality.ConclusionIntegrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.

Simultaneous multinuclear MRI via a single RF channel

Magnetic resonance imaging (MRI) stands as one of the most powerful noninvasive and non-destructive imaging techniques, finding extensive utility in medical and industrial applications. Its ability to acquire signals from multiple nuclei grants it additional levels of strength by providing multi-dimensional datasets of the object under test. However, this typically requires dedicated hardware to detect each nucleus. In this paper, we report on the use of a digital lock-in amplifier to perform simultaneous multi-nuclear MRI with a single physical radio frequency (RF) channel. While we showcase this concept by demonstrating the results of fully parallel (TX and RX) 1H and 19F MRI images, we emphasize that it is not limited to two nuclei but can accommodate more nuclei with no extra cost on the hardware or scan time. The scalability is virtually unlimited, constrained only by the processing speed of the digital unit. Furthermore, we demonstrate that the quality of parallel imaging with SNR of 54 is comparable to the commercial single channel with SNR of 43. Thus with no reduction in imaging quality, the proposed concept promises a tremendous reduction in scan time, system complexity, and hardware costs.

Accelerating brain three-dimensional T2 fluid-attenuated inversion recovery using artificial intelligence-assisted compressed sensing: a comparison study with parallel imaging.

Shortening the acquisition time of brain three-dimensional T2 fluid-attenuated inversion recovery (3D T2 FLAIR) by using acceleration techniques has the potential to reduce motion artifacts in images and facilitate clinical application. This study aimed to assess the image quality of brain 3D T2 FLAIR accelerated by artificial intelligence-assisted compressed sensing (ACS) in comparison to 3D T2 FLAIR accelerated by parallel imaging (PI). In this prospective cohort study, 102 consecutive participants, including both healthy individuals and those with suspected brain diseases, were recruited and underwent both ACS- and PI-3D T2 FLAIR scans with a 3.0-Tesla magnetic resonance imaging system from February 2023 to October 2023 in Beijing Tiantan Hospital, Capital Medical University. Quantitative assessment involved white matter (WM) and gray matter (GM) signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), whole-image sharpness, and tumor volume. Qualitative assessment included the scoring of overall image quality, GM-WM border sharpness, and diagnostic confidence in lesion detection. ACS-3D T2 FLAIR exhibited a shorter acquisition time compared to PI-3D T2 FLAIR (105 vs. 320 seconds). ACS-3D T2 FLAIR, compared to PI-3D T2 FLAIR, demonstrated a significantly higher mean SNRWM (25.922±6.811 vs. 22.544±5.853; P<0.001), SNRGM (18.324±7.137 vs. 17.102±6.659; P=0.049), CNRWM/GM (4.613±1.547 vs. 4.160±1.552; P<0.001), and sharpness (0.413±0.049 vs. 0.396±0.034; P<0.001), while no significant differences were found for the overall image quality ratings (P=0.063) or GM-WM border sharpness ratings (P=0.125). A good agreement on tumor volume was achieved between ACS-3D T2 FLAIR and PI-3D T2 FLAIR images (intraclass correlation coefficient =0.999; 0.998-1.000; P<0.001). Images acquired with ACS demonstrated nearly equivalent diagnostic confidence to those obtained with PI (P>0.05). The ACS technique offers a substantial reduction in scanning time for brain 3D T2 FLAIR compared to PI while maintaining good image quality and equivalent diagnostic confidence.

Intelligent extraction of salient objects from fuzzy distorted images based on multi-scale convolution

ABSTRACT To solve the problem of how to obtain important information from fuzzy distorted images, an intelligent method for extracting salient objects from fuzzy distorted images based on multi-scale convolution is proposed. A multi-scale deformation feature convolution network is established to extract the salient features of fuzzy distorted images. Among them, the multi-scale convolution network VGG-16 extracts the shallow features of the fuzzy distortion image through the convolution layer operation, uses the multi-scale convolution kernel to extract the fuzzy distortion image in parallel, and introduces the self-attention mechanism to make the extracted features more relevant, Input the fused features into the proposed area extraction network, Through the target area detection network, the proposed target area of the fuzzy distortion image is classified and position regression is performed to obtain the final salient target of the fuzzy distortion image. The experimental results show that this method can accurately recognize the contour of salient objects, and extract salient objects from fuzzy distorted images; The extracted salient objects have high accuracy, low mean square error and similarity of 92.9% with the original image; For the salient target extraction in various scenarios, it shows high advantages.

Pre-Trained Transformer-Based Parallel Multi-Channel Adaptive Image Sequence Interpolation Network

Pre-Trained Transformer-Based Parallel Multi-Channel Adaptive Image Sequence Interpolation Network

Interpreting the Projected Frontal Area in Front Crawl: Determining the Projected Frontal Area of Each Body Segment.

This study aimed to provide evidence for the interpretation of the projected frontal area (PFA) during front crawl. To achieve this goal, we developed a method for calculating the PFA of each body segment using digital human technology and compared the pressure drag under two calculation conditions: a combination of the PFA with and without accounting for the horizontal velocity of each body segment. Twelve competitive male swimmers performed a 15-meter front crawl at 1.20 m·s-1. The three-dimensional positions of the reflective markers attached to the swimmer's body were recorded using an underwater motion-capture system. Based on the body shape of each swimmer obtained from the photogenic body scanner, individual digital human body models were created with the color of the model's vertices divided into eight body segments. The time series of the volumetric swimming motion was reconstructed using inverse kinematics. The PFA of each body segment was then calculated by the automatic processing of a series of parallel frontal images. The pressure drag index, defined as the value excluding the drag coefficient while simultaneously considering the PFA and the horizontal velocity, was calculated under two conditions: the static condition (accounting for only the PFA of each body segment) and the dynamic condition (accounting for the PFA and horizontal velocity of each body segment). Notably, the pressure drag index was higher under the static condition than under the dynamic condition for the humerus, ulna, and hand segments (p < 0.001). The results obtained using our methodology indicate that the PFA of the upper limb segments overestimates their contribution to pressure drag during front crawl under the static condition.

Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power

In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or “folded,” images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled k-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from a priori k-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power.

Reduced physiology-induced temporal instability achieved with variable-flip-angle fast low-angle excitation echo-planar technique with multishot echo planar time-resolved imaging.

Echo planar time-resolved imaging (EPTI) is a new imaging approach that addresses the limitations of EPI by providing high-resolution, distortion- and T2/ blurring-free imaging for functional MRI (fMRI). However, as in all multishot sequences, intershot phase variations induced by physiological processes can introduce temporal instabilities to the reconstructed time-series data. This study aims to reduce these instabilities in multishot EPTI. In conventional multishot EPTI, the time intervals between the shots comprising each slice can introduce intershot phase variations. Here, the fast low-angle excitation echo-planar technique (FLEET), in which all shots of each slice are acquired consecutively with minimal time delays, was combined with a variable flip angle (VFA) technique to improve intershot consistency and maximize signal. A recursive Shinnar-Le Roux RF pulse design algorithm was used to generate pulses for different shots to produce consistent slice profiles and signal intensities across shots. Blipped controlled aliasing in parallel imaging simultaneous multislice was also combined with the proposed VFA-FLEET EPTI to improve temporal resolution and increase spatial coverage. The temporal stability of VFA-FLEET EPTI was compared with conventional EPTI at 7 T. The results demonstrated that VFA-FLEET can provide spatial-specific increase of temporal stability. We performed high-resolution task-fMRI experiments at 7 T using VFA-FLEET EPTI, and reliable BOLD responses to a visual stimulus were detected. The intershot phase variations induced by physiological processes in multishot EPTI can manifest as specific spatial patterns of physiological noise enhancement and lead to reduced temporal stability. The VFA-FLEET technique can substantially reduce these physiology-induced instabilities in multishot EPTI acquisitions. The proposed method provides sufficient stability and sensitivity for high-resolution fMRI studies.

High-accuracy 3D reconstruction of step edge with ray aliasing based on projective parallel single-pixel imaging

Optical 3D measurement technology especially fringe projection profilometry (FPP) is widely utilized in industrial production. However, invalid mixed phases are common when measuring step edges in industrial parts which leads to outliers in reconstructed point cloud and ultimately causes reduction of valid point cloud and dimensional measurement errors. This paper first analyzes reasons for failure of step edge measurement in FPP and illustrates feasibility of parallel single-pixel imaging (PSI) applied to eliminate ray aliasing of step edge. Further for a good balance of accuracy and efficiency, we propose an optimized projective parallel single-pixel imaging (pPSI) method combing with edge detection in wrapped phase generated by pPSI patterns and adaptive bi-direction projection strategy to accurately identify direct correspondence matching points of step edge. Experimental results verified that the proposed method can achieve high accuracy of 3D reconstruction on step edge while taking less measurement time.

POSE: POSition Encoding for accelerated quantitative MRI

Quantitative MRI utilizes multiple acquisitions with varying sequence parameters to sufficiently characterize a biophysical model of interest, resulting in undesirable scan times. Here we propose, validate and demonstrate a new general strategy for accelerating MRI using subvoxel shifting as a source of encoding called POSition Encoding (POSE). The POSE framework applies unique subvoxel shifts along the acquisition parameter dimension, thereby creating an extra source of encoding. Combining with a biophysical signal model of interest, accelerated and enhanced resolution maps of biophysical parameters are obtained. This has been validated and demonstrated through numerical Bloch equation simulations, phantom experiments and in vivo experiments using the variable flip angle signal model in 3D acquisitions as an application example. Monte Carlo simulations were performed using in vivo data to investigate our method's noise performance. POSE quantification results from numerical Bloch equation simulations of both a numerical phantom and realistic digital brain phantom concur well with the reference method, validating our method both theoretically and for realistic situations. NIST phantom experiment results show excellent overall agreement with the reference method, confirming our method's applicability for a wide range of T1 values. In vivo results not only exhibit good agreement with the reference method, but also show g-factors that significantly outperforms conventional parallel imaging methods with identical acceleration. Furthermore, our results show that POSE can be combined with parallel imaging to further accelerate while maintaining superior noise performance over parallel imaging that uses lower acceleration factors.

Nonlinear metamaterials enhanced surface coil array for parallel magnetic resonance imaging

Magnetic resonance imaging (MRI) is a crucial medical imaging modality, with parallel MRI accelerating scans but often reducing the signal-to-noise ratio (SNR). Recent advances in metamaterials have shown considerable potential in enhancing the SNR of MRI and consequently improving the quality of parallel MRI. In this study, we present a nonlinear metamaterial comprising nonlinear meta-atoms designed to selectively enhance the radio-frequency reception field in MRI, while minimizing interference with the radio-frequency transmission field. We develop an electromagnetic field-circuit joint simulation process for analyzing and optimizing the nonlinear response. Experimental validation confirms that nonlinear metamaterial integration in a surface coil array delivers a 3-fold SNR increase for parallel MRI compared to using the surface coil array alone. This research advances our understanding of such metamaterials and demonstrates their potential for practical utilization in MRI and clinical settings, thereby promising substantial enhancements in imaging capabilities.

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.

Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T

BackgroundWe aimed to determine the capabilities of compressed sensing (CS) and deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) for improving image quality while reducing examination time on female pelvic 1.5-T magnetic resonance imaging (MRI).MethodsFifty-two consecutive female patients with various pelvic diseases underwent MRI with T1- and T2-weighted sequences using CS and PI. All CS data was reconstructed with and without DLR. Signal-to-noise ratio (SNR) of muscle and contrast-to-noise ratio (CNR) between fat tissue and iliac muscle on T1-weighted images (T1WI) and between myometrium and straight muscle on T2-weighted images (T2WI) were determined through region-of-interest measurements. Overall image quality (OIQ) and diagnostic confidence level (DCL) were evaluated on 5-point scales. SNRs and CNRs were compared using Tukey’s test, and qualitative indexes using the Wilcoxon signed-rank test.ResultsSNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.010). CNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.003). OIQ of T1WI and T2WI obtained using CS with DLR were higher than that using CS without DLR or conventional PI (p < 0.001). DCL of T2WI obtained using CS with DLR was higher than that using conventional PI or CS without DLR (p < 0.001).ConclusionCS with DLR provided better image quality and shorter examination time than those obtainable with PI for female pelvic 1.5-T MRI.Relevance statementCS with DLR can be considered effective for attaining better image quality and shorter examination time for female pelvic MRI at 1.5 T compared with those obtainable with PI.Key PointsPatients underwent MRI with T1- and T2-weighted sequences using CS and PI.All CS data was reconstructed with and without DLR.CS with DLR allowed for examination times significantly shorter than those of PI and provided significantly higher signal- and CNRs, as well as OIQ.Graphical

Peripheral Non-Contrast MR Angiography Using FBI: Scan Time and T2 Blurring Reduction with 2D Parallel Imaging.

Non-contrast magnetic resonance angiography (NC-MRA), including fresh blood imaging (FBI), is a suitable choice for evaluating patients with peripheral artery disease (PAD). We evaluated standard FBI (sFBI) and centric ky-kz FBI (cFBI) acquisitions, using 1D and 2D parallel imaging factors (PIFs) to assess the trade-off between scan time and image quality due to blurring. The bilateral legs of four volunteers (mean age 33 years, two females) were imaged in the coronal plane using a body array coil with a posterior spine coil. Two types of sFBI and cFBI sequences with 1D PIF factor 5 in the phase encode (PE) direction (in-plane) and 2D PIF 3 (PE) × 2 (slice encode (SE)) (in-plane, through-slice) were studied. Image quality was evaluated by a radiologist, the vessel's signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured, and major vessel width was measured on the coronal maximum intensity projection (MIP) and 80-degree MIP. Results showed significant time reductions from 184 to 206 s on average when using sFBI down to 98 to 162 s when using cFBI (p = 0.003). Similar SNRs (averaging 200 to 370 across all sequences and PIF) and CNRs (averaging 190 to 360) for all techniques (p > 0.08) were found. There was no significant difference in the image quality (averaging 4.0 to 4.5; p > 0.2) or vessel width (averaging 4.1 to 4.9 mm; p > 0.1) on coronal MIP due to sequence or PIF. However, vessel width measured using 80-degree MIP demonstrated a significantly wider vessel in cFBI (5.6 to 6.8 mm) compared to sFBI (4.5 to 4.7 mm) (p = 0.022), and in 1D (4.7 to 6.8 mm) compared to 2D (4.5 to 5.6 mm) (p < 0.05) PIF. This demonstrated a trade-off in T2 blurring between 1D and 2D PIF: 1D using a PIF of 5 shortened the acquisition window, resulting in sharper arterial blood vessels in coronal images but significant blur in the 80-degree MIP. Two-dimensional PIF for cFBI provided a good balance between shorter scan time (relative to sFBI) and good sharpness in both in- and through-plane, while no benefit of 2D PIF was seen for sFBI. In conclusion, this study demonstrated the usefulness of FBI-based techniques for peripheral artery imaging and underscored the need to strike a balance between scan time and image quality in different planes through the use of 2D parallel imaging.

A 32-channel high-impedance honeycomb-shaped receive array for temporal lobes exploration at 11.7T.

The newly operational 11.7T Iseult scanner provides an improved global SNR in the human brain. This gain in SNR can be pushed even further locally by designing region-focused dense receive arrays. The temporal lobes are particularly interesting to neuroscientists as they are associated with language and concept recognition. Our main goal was to maximize the SNR in the temporal lobes and provide high-acceleration capabilities for fMRI studies. We designed and developed a 32-channel receive array made of non-overlapped hexagonal loops. The loops were arranged in a honeycomb pattern and targeted the temporal lobes. They were placed on a flexible neoprene cap closely fitting the head. A new stripline design with a high impedance was proposed and applied for the first time at 11.7T. Specific homebuilt miniaturized low-impedance preamplifiers were directly mounted on the loops, providing preamplifier decoupling in a compact and modular design. Using an anatomical phantom, we experimentally compared the SNR and parallel imaging performance of the region-focused cap to a 32-channel whole-brain receive array at 11.7T. The experimental results showed a 1.7-time higher SNR on average in the temporal lobes compared to the whole brain receive array. The g-factor is also improved when undersampling in the antero-posterior and head-foot directions. A significant SNR boost in the temporal lobes was demonstrated at 11.7T compared to the whole-brain receive array. The parallel imaging capabilities were also improved in the temporal lobes in some acceleration directions.