Parallel Imaging Research Articles

TagGen: Diffusion-based generative model for cardiac MR tagging super resolution.

The aim of the work is to develop a cascaded diffusion-based super-resolution model for low-resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag grid quality of low-resolution images. We introduced TagGen, a diffusion-based conditional generative model that uses low-resolution MR tagging images as guidance to generate corresponding high-resolution tagging images. The model was developed on 50 patients with long-axis-view, high-resolution tagging acquisitions. During training, we retrospectively synthesized LR tagging images using an undersampling rate (R) of 3.3 with truncated outer phase-encoding lines. During inference, we evaluated the performance of TagGen and compared it with REGAIN, a generative adversarial network-based super-resolution model that was previously applied to MR tagging. In addition, we prospectively acquired data from 6 subjects with three heartbeats per slice using 10-fold acceleration achieved by combining low-resolution R = 3.3 with GRAPPA-3 (generalized autocalibrating partially parallel acquisitions 3). For synthetic data (R = 3.3), TagGen outperformed REGAIN in terms of normalized root mean square error, peak signal-to-noise ratio, and structural similarity index (p < 0.05 for all). For prospectively 10-fold accelerated data, TagGen provided better tag grid quality, signal-to-noise ratio, and overall image quality than REGAIN, as scored by two (blinded) radiologists (p < 0.05 for all). We developed a diffusion-based generative super-resolution model for MR tagging images and demonstrated its potential to integrate with parallel imaging to reconstruct highly accelerated cine MR tagging images acquired in three heartbeats with enhanced tag grid quality.

High Impedance Microstrip Resonators and Transceiver Arrays for Ultrahigh Field 10.5T MR Imaging.

Multichannel transceiver coil arrays are needed to enable parallel imaging and B1 manipulation in ultrahigh field MR imaging and spectroscopy. However, the design of such transceiver coils and coil arrays often faces technical challenges in achieving the required high operating frequency at the ultrahigh fields and sufficient electromagnetic (EM) decoupling between resonant elements. In this work, we propose a high impedance microstrip transmission line resonator (HIMTL) technique that has unique high frequency capability and adequate EM decoupling without the use of dedicated decoupling circuits. To validate the proposed technique for the ultrahigh field 10.5T applications, a two-channel high impedance microstrip array with the element dimension of 8cm by 8cm was built and tuned to 447 MHz, Larmor frequency of proton at 10.5T, for signal excitation and reception. Bench tests and numerical simulations were performed to evaluate its feasibility and performance. The results show that the proposed high impedance microstrip technique can be a simple and robust way to design high frequency transceiver coils and coil arrays for ultrahigh field MR applications.

Cardiac MR image reconstruction using cascaded hybrid dual domain deep learning framework.

Recovering diagnostic-quality cardiac MR images from highly under-sampled data is a current research focus, particularly in addressing cardiac and respiratory motion. Techniques such as Compressed Sensing (CS) and Parallel Imaging (pMRI) have been proposed to accelerate MRI data acquisition and improve image quality. However, these methods have limitations in high spatial-resolution applications, often resulting in blurring or residual artifacts. Recently, deep learning-based techniques have gained attention for their accuracy and efficiency in image reconstruction. Deep learning-based MR image reconstruction methods are divided into two categories: (a) single domain methods (image domain learning and k-space domain learning) and (b) cross/dual domain methods. Single domain methods, which typically use U-Net in either the image or k-space domain, fail to fully exploit the correlation between these domains. This paper introduces a dual-domain deep learning approach that incorporates multi-coil data consistency (MCDC) layers for reconstructing cardiac MR images from 1-D Variable Density (VD) random under-sampled data. The proposed hybrid dual-domain deep learning models integrate data from both the domains to improve image quality, reduce artifacts, and enhance overall robustness and accuracy of the reconstruction process. Experimental results demonstrate that the proposed methods outperform than conventional deep learning and CS techniques, as evidenced by higher Structural Similarity Index (SSIM), lower Root Mean Square Error (RMSE), and higher Peak Signal-to-Noise Ratio (PSNR).

How to Implement Clinical 7T MRI-Practical Considerations and Experience with Ultra-High-Field MRI.

The implementation of clinical 7T MRI presents both opportunities and challenges for advanced medical imaging. This tutorial provides practical considerations and experiences with 7T MRI in clinical settings. We first explore the history and evolution of MRI technology, highlighting the benefits of increased signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and susceptibility at 7T. Technical challenges such as increased susceptibility artifacts and RF inhomogeneity are also discussed, along with innovative adaptations. This review also discusses hardware and software considerations, including new parallel transmission head coils and advanced image processing techniques to optimize image quality. Safety considerations, such as managing tissue heating and susceptibility to artifacts, are also discussed. Additionally, clinical applications of 7T MRI are examined, focusing on neurological conditions such as epilepsy, multiple sclerosis, and vascular imaging. Emerging trends in the use of 7T MRI for spectroscopy, perfusion imaging, and multinuclear imaging are explored, with insights into the future of ultra-high-field MRI in clinical practice. This review aims to provide clinicians, technologists, and researchers with a roadmap for successfully implementing 7T MRI in both research and clinical environments.

Understanding Permeability Changes in Vestibular Schwannomas as Part of the Dynamic Response to Radiosurgery Using Golden-Angle Radial Sparse Parallel Imaging: A Retrospective Study

BACKGROUND AND OBJECTIVES: Vestibular schwannomas demonstrate different responses after stereotactic radiosurgery (SRS), commonly including a transient loss of internal enhancement on postcontrast T1-weighted MRI thought to be due to an early reduction in tumor vascularity. We used dynamic contrast-enhanced based golden-angle radial sparse parallel (GRASP) MRI to characterize the vascular permeability changes underlying this phenomenon, with correlations to long-term tumor regression. METHODS: Consecutive patients with vestibular schwannoma who underwent SRS between 2017 and 2019, had a transient loss of enhancement after SRS, and had long-term longitudinal GRASP studies (6, 18, and 30 months) were included in this retrospective cohort analysis (n = 19). Using GRAVIS (https://gravis-imaging.org/gravis/), an analysis pipeline for GRASP studies, we extracted the key parameters normalized to the venous sinus from a region of interest within the tumor. RESULTS: The peak, area under the curve (AUC), and wash-in phase slope were significantly reduced at 6, 18, and 30 months after SRS (corrected P &lt; .05), even while the internal enhancement returned in the tumors. Larger pre-SRS tumors were more likely to have a greater reduction in peak (P = .013) and AUC (P = .029) at 6 months. In a subset of patients (N = 13) with long-term follow-up, the median percentage reduction in tumor volume was 58% at a median of 62 months. These patients showed a strong correlation between peak, AUC, and wash-in phase slope changes at 6 months and tumor volume at the last follow-up. CONCLUSION: After SRS and loss of internal contrast uptake within vestibular schwannomas, a slow vascular permeability dynamic persisted, suggesting the presence of postradiation processes such as fibrosis. We show for the first time, using GRASP, a quantitative assessment of the vascular radiobiological effect.

Process tomography of structured optical gates with convolutional neural networks

Abstract Efficient and accurate characterization of an experimental setup is a critical requirement in any physical setting. In the quantum realm, the characterization of an unknown operator is experimentally accomplished via Quantum Process Tomography (QPT). This technique combines the outcomes of different projective measurements to reconstruct the underlying process matrix, typically extracted from maximum-likelihood estimation. Here, we exploit the logical correspondence between optical polarization and two-level quantum systems to retrieve the complex action of structured metasurfaces within a QPT-inspired context. In particular, we investigate a deep-learning approach that allows for fast and accurate reconstructions of space-dependent SU(2) operators by only processing a minimal set of measurements. We train a convolutional neural network based on a scalable U-Net architecture to process entire experimental images in parallel. Synthetic processes are reconstructed with average fidelity above 90%. The performance of our routine is experimentally validated in the case of space-dependent polarization transformations acting on a classical laser beam. Our approach further expands the toolbox of data-driven approaches to QPT and shows promise in the real-time characterization of complex optical gates.

Partition-based k-space synthesis for multi-contrast parallel imaging.

Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique. However, multi-contrast imaging has longer acquisition time and is easy to cause motion artifacts. In particular, the acquisition time for a T2-weighted image is prolonged due to its longer repetition time (TR). On the contrary, T1-weighted image has a shorter TR. Therefore, utilizing complementary information across T1 and T2-weighted image is a way to decrease the overall imaging time. Previous T1-assisted T2 reconstruction methods have mostly focused on image domain using whole-based image fusion approaches. The image domain reconstruction method has the defects of high computational complexity and limited flexibility. To address this issue, we propose a novel multi-contrast imaging method called partition-based k-space synthesis (PKS) which can achieve better reconstruction quality of T2-weighted image by feature fusion. Concretely, we first decompose fully-sampled T1 k-space data and under-sampled T2 k-space data into two sub-data, separately. Then two new objects are constructed by combining the two sub-T1/T2 data. After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image. Experimental results showed that the developed PKS scheme can achieve comparable or better results than using traditional k-space parallel imaging (SAKE) that processes each contrast independently. At the same time, our method showed good adaptability and robustness under different contrast-assisted and T1-T2 ratios. Efficient target modal image reconstruction under various conditions were realized and had excellent performance in restoring image quality and preserving details. This work proposed a PKS multi-contrast method to assist in target mode image reconstruction. We have conducted extensive experiments on different multi-contrast, diverse ratios of T1 to T2 and different sampling masks to demonstrate the generalization and robustness of our proposed model.

Eliminating electromagnetic interference for RF shielding-free MRI via k-space convolution: Insights from MR parallel imaging advances.

Recent advances in ultra-low field MRI have attracted attention from both academic and industrial MR communities for its potential in democratizing MRI applications. One of the most striking features on those advances is shielding-free imaging by actively sensing and eliminating the electromagnetic interference (EMI). In this study, we review the analytical approaches for EMI estimation/elimination, and investigate their theoretical basis and relations with parallel imaging reconstruction. We provide further understanding of the existing approaches, formulating EMI estimation as convolution in k-space or multiplication in spectrum-space. We further propose to use tailored convolutional kernel to adaptively fit the varying EMI coupling across the acquisition window. These methods were evaluated with both simulation study and human brain imaging. The results show that using tailored convolutional kernel can achieve more robust performance against system and acquisition imperfections.

Prospective acceleration of whole-brain CEST imaging by golden-angle view ordering in Cartesian coordinates and joint k-space and image-space parallel imaging (KIPI).

To prospectively accelerate whole-brain CEST acquisition by joint k-space and image-space parallel imaging (KIPI) with a proposed golden-angle view ordering technique (GAVOT) in Cartesian coordinates. The T2-decay effect will vary across frames with variable acceleration factors (AF) in the prospective acquisition using sequences with long echo trains. The GAVOT method uses a subset strategy to eliminate the T2-decay inconsistency, where all frames use a subset of shots from the calibration frame to form their k-space view ordering. The golden-angle rule is adapted to ensure uniform k-space coverage for arbitrary AFs in Cartesian coordinates. Phantom and in vivo studies were conducted on a 3 T scanner. The GAVOT view ordering yielded a higher g-factor than conventional uniformly centric ordering, whereas the noise propagation in amide proton transfer (APT) weighted images was similar between different view ordering. Compared to centric ordering, GAVOT successfully eliminated the T2-decay inconsistency across all frames, resulting in fewer image artifacts for both KIPI and conventional parallel imaging methods. The synergy of GAVOT and KIPI mitigated strong aliasing artifacts and achieved high-quality reconstruction of prospective variable-AF datasets. GAVOT-KIPI reduced the scan time to 2.1 min for whole-brain APT weighted imaging and 4.7 min for quantitative APT signal (APT#) mapping. GAVOT makes the prospective variable AF strategy flexible and practical, and, in conjunction with KIPI, ensures high-quality reconstruction from highly undersampled data, facilitating the clinical translation of whole-brain CEST imaging.

Perceptually-calibrated synergy network for night-time image quality assessment with enhancement booster and knowledge cross-sharing

Image quality assessment (IQA) and image enhancement (IE) of night-time images are highly correlated tasks. On the one hand, IQA task could obtain more complementary information from the enhanced image. On the other hand, IE task would benefit from the prior knowledge of quality-aware attributes. Thus, we propose a Perceptually-calibrated Synergy Network (PCSNet) to simultaneously predict and enhance image quality of night-time images. More specifically, a shared shallow network is applied to extract the shared features for both tasks by leveraging complementary in-formation. The shared features are then fed to task-specific sub-networks to predict quality scores and generate enhanced images in parallel. In order to better exploit the interaction of complementary information, intermediate Cross-Sharing Modules are used to form efficient feature representations for the image quality assessment (IQA) and image enhancement (IE) subnetworks. Experimental results of the night-time image datasets show that the proposed approach achieves state-of-the-art performance on both quality prediction and image enhancement tasks.

(Corrosion Division Morris Cohen Graduate Student Award) Respirometric Monitoring of Corrosion

Accurate measurement of the corrosion rate of metals and alloys under realistic exposure conditions is crucial for the elucidation of corrosion mechanisms, the development of new corrosion protection measures, and for reliable corrosion prediction. A respirometric approach to monitor corrosion rates based on quantifying the rates of the main cathodic reactions during corrosion is presented. With the proposed gravimetric, manometric, or flow cell experimental setups it is possible to follow the rates of the H2 evolution reaction (HER), the O2 reduction reaction (ORR), or both cathodic reactions simultaneously.The respirometric method can universally provide real-time corrosion rate monitoring for atmospheric corrosion conditions under thin electrolyte films, where suitable corrosion monitoring capabilities have been lacking so far. Furthermore, respirometric methods applicable to corrosion under immersion conditions are presented and coupled with electrochemical measurements. It was demonstrated on various relevant engineering metals and alloys that reliable and sensitive real-time monitoring is possible without influencing the corrosion process by the measurement itself. The accuracy and validity of the developed methodology was confirmed by independent parallel measurement methods such as mass loss, solution analysis by ion chromatography, profilometry, and electrochemical measurements. Parallel imaging of the corroding surface was carried out to visually link changes of the respirometric rates and of the exposure conditions to observations of the surface morphology, gas evolution and corrosion propagation.Besides measuring corrosion rates, the respirometric method can be used to elucidate corrosion mechanisms. The effect of changes in different exposure conditions on the corrosion rate and on the rate of the cathodic reactions could be followed in situ. This includes changes in the temperature, the composition of electrolyte, or wet–dry cycling. The role of the ORR for Mg alloy corrosion was investigated and it was found that besides HER, the ORR contributes substantially to the cathodic reactions both for atmospheric corrosion and immersion. A change in the mechanism from ORR to HER was observed with the onset of localized corrosion of Al alloys under atmospheric conditions. In the context of biodegradable metallic implants, the time-dependent corrosion behavior of Zn and Mg alloys was investigated and compared in simulated body fluids of increasing complexity. The contribution of the two-electron ORR pathway was confirmed for Zn under immersion conditions and quantified for the different simulated body fluids.By coupling the respirometric method with electrochemical measurements, it was shown that the contribution of the involved reactions and pathways to the net electric current can be extracted at different applied currents or potentials. As a result, it was possible to deconvolute dynamic polarization curves into the contributing partial reactions. Unexpected cathodic reactions (negative difference effect) during localized corrosion at anodic polarization as well as unexpected anodic dissolution (cathodic corrosion) under cathodic polarization could be revealed and quantified with the respirometric method. Moreover, the fraction of anodic current related to the O2 evolution reaction could be differentiated from the fraction of current that caused transpassive corrosion.A more sensitive respirometric method was developed that is suitable to measure corrosion rates of more corrosion resistant materials or materials in weakly corrosive environments. Corrosion scenarios that were investigated with this high resolution respirometric method include breakdown of passivity of Al and stainless steel, H2 evolution of Zn anodes in Zn ion batteries and corrosion of metals in ionic liquids. While this approach is certainly promising for laboratory applications, also the applicability to respirometric field measurements will be demonstrated. A respirometric probe was developed that can be attached to corroding structures in the field.Overall, the respirometric method provides a monitoring tool to establish the relationship between relevant exposure parameters and accurate corrosion rates. The advantages and possibilities of the developed respirometric methods open up new doors to in situ investigation of corrosion and other electrode processes Figure 1

Comparison of different acceleration factors of artificial intelligence-compressed sensing for brachial plexus MRI imaging: scanning time and image quality

Background3D brachial plexus MRI scanning is prone to examination failure due to the lengthy scan times, which can lead to patient discomfort and motion artifacts. Our purpose is to investigate the efficacy of artificial intelligence-assisted compressed sensing (ACS) in improving the acceleration efficiency and maintaining or enhancing the image quality of brachial plexus MR imaging.MethodsA total of 30 volunteers underwent 3D sampling perfection with application-optimized contrast using different flip angle evolution short time inversion recovery using a 3.0T MR scanner. The imaging protocol included parallel imaging (PI) and ACS employing acceleration factors of 4.37, 6.22, and 9.03. Radiologists evaluated the neural detail display, fat suppression effectiveness, presence of image artifacts, and overall image quality. Signal intensity and standard deviation of specific anatomical sites within the brachial plexus and background tissues were measured, with signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) subsequently calculated. Cohen's weighted kappa (κ), One-way ANOVA, Kruskal–Wallis and pairwise comparisons with Bonferroni-adjusted significance level. P < 0.05 was considered statistically significant.ResultsACS significantly reduced scanning times compared to PI. Evaluations revealed differences in subjective scores and SNR across the sequences (P < 0.05), with no marked differences in CNR (P > 0.05). For subjective scores, ACS 9.03 were lower than the other three sequences in neural details display, image artifacts and overall image quality. There was no significant difference in fat suppression. For objective quantitative evaluation, SNR of right C6 root in ACS 6.22 and ACS 9.03 was higher than that in PI; SNR of left C6 root in ACS 4.37, ACS 6.22 and ACS 9.03 was higher than that in PI; SNR of medial cord in ACS 6.22, ACS 9.03 was higher than that in PI.ConclusionCompared with PI, ACS can shorten scanning time while ensuring good image quality.

MRI denoising with a non-blind deep complex-valued convolutional neural network.

MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex-valued convolutional neural network (CNN) for MRI denoising. A complex-valued CNN incorporating the noise level map (non-blind DnCNN) was trained with ground truth and simulated noise-corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low-field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real-valued CNN and several other algorithms. For the simulated noise-corrupted testing dataset, the complex-valued models had superior normalized root-mean-square error, peak SNR, structural similarity index, and phase ABSD. By incorporating the noise level map, the non-blind DnCNN showed better performance in dealing with spatially varying parallel imaging noise. For in vivo low-field data, the non-blind DnCNN significantly improved the SNR and visual quality of the image. The proposed non-blind DnCNN provides an efficient and effective approach for MRI denoising. This is the first application of non-blind DnCNN to medical imaging. The method holds the potential to enable improved low-field MRI, facilitating enhanced diagnostic imaging in under-resourced areas.

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.

Medium Bands, Mega Science: A JWST/NIRCam Medium-band Imaging Survey of A2744

In this paper, we describe the “Medium Bands, Mega Science” JWST Cycle 2 survey (JWST-GO-4111) and demonstrate the power of these data to reveal both the spatially integrated and spatially resolved properties of galaxies from the local Universe to the era of cosmic dawn. Executed in 2023 November, MegaScience obtained ∼30 arcmin2 of deep multiband NIRCam imaging centered on the z ∼ 0.3 A2744 cluster, including 11 medium-band filters and the two shortest-wavelength broadband filters, F070W and F090W. Together, MegaScience and the UNCOVER Cycle 1 treasury program provide a complete set of deep (∼28–30 magAB) images in all NIRCam medium- and broadband filters. This unique data set allows us to precisely constrain photometric redshifts, map stellar populations and dust attenuation for large samples of distant galaxies, and examine the connection between galaxy structures and formation histories. MegaScience also includes ∼17 arcmin2 of NIRISS parallel imaging in two broadband and four medium-band filters from 0.9 to 4.8 μm, expanding the footprint where robust spectral energy distribution (SED) fitting is possible. We provide example SEDs and multiband cutouts at a variety of redshifts, and use a catalog of JWST spectroscopic redshifts to show that MegaScience improves both the scatter and catastrophic outlier rate of photometric redshifts by factors of 2–3. Additionally, we demonstrate the spatially resolved science enabled by MegaScience by presenting maps of the [O iii] line emission and continuum emission in three spectroscopically confirmed z > 6 galaxies. We show that line emission in reionization-era galaxies can be clumpy, extended, and spatially offset from continuum emission, implying that galaxy assembly histories are complex even at these early epochs. We publicly release fully reduced mosaics and photometric catalogs for both the NIRCam primary and NIRISS parallel fields (jwst-uncover.github.io/megascience).

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.

Image reconstruction impacts haemodynamic parameters derived from 4D flow magnetic resonance imaging with compressed sensing.

4D blood flow measurements by cardiac magnetic resonance imaging (CMR) can be used to simplify blood flow assessment. Compressed sensing (CS) can provide better flow measurements than conventional parallel imaging (PI), but clinical validation is needed. This study aimed to validate stroke volume (SV) measurements by 4D-CS in healthy volunteers and patients while also investigating the influence of the CS image reconstruction parameter λ on haemodynamic parameters. Healthy participants (n = 9; 20-62 years) underwent CMR with 2D, 4D-CS, and 4D-PI flow. Patients (n = 30, 17 with congenital heart defect; 2-75 years) had 4D-CS added to their clinical examination. Impact of λ was assessed by reconstructing 4D-CS data for six different λ values. In healthy volunteers, 4D-CS and 4D-PI SV differed by 0.4 ± 6.5 mL [0.6 ± 9.1%; intraclass correlation coefficient (ICC) 0.98], and 4D-CS and 2D flow by 0.9 ± 7.0 mL (0.9 ± 10.6%; ICC 0.98). In patients, 4D-CS and 2D flow differed by -1.3 ± 6.0 mL (-7.2 ± 20%; ICC 0.97). SV was not dependent on λ in patients (P = 0.75) but an increase in λ by 0.001 led to increased differences between 4D-CS and 4D-PI of -0.4% (P = 0.0021) in healthy participants. There were significant differences for ventricular kinetic energy (systole: P < 0.0001; diastole: P < 0.0001) and haemodynamic forces (systole: P < 0.0001; diastole: P < 0.0001), where error increased with increasing λ values in both healthy participants and patients. 4D flow CMR with CS can be used clinically to assess SV in paediatric and adult patients. Ventricular kinetic energy and haemodynamic forces are however sensitive to the change in reconstruction parameter λ, and it is therefore important to validate advanced blood flow measurements before comparing data between scanners and centres.

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.