Conflicting sensory information sharpens the neural representations of early selective visuospatial attention.

Adaptive behaviors require the ability to resolve conflicting information caused by the processing of incompatible sensory inputs. Prominent theories of attention have posited that early selective attention helps mitigate cognitive interference caused by conflicting sensory information by facilitating the processing of task-relevant sensory inputs and filtering out behaviorally irrelevant information. Surprisingly, many recent studies that investigated the role of early selective attention on conflict mitigation have failed to provide positive evidence. Here, we examined changes in the selectivity of early visuospatial attention in male and female human subjects performing an attention-cueing Eriksen flanker task, where they discriminated the shape of a visual target surrounded by congruent or incongruent distractors. We used the inverted encoding model to reconstruct spatial representations of visual selective attention from the topographical patterns of amplitude modulations in alpha-band oscillations in scalp EEG (∼8-12Hz). We found that the fidelity of the alpha-based spatial reconstruction was significantly higher in the incongruent compared to the congruent condition. Importantly, these conflict-related modulations in the reconstruction fidelity occurred at a much earlier time window than those of the lateralized posterior event-related potentials (ERPs) associated with target selection and distractor suppression processes, as well as conflict-related modulations in the fronto-central negative-going wave and midline-frontal theta oscillations (∼3-7 Hz), thought to track executive control functions. Taken together, our data suggest that conflict resolution is supported by the cascade of neural processes underlying early selective visuospatial attention and frontal executive functions that unfold over time.Significance Statement The ability to resolve conflict is essential for adaptive behaviors. Here, we utilized a model-based approach to examine conflict-related changes in spatial representations of visuospatial attention from alpha band oscillations (∼8-12Hz) in EEG obtained from human participants performing an attention-cueing Eriksen flanker task. We observed increased fidelity of neural representations of early visuospatial attention immediately after stimulus onset in trials with incongruent compared to congruent distractors. Moreover, these conflict-related changes in the alpha-based representations occurred more rapidly than overall changes in parietal and frontal activity, which are thought to track late selection processes. This finding implicates the role of early selective visuospatial attention in mitigating cognitive interference and emphasizes that attention mechanisms involve multiple selection processes that evolve over time.

Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement.

OBJECTIVE: This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was also assessed through the application of brain volume measurement (BVM). MATERIALS AND METHODS: In vivo brain images acquired with 3D-T1W from various MRI scanners were utilized. For model training, LR images were generated by downsampling the original 1 mm-2 mm isotropic resolution images. Pairs of LR and HR images were used for training 3D residual dense net (RDN). For model testing, actual scanned 2 mm isotropic resolution 3D-T1W images with one-minute scan time were used. Normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used for model evaluation. The evaluation also included brain volume measurement, with assessments of subcortical brain regions. RESULTS: The results showed that DL-SR model improved the quality of LR images compared with cubic interpolation, as indicated by NRMSE (24.22% vs 30.13%), PSNR (26.19 vs 24.65), and SSIM (0.96 vs 0.95). For volumetric assessments, there were no significant differences between DL-SR and actual HR images (p > 0.05, Pearson's correlation > 0.90) at seven subcortical regions. DISCUSSION: The combination of LR MRI and DL-SR enables addressing prolonged scan time in 3D MRI scans while providing sufficient image quality without affecting brain volume measurement.

Sustained attention operates via dissociable neural mechanisms across different eccentric locations.

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

Improved reconstruction for highly accelerated propeller diffusion 1.5 T clinical MRI.

PURPOSE: Propeller fast-spin-echo diffusion magnetic resonance imaging (FSE-dMRI) is essential for the diagnosis of Cholesteatoma. However, at clinical 1.5 T MRI, its signal-to-noise ratio (SNR) remains relatively low. To gain sufficient SNR, signal averaging (number of excitations, NEX) is usually used with the cost of prolonged scan time. In this work, we leveraged the benefits of Locally Low Rank (LLR) constrained reconstruction to enhance the SNR. Furthermore, we enhanced both the speed and SNR by employing Convolutional Neural Networks (CNNs) for the accelerated PROPELLER FSE-dMRI on a 1.5 T clinical scanner. METHODS: Residual U-Net (RU-Net) was found to be efficient for propeller FSE-dMRI data. It was trained to predict 2-NEX images obtained by Locally Low Rank (LLR) constrained reconstruction and used 1-NEX images obtained via simplified reconstruction as the inputs. The brain scans from healthy volunteers and patients with cholesteatoma were performed for model training and testing. The performance of trained networks was evaluated with normalized root-mean-square-error (NRMSE), structural similarity index measure (SSIM), and peak SNR (PSNR). RESULTS: For 4 × under-sampled with 7 blades data, online reconstruction appears to provide suboptimal images-some small details are missing due to high noise interferences. Offline LLR enables suppression of noises and discovering some small structures. RU-Net demonstrated further improvement compared to LLR by increasing 18.87% of PSNR, 2.11% of SSIM, and reducing 53.84% of NRMSE. Moreover, RU-Net is about 1500 × faster than LLR (0.03 vs. 47.59 s/slice). CONCLUSION: The LLR remarkably enhances the SNR compared to online reconstruction. Moreover, RU-Net improves propeller FSE-dMRI as reflected in PSNR, SSIM, and NRMSE. It requires only 1-NEX data, which allows a 2 × scan time reduction. In addition, its speed is approximately 1500 times faster than that of LLR-constrained reconstruction.

Sustained attention operates via dissociable neural mechanisms across different eccentric locations.

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

Attentive pairwise interaction network for AI-assisted clock drawing test assessment of early visuospatial deficits.

Dementia is a debilitating neurological condition which impairs the cognitive function and the ability to take care of oneself. The Clock Drawing Test (CDT) is widely used to detect dementia, but differentiating normal from borderline cases requires years of clinical experience. Misclassifying mild abnormal as normal will delay the chance to investigate for potential reversible causes or slow down the progression. To help address this issue, we propose an automatic CDT scoring system that adopts Attentive Pairwise Interaction Network (API-Net), a fine-grained deep learning model that is designed to distinguish visually similar images. Inspired by how humans often learn to recognize different objects by looking at two images side-by-side, API-Net is optimized using image pairs in a contrastive manner, as opposed to standard supervised learning, which optimizes a model using individual images. In this study, we extend API-Net to infer Shulman CDT scores from a dataset of 3108 subjects. We compare the performance of API-Net to that of convolutional neural networks: VGG16, ResNet-152, and DenseNet-121. The best API-Net achieves an F1-score of 0.79, which is a 3% absolute improvement over ResNet-152's F1-score of 0.76. The code for API-Net and the dataset used have been made available at https://github.com/cccnlab/CDT-API-Network .

Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction.

Introduction: Spatio-temporal MRI methods enable whole-brain multi-parametric mapping at ultra-fast acquisition times through efficient k-space encoding, but can have very long reconstruction times, which limit their integration into clinical practice. Deep learning (DL) is a promising approach to accelerate reconstruction, but can be computationally intensive to train and deploy due to the large dimensionality of spatio-temporal MRI. DL methods also need large training data sets and can produce results that don't match the acquired data if data consistency is not enforced. The aim of this project is to reduce reconstruction time using DL whilst simultaneously limiting the risk of deep learning induced hallucinations, all with modest hardware requirements. Methods: Deep Learning Initialized Compressed Sensing (Deli-CS) is proposed to reduce the reconstruction time of iterative reconstructions by "kick-starting" the iterative reconstruction with a DL generated starting point. The proposed framework is applied to volumetric multi-axis spiral projection MRF that achieves whole-brain T1 and T2 mapping at 1-mm isotropic resolution for a 2-minute acquisition. First, the traditional reconstruction is optimized from over two hours to less than 40 minutes while using more than 90% less RAM and only 4.7 GB GPU memory, by using a memory-efficient GPU implementation. The Deli-CS framework is then implemented and evaluated against the above reconstruction. Results: Deli-CS achieves comparable reconstruction quality with 50% fewer iterations bringing the full reconstruction time to 20 minutes. Conclusion: Deli-CS reduces the reconstruction time of subspace reconstruction of volumetric spatio-temporal acquisitions by providing a warm start to the iterative reconstruction algorithm.

Wave-Encoded Model-Based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction.

A recently introduced model-based deep learning (MoDL) technique successfully incorporates convolutional neural network (CNN)-based regularizers into physics-based parallel imaging reconstruction using a small number of network parameters. Wave-controlled aliasing in parallel imaging (CAIPI) is an emerging parallel imaging method that accelerates imaging acquisition by employing sinusoidal gradients in the phase- and slice/partition-encoding directions during the readout to take better advantage of 3D coil sensitivity profiles. We propose wave-encoded MoDL (wave-MoDL) combining the wave-encoding strategy with unrolled network constraints for highly accelerated 3D imaging while enforcing data consistency. We extend wave-MoDL to reconstruct multicontrast data with CAIPI sampling patterns to leverage similarity between multiple images to improve the reconstruction quality. We further exploit this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T2 preparation pulse (3D-QALAS). Wave-MoDL enables a 40 s MPRAGE acquisition at 1 mm resolution at 16-fold acceleration. For quantitative imaging, wave-MoDL permits a 1:50 min acquisition for T1, T2, and proton density mapping at 1 mm resolution at 12-fold acceleration, from which contrast-weighted images can be synthesized as well. In conclusion, wave-MoDL allows rapid MR acquisition and high-fidelity image reconstruction and may facilitate clinical and neuroscientific applications by incorporating unrolled neural networks into wave-CAIPI reconstruction.

An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks.

BACKGROUND: Mild cognitive impairment (MCI) is an early stage of cognitive decline which could develop into dementia. An early detection of MCI is a crucial step for timely prevention and intervention. Recent studies have developed deep learning models to detect MCI and dementia using a bedside task like the classic clock drawing test (CDT). However, it remains a challenge to predict the early stage of the disease using the CDT data alone. Moreover, the state-of-the-art deep learning techniques still face black box challenges, making it questionable to implement them in a clinical setting. METHODS: We recruited 918 subjects from King Chulalongkorn Memorial Hospital (651 healthy subjects and 267 MCI patients). We propose a novel deep learning framework that incorporates data from the CDT, cube-copying, and trail-making tests. Soft label and self-attention were applied to improve the model performance and provide a visual explanation. The interpretability of the visualization of our model and the Grad-CAM approach were rated by experienced medical personnel and quantitatively evaluated using intersection over union (IoU) between the models' heat maps and the regions of interest. RESULTS: Rather than using a single CDT image in the baseline VGG16 model, using multiple drawing tasks as inputs into our proposed model with soft label significantly improves the classification performance between the healthy aging controls and the MCI patients. In particular, the classification accuracy increases from 0.75 (baseline model) to 0.81. The F1-score increases from 0.36 to 0.65, and the area under the receiver operating characteristic curve (AUC) increases from 0.74 to 0.84. Compared to the multi-input model that also offers interpretable visualization, i.e., Grad-CAM, our model receives higher interpretability scores given by experienced medical experts and higher IoUs. CONCLUSIONS: Our model achieves better classification performance at detecting MCI compared to the baseline model. In addition, the model provides visual explanations that are superior to those of the baseline model as quantitatively evaluated by experienced medical personnel. Thus, our work offers an interpretable machine learning model with high classification performance, both of which are crucial aspects of artificial intelligence in medical diagnosis.

One-step colorimetric isothermal detection of COVID-19 with AI-assisted automated result analysis: A platform model for future emerging point-of-care RNA/DNA disease diagnosis.

Colorimetric loop-mediated DNA isothermal amplification-based assays have gained momentum in the diagnosis of COVID-19 owing to their unmatched feasibility in low-resource settings. However, the vast majority of them are restricted to proprietary pH-sensitive dyes that limit downstream assay optimization or hinder efficient result interpretation. To address this problem, we developed a novel dual colorimetric RT-LAMP assay using in-house pH-dependent indicators to maximize the visual detection and assay simplicity, and further integrated it with the artificial intelligence (AI) operated tool (RT-LAMP-DETR) to enable a more precise and rapid result analysis in large scale testing. The dual assay leverages xylenol orange (XO) and a newly formulated lavender green (LG) dye for distinctive colorimetric readouts, which enhance the test accuracy when performed and analyzed simultaneously. Our RT-LAMP assay has a detection limit of 50 viral copies/reaction with the cycle threshold (Ct) value ≤ 39.7 ± 0.4 determined by the WHO-approved RT-qPCR assay. RT-LAMP-DETR exhibited a complete concordance with the results from naked-eye observation and RT-qPCR, achieving 100% sensitivity, specificity, and accuracy that altogether render it suitable for ultrasensitive point-of-care COVID-19 screening efforts. From the perspective of pandemic preparedness, our method offers a simpler, faster, and cheaper (∼$8/test) approach for COVID-19 testing and other emerging pathogens with respect to RT-qPCR.

Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM).

High-quality Quantitative Susceptibility Mapping (QSM) with Nonlinear Dipole Inversion (NDI) is developed with pre-determined regularization while matching the image quality of state-of-the-art reconstruction techniques and avoiding over-smoothing that these techniques often suffer from. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7 T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data.

Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction.

PURPOSE: To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging. METHODS: Single-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution. RESULTS: Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8- × 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T2 and T2 * parameter maps could be derived from an 8.3-second acquisition at 1 × 1 × 3-mm3 resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9- × 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network. CONCLUSION: Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.

Quantitative susceptibility mapping using deep neural network: QSMnet.

Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill-conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U-net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch-wise network training after doubling the training data using a model-based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications.

Dynamic 2D self-phase-map Nyquist ghost correction for simultaneous multi-slice echo planar imaging.

PURPOSE: To develop a reconstruction pipeline that intrinsically accounts for both simultaneous multislice echo planar imaging (SMS-EPI) reconstruction and dynamic slice-specific Nyquist ghosting correction in time-series data. METHODS: After 1D slice-group average phase correction, the separate polarity (i.e., even and odd echoes) SMS-EPI data were unaliased by slice GeneRalized Autocalibrating Partial Parallel Acquisition. Both the slice-unaliased even and odd echoes were jointly reconstructed using a model-based framework, extended for SMS-EPI reconstruction that estimates a 2D self-phase map, corrects dynamic slice-specific phase errors, and combines data from all coils and echoes to obtain the final images. RESULTS: The percentage ghost-to-signal ratios (%GSRs) and its temporal variations for MB3Ry 2 with a field of view/4 shift in a human brain obtained by the proposed dynamic 2D and standard 1D phase corrections were 1.37 ± 0.11 and 2.66 ± 0.16, respectively. Even with a large regularization parameter λ applied in the proposed reconstruction, the smoothing effect in fMRI activation maps was comparable to a very small Gaussian kernel size 1 × 1 × 1 mm3 . CONCLUSION: The proposed reconstruction pipeline reduced slice-specific phase errors in SMS-EPI, resulting in reduction of GSR. It is applicable for functional MRI studies because the smoothing effect caused by the regularization parameter selection can be minimal in a blood-oxygen-level-dependent activation map.

Model-based iterative reconstruction for single-shot EPI at 7T.

PURPOSE: To describe a model-based reconstruction strategy for single-shot echo planar imaging (EPI) that intrinsically accounts for k-space nonuniformity, Nyquist ghosting, and geometric distortions during rather than before or after image reconstruction. METHODS: Ramp sampling and inhomogeneous B0 field-induced distortion cause the EPI samples to lie on a non-Cartesian grid, thus requiring the nonuniform fast Fourier transform. Additionally, a 2D Nyquist ghost phase correction without the need for extra navigator acquisition is included in the proposed reconstruction. Coil compression is also incorporated to reduce the computational load. The proposed method is applied to phantom and human brain MRI data. RESULTS: The results demonstrate that Nyquist ghosting and geometric distortions are reduced by the proposed reconstruction. The proposed 2D phase correction is superior to a conventional 1D correction. The reductions of both artifacts lead to improved temporal signal-to-noise ratio (tSNR). The virtual coil results suggest that the processing time can be reduced by up to 75%, with a mean tSNR loss of only 3.2% when using 8-virtual instead of 32-physical coils for twofold undersampled data. CONCLUSION: The proposed reconstruction improves the quality (ghosting, geometry, and tSNR) of EPI without requiring calibration data for Nyquist ghost correction. Magn Reson Med 78:2250-2264, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Single-step quantitative susceptibility mapping with variational penalties.

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the gradient echo (GRE) phase signal through background phase removal and dipole inversion steps. Each of these steps typically requires the solution of an ill-posed inverse problem and thus necessitates additional regularization. Recently developed single-step QSM algorithms directly relate the unprocessed GRE phase to the unknown susceptibility distribution, thereby requiring the solution of a single inverse problem. In this work, we show that such a holistic approach provides susceptibility estimation with artifact mitigation and develop efficient algorithms that involve simple analytical solutions for all of the optimization steps. Our methods employ total variation (TV) and total generalized variation (TGV) to jointly perform the background removal and dipole inversion in a single step. Using multiple spherical mean value (SMV) kernels of varying radii permits high-fidelity background removal whilst retaining the phase information in the cortex. Using numerical simulations, we demonstrate that the proposed single-step methods reduce the reconstruction error by up to 66% relative to the multi-step methods that involve SMV background filtering with the same number of SMV kernels, followed by TV- or TGV-regularized dipole inversion. In vivo single-step experiments demonstrate a dramatic reduction in dipole streaking artifacts and improved homogeneity of image contrast. These acquisitions employ the rapid three-dimensional echo planar imaging (3D EPI) and Wave-CAIPI (controlled aliasing in parallel imaging) trajectories for signal-to-noise ratio-efficient whole-brain imaging. Herein, we also demonstrate the multi-echo capability of the Wave-CAIPI sequence for the first time, and introduce an automated, phase-sensitive coil sensitivity estimation scheme based on a 4-s calibration acquisition. Copyright © 2016 John Wiley & Sons, Ltd.

Vectorial total generalized variation for accelerated multi-channel multi-contrast MRI.

PURPOSE: To develop and implement an efficient reconstruction technique to improve accelerated multi-channel multi-contrast MRI. THEORY AND METHODS: The vectorial total generalized variation (TGV) operator is used as a regularizer for the sensitivity encoding (SENSE) technique to improve image quality of multi-channel multi-contrast MRI. The alternating direction method of multipliers (ADMM) is used to efficiently reconstruct the data. The performance of the proposed method (MC-TGV-SENSE) is assessed on two healthy volunteers at several acceleration factors. RESULTS: As demonstrated on the in vivo results, MC-TGV-SENSE had the lowest root-mean-square error (RMSE), highest structural similarity index, and best visual quality at all acceleration factors, compared to other methods under consideration. MC-TGV-SENSE yielded up to 17.3% relative RMSE reduction compared to the widely used total variation regularized SENSE. Furthermore, we observed that the reconstruction time of MC-TGV-SENSE is reduced by approximately a factor of two with comparable RMSEs by using the proposed ADMM-based algorithm as opposed to the more commonly used Chambolle-Pock primal-dual algorithm for the TGV-based reconstruction. CONCLUSION: MC-TGV-SENSE is a better alternative than the existing reconstruction methods for accelerated multi-channel multi-contrast MRI. The proposed method exploits shared information among the images (MC), mitigates staircasing artifacts (TGV), and uses the encoding power of multiple receiver coils (SENSE).

Accelerated 1 H MRSI using randomly undersampled spiral-based k-space trajectories.

PURPOSE: To develop and evaluate the performance of an acquisition and reconstruction method for accelerated MR spectroscopic imaging (MRSI) through undersampling of spiral trajectories. THEORY AND METHODS: A randomly undersampled spiral acquisition and sensitivity encoding (SENSE) with total variation (TV) regularization, random SENSE+TV, is developed and evaluated on single-slice numerical phantom, in vivo single-slice MRSI, and in vivo three-dimensional (3D)-MRSI at 3 Tesla. Random SENSE+TV was compared with five alternative methods for accelerated MRSI. RESULTS: For the in vivo single-slice MRSI, random SENSE+TV yields up to 2.7 and 2 times reduction in root-mean-square error (RMSE) of reconstructed N-acetyl aspartate (NAA), creatine, and choline maps, compared with the denoised fully sampled and uniformly undersampled SENSE+TV methods with the same acquisition time, respectively. For the in vivo 3D-MRSI, random SENSE+TV yields up to 1.6 times reduction in RMSE, compared with uniform SENSE+TV. Furthermore, by using random SENSE+TV, we have demonstrated on the in vivo single-slice and 3D-MRSI that acceleration factors of 4.5 and 4 are achievable with the same quality as the fully sampled data, as measured by RMSE of reconstructed NAA map, respectively. CONCLUSION: With the same scan time, random SENSE+TV yields lower RMSEs of metabolite maps than other methods evaluated. Random SENSE+TV achieves up to 4.5-fold acceleration with comparable data quality as the fully sampled acquisition. Magn Reson Med 74:13-24, 2015. © 2014 Wiley Periodicals, Inc.

Organ-targeted high-throughput in vivo biologics screen identifies materials for RNA delivery.

Therapies based on biologics involving delivery of proteins, DNA, and RNA are currently among the most promising approaches. However, although large combinatorial libraries of biologics and delivery vehicles can be readily synthesized, there are currently no means to rapidly characterize them in vivo using animal models. Here, we demonstrate high-throughput in vivo screening of biologics and delivery vehicles by automated delivery into target tissues of small vertebrates with developed organs. Individual zebrafish larvae are automatically oriented and immobilized within hydrogel droplets in an array format using a microfluidic system, and delivery vehicles are automatically microinjected to target organs with high repeatability and precision. We screened a library of lipid-like delivery vehicles for their ability to facilitate the expression of protein-encoding RNAs in the central nervous system. We discovered delivery vehicles that are effective in both larval zebrafish and rats. Our results showed that the in vivo zebrafish model can be significantly more predictive of both false positives and false negatives in mammals than in vitro mammalian cell culture assays. Our screening results also suggest certain structure-activity relationships, which can potentially be applied to design novel delivery vehicles.

Fast image reconstruction with L2-regularization.

PURPOSE: We introduce L2-regularized reconstruction algorithms with closed-form solutions that achieve dramatic computational speed-up relative to state of the art L1- and L2-based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction. MATERIALS AND METHODS: We compare fast L2-based methods to state of the art algorithms employing iterative L1- and L2-regularization in numerical phantom and in vivo data in three applications; (i) Fast Quantitative Susceptibility Mapping (QSM), (ii) Lipid artifact suppression in Magnetic Resonance Spectroscopic Imaging (MRSI), and (iii) Diffusion Spectrum Imaging (DSI). In all cases, proposed L2-based methods are compared with the state of the art algorithms, and two to three orders of magnitude speed up is demonstrated with similar reconstruction quality. RESULTS: The closed-form solution developed for regularized QSM allows processing of a three-dimensional volume under 5 s, the proposed lipid suppression algorithm takes under 1 s to reconstruct single-slice MRSI data, while the PCA based DSI algorithm estimates diffusion propagators from undersampled q-space for a single slice under 30 s, all running in Matlab using a standard workstation. CONCLUSION: For the applications considered herein, closed-form L2-regularization can be a faster alternative to its iterative counterpart or L1-based iterative algorithms, without compromising image quality.