DIMOND: DIffusion Model OptimizatioN with Deep Learning.

Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non-invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning-based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics-informed and self-supervised Deep learning entitled "DIMOND" is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self-supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications.

Individualized white matter connectivity of the articulatory pathway: An ultra-high field study.

In current sensorimotor theories pertaining to speech perception, there is a notable emphasis on the involvement of the articulatory-motor system in the processing of speech sounds. Using ultra-high field diffusion-weighted imaging at 7 Tesla, we visualized the white matter tracts connected to areas activated during a simple speech-sound production task in 18 healthy right-handed adults. Regions of interest for white matter tractography were individually determined through 7T functional MRI (fMRI) analyses, based on activations during silent vocalization tasks. These precentral seed regions, activated during the silent production of a lip-vowel sound, demonstrated anatomical connectivity with posterior superior temporal gyrus areas linked to the auditory perception of phonetic sounds. Our study provides a macrostructural foundation for understanding connections in speech production and underscores the central role of the articulatory motor system in speech perception. These findings highlight the value of ultra-high field 7T MR acquisition in unraveling the neural underpinnings of speech.

The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three-dimensional electron microscopy.

The increasing availability of high-performance gradient systems in human MRI scanners has generated great interest in diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon diameter in diffusion MRI is attained at strong diffusion weightings b , where the deviation from the expected 1 / b scaling in white matter yields a finite transverse diffusivity, which is then translated into an axon diameter estimate. While axons are usually modeled as perfectly straight, impermeable cylinders, local variations in diameter (caliber variation or beading) and direction (undulation) are known to influence axonal diameter estimates and have been observed in microscopy data of human axons. In this study, we performed Monte Carlo simulations of diffusion in axons reconstructed from three-dimensional electron microscopy of a human temporal lobe specimen using simulated sequence parameters matched to the maximal gradient strength of the next-generation Connectome 2.0 human MRI scanner ( ≲ 500 mT/m). We show that axon diameter estimation is accurate for nonbeaded, nonundulating fibers; however, in fibers with caliber variations and undulations, the axon diameter is heavily underestimated due to caliber variations, and this effect overshadows the known overestimation of the axon diameter due to undulations. This unexpected underestimation may originate from variations in the coarse-grained axial diffusivity due to caliber variations. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.

A Twin Study of Altered White Matter Heritability in Youth With Autism Spectrum Disorder.

OBJECTIVE: White matter alterations are frequently reported in autism spectrum disorder (ASD), yet the etiology is currently unknown. The objective of this investigation was to examine, for the first time, the impact of genetic and environmental factors on white matter microstructure in twins with ASD compared to control twins without ASD. METHOD: Diffusion-weighted MRIs were obtained from same-sex twin pairs (6-15 years of age) in which at least 1 twin was diagnosed with ASD or neither twin exhibited a history of neurological or psychiatric disorders. Fractional anisotropy (FA) and mean diffusivity (MD) were examined across different white matter tracts in the brain, and statistical and twin modeling were completed to assess the proportion of variation associated with additive genetic (A) and common/shared (C) or unique (E) environmental factors. We also developed a novel Twin-Pair Difference Score analysis method that produces quantitative estimates of the genetic and environmental contributions to shared covariance between different brain and behavioral traits. RESULTS: Good-quality data were available from 84 twin pairs, 50 ASD pairs (32 concordant for ASD [16 monozygotic; 16 dizygotic], 16 discordant for ASD [3 monozygotic; 13 dizygotic], and 2 pairs in which 1 twin had ASD and the other exhibited some subthreshold symptoms [1 monozygotic; 1 dizygotic]) and 34 control pairs (20 monozygotic; 14 dizygotic). Average FA and MD across the brain, respectively, were primarily genetically mediated in both control twins (A = 0.80, 95% CI [0.57, 1.02]; A = 0.80 [0.55, 1.04]) and twins concordant for having ASD (A = 0.71 [0.33, 1.09]; A = 0.84 [0.32,1.36]). However, there were also significant tract-specific differences between groups. For instance, genetic effects on commissural fibers were primarily associated with differences in general cognitive abilities and perhaps some diagnostic differences for ASD because Twin-Pair Difference-Score analysis indicated that genetic factors may have contributed to ∼40% to 50% of the covariation between IQ scores and FA of the corpus callosum. Conversely, the increased impact of environmental factors on some projection and association fibers were primarily associated with differences in symptom severity in twins with ASD; for example, our analyses suggested that unique environmental factors may have contributed to ∼10% to 20% of the covariation between autism-related symptom severity and FA of the cerebellar peduncles and external capsule. CONCLUSION: White matter alterations in youth with ASD are associated with both genetic contributions and potentially increased vulnerability or responsivity to environmental influences. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as living with a disability. The author list of this paper includes contributors from the location and/or community where the research was conducted and they participated in the data collection, design, analysis, and/or interpretation of the work.

Role of articulatory motor networks in perceptual categorization of speech signals: a 7T fMRI study.

Speech and language processing involve complex interactions between cortical areas necessary for articulatory movements and auditory perception and a range of areas through which these are connected and interact. Despite their fundamental importance, the precise mechanisms underlying these processes are not fully elucidated. We measured BOLD signals from normal hearing participants using high-field 7 Tesla fMRI with 1-mm isotropic voxel resolution. The subjects performed 2 speech perception tasks (discrimination and classification) and a speech production task during the scan. By employing univariate and multivariate pattern analyses, we identified the neural signatures associated with speech production and perception. The left precentral, premotor, and inferior frontal cortex regions showed significant activations that correlated with phoneme category variability during perceptual discrimination tasks. In addition, the perceived sound categories could be decoded from signals in a region of interest defined based on activation related to production task. The results support the hypothesis that articulatory motor networks in the left hemisphere, typically associated with speech production, may also play a critical role in the perceptual categorization of syllables. The study provides valuable insights into the intricate neural mechanisms that underlie speech processing.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Role of Articulatory Motor Networks in Perceptual Categorization of Speech Signals: A 7 T fMRI Study.

BACKGROUND: The association between brain regions involved in speech production and those that play a role in speech perception is not yet fully understood. We compared speech production related brain activity with activations resulting from perceptual categorization of syllables using high field 7 Tesla functional magnetic resonance imaging (fMRI) at 1-mm isotropic voxel resolution, enabling high localization accuracy compared to previous studies. METHODS: Blood oxygenation level dependent (BOLD) signals were obtained in 20 normal hearing subjects using a simultaneous multi-slice (SMS) 7T echo-planar imaging (EPI) acquisition with whole-head coverage and 1 mm isotropic resolution. In a speech production localizer task, subjects were asked to produce a silent lip-round vowel /u/ in response to the visual cue "U" or purse their lips when they saw the cue "P". In a phoneme discrimination task, subjects were presented with pairs of syllables, which were equiprobably identical or different along an 8-step continuum between the prototypic /ba/ and /da/ sounds. After the presentation of each stimulus pair, the subjects were asked to indicate whether the two syllables they heard were identical or different by pressing one of two buttons. In a phoneme classification task, the subjects heard only one syllable and asked to indicate whether it was /ba/ or /da/. RESULTS: Univariate fMRI analyses using a parametric modulation approach suggested that left motor, premotor, and frontal cortex BOLD activations correlate with phoneme category variability in the /ba/-/da/ discrimination task. In contrast, the variability related to acoustic features of the phonemes were the highest in the right primary auditory cortex. Our multivariate pattern analysis (MVPA) suggested that left precentral/inferior frontal cortex areas, which were associated with speech production according to the localizer task, play a role also in perceptual categorization of the syllables. CONCLUSIONS: The results support the hypothesis that articulatory motor networks in the left hemisphere that are activated during speech production could also have a role in perceptual categorization of syllables. Importantly, high voxel-resolution combined with advanced coil technology allowed us to pinpoint the exact brain regions involved in both perception and production tasks.

High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction.

PURPOSE: To develop a high-fidelity diffusion MRI acquisition and reconstruction framework with reduced echo-train-length for less T2* image blurring compared to typical highly accelerated echo-planar imaging (EPI) acquisitions at sub-millimeter isotropic resolution. METHODS: We first proposed a circular-EPI trajectory with partial Fourier sampling on both the readout and phase-encoding directions to minimize the echo-train-length and echo time. We then utilized this trajectory in an interleaved two-shot EPI acquisition with reversed phase-encoding polarity, to aid in the correction of off-resonance-induced image distortions and provide complementary k-space coverage in the missing partial Fourier regions. Using model-based reconstruction with structured low-rank constraint and smooth phase prior, we corrected the shot-to-shot phase variations across the two shots and recover the missing k-space data. Finally, we combined the proposed acquisition/reconstruction framework with an SNR-efficient RF-encoded simultaneous multi-slab technique, termed gSlider, to achieve high-fidelity 720 µm and 500 µm isotropic resolution in-vivo diffusion MRI. RESULTS: Both simulation and in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide distortion-corrected diffusion imaging at the mesoscale with markedly reduced T2*-blurring. The in-vivo results of 720 µm and 500 µm datasets show high-fidelity diffusion images with reduced image blurring and echo time using the proposed approaches. CONCLUSIONS: The proposed method provides high-quality distortion-corrected diffusion-weighted images with ∼40% reduction in the echo-train-length and T2* blurring at 500µm-isotropic-resolution compared to standard multi-shot EPI.

The influence of axonal beading and undulation on axonal diameter mapping.

We consider the effect of non-cylindrical axonal shape on axonal diameter mapping with diffusion MRI. Practical sensitivity to axon diameter is attained at strong diffusion weightings b, where the deviation from the 1/b scaling yields the finite transverse diffusivity, which is then translated into axon diameter. While axons are usually modeled as perfectly straight, impermeable cylinders, the local variations in diameter (caliber variation or beading) and direction (undulation) have been observed in microscopy data of human axons. Here we quantify the influence of cellular-level features such as caliber variation and undulation on axon diameter estimation. For that, we simulate the diffusion MRI signal in realistic axons segmented from 3-dimensional electron microscopy of a human brain sample. We then create artificial fibers with the same features and tune the amplitude of their caliber variations and undulations. Numerical simulations of diffusion in fibers with such tunable features show that caliber variations and undulations result in under- and over-estimation of axon diameters, correspondingly; this bias can be as large as 100%. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.

Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat).

Diffusion MRI is a useful neuroimaging tool for non-invasive mapping of human brain microstructure and structural connections. The analysis of diffusion MRI data often requires brain segmentation, including volumetric segmentation and cerebral cortical surfaces, from additional high-resolution T1-weighted (T1w) anatomical MRI data, which may be unacquired, corrupted by subject motion or hardware failure, or cannot be accurately co-registered to the diffusion data that are not corrected for susceptibility-induced geometric distortion. To address these challenges, this study proposes to synthesize high-quality T1w anatomical images directly from diffusion data using convolutional neural networks (CNNs) (entitled "DeepAnat"), including a U-Net and a hybrid generative adversarial network (GAN), and perform brain segmentation on synthesized T1w images or assist the co-registration using synthesized T1w images. The quantitative and systematic evaluations using data of 60 young subjects provided by the Human Connectome Project (HCP) show that the synthesized T1w images and results for brain segmentation and comprehensive diffusion analysis tasks are highly similar to those from native T1w data. The brain segmentation accuracy is slightly higher for the U-Net than the GAN. The efficacy of DeepAnat is further validated on a larger dataset of 300 more elderly subjects provided by the UK Biobank. Moreover, the U-Nets trained and validated on the HCP and UK Biobank data are shown to be highly generalizable to the diffusion data from Massachusetts General Hospital Connectome Diffusion Microstructure Dataset (MGH CDMD) acquired with different hardware systems and imaging protocols and therefore can be used directly without retraining or with fine-tuning for further improved performance. Finally, it is quantitatively demonstrated that the alignment between native T1w images and diffusion images uncorrected for geometric distortion assisted by synthesized T1w images substantially improves upon that by directly co-registering the diffusion and T1w images using the data of 20 subjects from MGH CDMD. In summary, our study demonstrates the benefits and practical feasibility of DeepAnat for assisting various diffusion MRI data analyses and supports its use in neuroscientific applications.

Axonal and myelin changes and their inter-relationship in the optic radiations in people with multiple sclerosis.

Background: The imaging g-ratio, estimated from axonal volume fraction (AVF) and myelin volume fraction (MVF), is a novel biomarker of microstructural tissue integrity in multiple sclerosis (MS). Objective: To assess axonal and myelin changes and their inter-relationship as measured by g-ratio in the optic radiations (OR) in people with MS (pwMS) with and without previous optic neuritis (ON) compared to healthy controls (HC). Methods: Thirty pwMS and 17 HCs were scanned on a 3Tesla Connectom scanner. AVF and MVF, derived from a multi-shell diffusion protocol and macromolecular tissue volume, respectively, were measured in normal-appearing white matter (NAWM) and lesions within the OR and used to calculate imaging g-ratio. Results: OR AVF and MVF were decreased in pwMS compared to HC, and in OR lesions compared to NAWM, whereas the g-ratio was not different. Compared to pwMS with previous ON, AVF and g-ratio tended to be higher in pwMS without prior ON. AVF and MVF, particularly in NAWM, were positively correlated with retinal thickness, which was more pronounced in pwMS with prior ON. Conclusion: Axonal measures reflect microstructural tissue damage in the OR, particularly in the setting of remote ON, and correlate with established metrics of visual health in MS.

3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolution T 2 * mapping.

PURPOSE: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. METHODS: 3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. RESULTS: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance image quality. For T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping, parameter values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. CONCLUSIONS: The proposed technique enables rapid 3D distortion-free high-resolution imaging and T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3T and 9 s on a 7T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping in 47 s at 1.1 × 1.1 × 1.0 mm3 resolution.

Changes in the Cerebello-Thalamo-Cortical Network After Magnetic Resonance-Guided Focused Ultrasound Thalamotomy.

Objective: In recent years, transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) has been established as a potential treatment option for movement disorders, including essential tremor (ET). So far, however, little is known about the impact of tcMRgFUS on structural connectivity. The objective of this study was to detect microstructural changes in tremor- and motor-related white matter tracts in ET patients treated with tcMRgFUS thalamotomy. Methods: Eleven patients diagnosed with ET were enrolled in this tcMRgFUS thalamotomy study. For each patient, 3 Tesla magnetic resonance imaging (3T MRI) including structural and diffusion MRI were acquired and the Clinical Rating Scale for Tremor was assessed before the procedure as well as 1 year after the treatment. Diffusion MRI tractography was performed to identify the cerebello-thalamo-cortical tract (CTCT), the medial lemniscus, and the corticospinal tract in both hemispheres on pre-treatment data. Pre-treatment tractography results were co-registered to post-treatment diffusion data. Diffusion tensor imaging (DTI) metrics, including fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD), were averaged across the tracts in the pre- and post-treatment data. Results: The mean value of tract-specific DTI metrics changed significantly within the thalamic lesion and in the CTCT on the treated side (p < 0.05). Changes of DTI-derived indices within the CTCT correlated well with lesion overlap (FA: r = -0.54, p = 0.04; MD: r = 0.57, p = 0.04); RD: r = 0.67, p = 0.036). Further, a trend was seen for the correlation between changes of DTI-derived indices within the CTCT and clinical improvement (FA: r = 0.58; p = 0.062; MD: r = -0.52, p = 0.64; RD: r = -0.61 p = 0.090). Conclusions: Microstructural changes were detected within the CTCT after tcMRgFUS, and these changes correlated well with lesion-tract overlap. Our results show that diffusion MRI is able to detect the microstructural effects of tcMRgFUS, thereby further elucidating the treatment mechanism, and ultimately to improve targeting prospectively. Impact statement The results of this study demonstrate microstructural changes within the cerebello-thalamo-cortical pathways 1 year after MR-guided focused ultrasound thalamotomy. Even more, microstructural changes within the cerebello-thalamo-cortical pathways correlated significantly with clinical outcome. These findings do not only highly emphasize the need of new targeting strategies for MR-guided focused ultrasound thalamotomy but also help to elucidate the treatment mechanism of it.

High-fidelity, high-spatial-resolution diffusion magnetic resonance imaging of ex vivo whole human brain at ultra-high gradient strength with structured low-rank echo-planar imaging ghost correction.

Diffusion magnetic resonance imaging (dMRI) of whole ex vivo human brain specimens enables three-dimensional (3D) mapping of structural connectivity at the mesoscopic scale, providing detailed evaluation of fiber architecture and tissue microstructure at a spatial resolution that is difficult to access in vivo. To account for the short T2 and low diffusivity of fixed tissue, ex vivo dMRI is often acquired using strong diffusion-sensitizing gradients and multishot/segmented 3D echo-planar imaging (EPI) sequences to achieve high spatial resolution. However, the combination of strong diffusion-sensitizing gradients and multishot/segmented EPI readout can result in pronounced ghosting artifacts incurred by nonlinear spatiotemporal variations in the magnetic field produced by eddy currents. Such ghosting artifacts cannot be corrected with conventional correction solutions and pose a significant roadblock to leveraging human MRI scanners with ultrahigh gradients for ex vivo whole-brain dMRI. Here, we show that ghosting-correction approaches that correct for either polarity-related ghosting or shot-to-shot variations in a separate manner are suboptimal for 3D multishot diffusion-weighted EPI experiments in fixed human brain specimens using strong diffusion-sensitizing gradients on the 3-T Connectom MRI scanner, resulting in orientationally biased dMRI estimates. We apply a recently developed advanced k-space reconstruction method based on structured low-rank matrix (SLM) modeling that handles both polarity-related ghosting and shot-to-shot variation simultaneously, to mitigate artifacts in high-angular resolution multishot dMRI data acquired in several fixed human brain specimens at 0.7-0.8-mm isotropic spatial resolution using b-values up to 10,000 s/mm2 and gradient strengths up to 280 mT/m. We demonstrate the improved mapping of diffusion tensor imaging and fiber orientation distribution functions in key neuroanatomical areas distributed across the whole brain using SLM-based EPI ghost correction compared with alternative techniques.

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.

Estimating axial diffusivity in the NODDI model.

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7µm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2-2.5µm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

Highly accelerated EPI with wave encoding and multi-shot simultaneous multislice imaging.

PURPOSE: To introduce wave-encoded acquisition and reconstruction techniques for highly accelerated EPI with reduced g-factor penalty and image artifacts. THEORY AND METHODS: Wave-EPI involves application of sinusoidal gradients during the EPI readout, which spreads the aliasing in all spatial directions, thereby taking better advantage of 3D coil sensitivity profiles. The amount of voxel spreading that can be achieved by the wave gradients during the short EPI readout period is constrained by the slew rate of the gradient coils and peripheral nerve stimulation monitor. We propose to use a "half-cycle" sinusoidal gradient to increase the amount of voxel spreading that can be achieved while respecting the slew and stimulation constraints. Extending wave-EPI to multi-shot acquisition minimizes geometric distortion and voxel blurring at high in-plane resolutions, while structured low-rank regularization mitigates shot-to-shot phase variations. To address gradient imperfections, we propose to use different point spread functions for the k-space lines with positive and negative polarities, which are calibrated with a FLEET-based reference scan. RESULTS: Wave-EPI enabled whole-brain single-shot gradient-echo (GE) and multi-shot spin-echo (SE) EPI acquisitions at high acceleration factors at 3T and was combined with g-Slider encoding to boost the SNR level in 1 mm isotropic diffusion imaging. Relative to blipped-CAIPI, wave-EPI reduced average and maximum g-factors by up to 1.21- and 1.37-fold at Rin × Rsms  = 3 × 3, respectively. CONCLUSION: Wave-EPI allows highly accelerated single- and multi-shot EPI with reduced g-factor and artifacts and may facilitate clinical and neuroscientific applications of EPI by improving the spatial and temporal resolution in functional and diffusion imaging.

Static and dynamic BOLD fMRI components along white matter fibre tracts and their dependence on the orientation of the local diffusion tensor axis relative to the B0-field.

Recent studies have reported functional MRI (fMRI) activation within cerebral white matter (WM) using blood-oxygenation-level-dependent (BOLD) contrast. Many blood vessels in WM run parallel to the fibre bundles, and other studies observed dependence of susceptibility contrast-based measures of blood volume on the local orientation of the fibre bundles relative to the magnetic field or B0 axis. Motivated by this, we characterized the dependence of gradient-echo BOLD fMRI on fibre orientation (estimated by the local diffusion tensor) relative to the B0 axis to test whether the alignment between bundles and vessels imparts an orientation dependence on resting-state BOLD fluctuations in the WM. We found that the baseline signal level of the T2*-weighted data is 11% higher in voxels containing fibres parallel to B0 than those containing perpendicular fibres, consistent with a static influence of either fibre or vessel orientation on local T2* values. We also found that BOLD fluctuations in most bundles exhibit orientation effects expected from oxygenation changes, with larger amplitudes from voxels containing perpendicular fibres. Different magnitudes of this orientation effect were observed across the major WM bundles, with inferior fasciculus, corpus callosum and optic radiation exhibiting 14-19% higher fluctuations in voxels containing perpendicular compared to parallel fibres.

Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage.

The aim of this study was to explore the application value of brain computed tomography (CT) images under intelligent segmentation algorithm and serological indexes in the early prediction of hematoma enlargement in patients with intracerebral hemorrhage (ICH). Fuzzy C-means (FCM) intelligence segmentation algorithm was introduced, and 150 patients with early ICH were selected as the research objects. Patient cerebral CT images were intelligently segmented to assess the diagnostic value of this algorithm. According to different hematoma volumes during CT examination, patients were divided into observation group (hematoma enlargement occurred, n = 48) and control group (no hematoma enlargement occurred, n = 102). The predicative value of hematoma enlargement after ICH was investigated by assessing CT image quality and measuring intracerebral edema, hematoma volume, and serological indicators of the patients of the two groups. The results demonstrated that the sensitivity, specificity, and accuracy of CT images processed by intelligence segmentation algorithm amounted to 0.894, 0.898, and 0.930, respectively. Besides, early edema enlargement and hematoma of patients in the observation group were more significant than those of patients in the control group. Relative edema volume was 0.912, which was apparently lower than that in the control group (1.017) (P < 0.05). In terms of CT signs of ICH patients, the incidence of blend sign, low density sign, and stroke of the observation group was evidently higher than those of the control group (P < 0.05). Besides, absolute lymphocyte count (ALC) and hemoglobin (HGB) concentration of the patients in the observation group were 6.23 × 109/L and 6.29 × 109/L, respectively, both of which were higher than those of the control group (6.08 × 109/L and 4.25 × 109/L). Neutrophil to lymphocyte ratio (NLR) was 0.99 × 109/L, which was apparently lower than that in the control group (1.43 × 109/L) (P < 0.05). To sum up, cerebral CT images processed by FCM algorithm showed good diagnostic effect on ICH and high clinical values in the early prediction of hematoma among ICH patients.

Metabolomics Analysis for Nitrite Degradation by the Metabolites of Limosilactobacillus fermentum RC4.

Nitrite (NIT), a commonly used food additive, especially in pickled and cured vegetables and meat products, might cause acute and chronic diseases. Fermentation with lactic acid bacteria (LAB) is an effective method for degrading NIT and improving the flavor of pickled and cured foods. In this study, Limosilactobacillus fermentum (L. fermentum) RC4 with a high NIT degradation ability was found to degrade NIT in a new manner when compared with reported enzymatic and acid degradation, namely, metabolite degradation during fermentation in MRS broth, which shows a synergistic effect with acid to increase NIT degradation. Liquid chromatography-mass spectrometry analysis identified 134 significantly different metabolites, of which 11 metabolites of L. fermentum RC4, namely, γ-aminobutyric acid (GABA), isocitric acid, D-glucose, 3-methylthiopropionic acid (MTP), N-formyl-L-methionine, dimethyl sulfone (MSM), D-ribose, mesaconate, trans-aconitic acid, L-lysine, and carnosine, showed significant NIT degradation effects compared with the control group (MRS broth). Verification experiments showed that adding the above 11 metabolites to 100 mg/L NIT and incubating for 24 h resulted in NIT degradation rates of 5.07%, 4.41%, 6.08%, 16.93%, 5.28%, 2.41%, 0.93%, 18.93%, 12.25%, 6.42%, and 3.21%, respectively. Among these, three metabolites, namely, mesaconate, MTP, and trans-aconitic acid, showed efficient NIT degradation abilities that might be related to the degradation mechanism involving decarboxylation reactions. This is the first systematic study of NIT degradation by LAB, resulting in the identification of a new metabolite degradation pathway and three efficient NIT degradation metabolites.