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Improved visualization of intracranial distal arteries with multiple 2D slice dynamic ASL‐MRA and super‐resolution convolutional neural network
AbstractPurposeTo develop a novel framework to improve the visualization of distal arteries in arterial spin labeling (ASL) dynamic MRA.MethodsThe attenuation of ASL blood signal due to the repetitive application of excitation RF pulses was minimized by splitting the acquisition volume into multiple thin 2D (M2D) slices, thereby reducing the exposure of the arterial blood magnetization to RF pulses while it flows within the brain. To improve the degraded vessel visualization in the slice direction due to the limited minimum achievable 2D slice thickness, a super‐resolution (SR) convolutional neural network (CNN) was trained by using 3D time‐of‐flight (TOF)‐MRA images from a large public dataset. And then, we applied domain transfer from 3D TOF‐MRA to M2D ASL‐MRA, while avoiding acquiring a large number of ASL‐MRA data required for CNN training.ResultsCompared to the conventional 3D ASL‐MRA, far more distal arteries were visualized with higher signal intensity by using M2D ASL‐MRA. In general, however, the vessel visualization with a conventional interpolation was prone to be blurry and unclear due to the limited spatial resolution in the slice direction, particularly in small vessels. Application of CNN‐based SR transferred from 3D TOF‐MRA to M2D ASL‐MRA successfully addressed such a limitation and achieved clearer visualization of small vessels than conventional interpolation.ConclusionThis study demonstrated that the proposed framework provides improved visualization of distal arteries in later dynamic phases, which will particularly benefit the application of this approach in patients with cerebrovascular disease who have slow blood flow.
Structured low‐rank reconstruction for navigator‐free water/fat separated multi‐shot diffusion‐weightedEPI
AbstractPurposeMulti‐shot diffusion‐weighted EPI allows an increase in image resolution and reduced geometric distortions and can be combined with chemical‐shift encoding (Dixon) to separate water/fat signals. However, such approaches suffer from physiological motion‐induced shot‐to‐shot phase variations. In this work, a structured low‐rank‐based navigator‐free algorithm is proposed to address the challenge of simultaneously separating water/fat signals and correcting for physiological motion‐induced shot‐to‐shot phase variations in multi‐shot EPI‐based diffusion‐weighted MRI.Theory and MethodsWe propose an iterative, model‐based reconstruction pipeline that applies structured low‐rank regularization to estimate and eliminate the shot‐to‐shot phase variations in a data‐driven way, while separating water/fat images. The algorithm is tested in different anatomies, including head–neck, knee, brain, and prostate. The performance is validated in simulations and in‐vivo experiments in comparison to existing approaches.ResultsIn‐vivo experiments and simulations demonstrated the effectiveness of the proposed algorithm compared to extra‐navigated and an alternative self‐navigation approach. The proposed algorithm demonstrates the capability to reconstruct in the multi‐shot/Dixon hybrid space domain under‐sampled datasets, using the same number of acquired EPI shots compared to conventional fat‐suppression techniques but eliminating fat signals through chemical‐shift encoding. In addition, partial Fourier reconstruction can also be achieved by using the concept of virtual conjugate coils in conjunction with the proposed algorithm.ConclusionThe proposed algorithm effectively eliminates the shot‐to‐shot phase variations and separates water/fat images, making it a promising solution for future DWI on different anatomies.
High‐fidelity fast volumetric brain MRI using synergistic wave‐controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN)
Abstract Purpose The goal of this study is to leverage an advanced fast imaging technique, wave‐controlled aliasing in parallel imaging (Wave‐CAIPI), and a generative adversarial network (GAN) for denoising to achieve accelerated high‐quality high‐signal‐to‐noise‐ratio (SNR) volumetric magnetic resonance imaging (MRI). Methods Three‐dimensional (3D) T 2 ‐weighted fluid‐attenuated inversion recovery (FLAIR) image data were acquired on 33 multiple sclerosis (MS) patients using a prototype Wave‐CAIPI sequence (acceleration factor R = 3 × 2, 2.75 min) and a standard T 2 ‐sampling perfection with application‐optimized contrasts by using flip angle evolution (SPACE) FLAIR sequence ( R = 2, 7.25 min). A hybrid denoising GAN entitled “HDnGAN” consisting of a 3D generator and a 2D discriminator was proposed to denoise highly accelerated Wave‐CAIPI images. HDnGAN benefits from the improved image synthesis performance provided by the 3D generator and increased training samples from a limited number of patients for training the 2D discriminator. HDnGAN was trained and validated on data from 25 MS patients with the standard FLAIR images as the target and evaluated on data from eight MS patients not seen during training. HDnGAN was compared to other denoising methods including adaptive optimized nonlocal means (AONLM), block matching with 4D filtering (BM4D), modified U‐Net (MU‐Net), and 3D GAN in qualitative and quantitative analysis of output images using the mean squared error (MSE) and Visual Geometry Group (VGG) perceptual loss compared to standard FLAIR images, and a reader assessment by two neuroradiologists regarding sharpness, SNR, lesion conspicuity, and overall quality. Finally, the performance of these denoising methods was compared at higher noise levels using simulated data with added Rician noise. Results HDnGAN effectively denoised low‐SNR Wave‐CAIPI images with sharpness and rich textural details, which could be adjusted by controlling the contribution of the adversarial loss to the total loss when training the generator. Quantitatively, HDnGAN ( λ = 10 –3 ) achieved low MSE and the lowest VGG perceptual loss. The reader study showed that HDnGAN ( λ = 10 –3 ) significantly improved the SNR of Wave‐CAIPI images ( p < 0.001), outperformed AONLM ( p = 0.015), BM4D ( p < 0.001), MU‐Net ( p < 0.001), and 3D GAN ( λ = 10 –3 ) ( p < 0.001) regarding image sharpness, and outperformed MU‐Net ( p < 0.001) and 3D GAN ( λ = 10 –3 ) ( p = 0.001) regarding lesion conspicuity. The overall quality score of HDnGAN ( λ = 10 –3 ) (4.25 ± 0.43) was significantly higher than those from Wave‐CAIPI (3.69 ± 0.46, p = 0.003), BM4D (3.50 ± 0.71, p = 0.001), MU‐Net (3.25 ± 0.75, p < 0.001), and 3D GAN ( λ = 10 –3 ) (3.50 ± 0.50, p < 0.001), with no significant difference compared to standard FLAIR images (4.38 ± 0.48, p = 0.333). The advantages of HDnGAN over other methods were more obvious at higher noise levels. Conclusion HDnGAN provides robust and feasible denoising while preserving rich textural detail in empirical volumetric MRI data. Our study using empirical patient data and systematic evaluation supports the use of HDnGAN in combination with modern fast imaging techniques such as Wave‐CAIPI to achieve high‐fidelity fast volumetric MRI and represents an important step to the clinical translation of GANs.
Artificial intelligence for neuro MRI acquisition: a review.
OBJECT: To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts. MATERIALS AND METHODS: A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods. RESULTS: The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency. DISCUSSION: The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.
Effects of diffusion MRI spatial resolution on human brain short-range association fiber reconstruction and structural connectivity estimation.
Short-range association fibers (SAFs) are critical for cortical communications but are often underestimated in conventional resolution diffusion magnetic resonance imaging (dMRI) since they locate within a ~1.5 mm thin layer of superficial white matter. With the advent of high-resolution diffusion imaging techniques, this study evaluated the effects of image spatial resolution on SAF reconstruction using two datasets: (1) prospectively acquired dMRI data from 20 healthy subjects, each scanned at 3 resolutions (i.e., 2, 1.5, and 0.96 mm iso.), and (2) retrospectively down-sampled dMRI data from the Human Connectome Project dataset, as well as 20 representative MRtrix3-based tractography pipelines. It was found that lower resolution degraded superficial white matter model fitting, lowered the SAF streamline counts, and reduced global and regional short-range connectivity fraction (SCF), defined as the fraction of SAF connections among all association fiber connections, across all tested methods. Temporal lobe cortical regions exhibited the greatest SCF declines at lower resolutions. Tractography methods differed in resolution sensitivity, with diffusion tensor imaging (DTI)-based single-tissue single-fiber tractography showing greater decreases in SCF than constrained spherical deconvolution (CSD)-based multi-tissue multi-fiber tractography at lower resolutions. Probabilistic, anatomically constrained tractography combined with spherical-deconvolution informed filtering of tractograms was more robust to decreases in resolution. Up-sampling to a nominally higher resolution partially improved model fitting and SCF accuracy across the evaluated pipelines, with the greatest effect observed for DTI. Using the 0.96 mm iso. gSlider data and optimized tractography pipelines from this study, we constructed the first human brain atlas of RSCF. In summary, this study provides a systematic and quantitative evaluation using MRtrix3 of how spatial resolution, fiber models, and tracking methodologies affect SAF reconstruction and structural connectivity estimation, serving as a reference framework for methodological choices. These advances may enhance the characterization of both healthy and diseased human brains across a wide range of neuroscientific and clinical applications.
Enhance the image: Super resolution in MRI
By giving an interdisciplinary presentation and discussion on the obstacles and possible solutions for the clinical translation of machine learning methods, this book enables the evolution of machine learning in medical imaging for the next ...
DIMOND: DIffusion Model OptimizatioN with Deep Learning
AbstractDiffusion 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.
Quantifying axonal features of human superficial white matter from three-dimensional multibeam serial electron microscopy data assisted by deep learning.
Short-range association fibers located in the superficial white matter play an important role in mediating higher-order cognitive function in humans. Detailed morphological characterization of short-range association fibers at the microscopic level promises to yield important insights into the axonal features driving cortico-cortical connectivity in the human brain yet has been difficult to achieve to date due to the challenges of imaging at nanometer-scale resolution over large tissue volumes. This work presents results from multi-beam scanning electron microscopy (EM) data acquired at 4 × 4 × 33 nm3 resolution in a volume of human superficial white matter measuring 200 × 200 × 112 μm3, leveraging automated analysis methods. Myelin and myelinated axons were automatically segmented using deep convolutional neural networks (CNNs), assisted by transfer learning and dropout regularization techniques. A total of 128,285 myelinated axons were segmented, of which 70,321 and 2102 were longer than 10 and 100 μm, respectively. Marked local variations in diameter (i.e., beading) and direction (i.e., undulation) were observed along the length of individual axons. Myelinated axons longer than 10 μm had inner diameters around 0.5 µm, outer diameters around 1 µm, and g-ratios around 0.5. This work fills a gap in knowledge of axonal morphometry in the superficial white matter and provides a large 3D human EM dataset and accurate segmentation results for a variety of future studies in different fields.
Sampling strategies and integrated reconstruction for reducing distortion and boundary slice aliasing in high‐resolution 3D diffusion MRI
Purpose To develop a new method for high‐fidelity, high‐resolution 3D multi‐slab diffusion MRI with minimal distortion and boundary slice aliasing. Methods Our method modifies 3D multi‐slab imaging to integrate blip‐reversed acquisitions for distortion correction and oversampling in the slice direction (k z ) for reducing boundary slice aliasing. Our aim is to achieve robust acceleration to keep the scan time the same as conventional 3D multi‐slab acquisitions, in which data are acquired with a single direction of blip traversal and without k z ‐oversampling. We employ a two‐stage reconstruction. In the first stage, the blip‐up/down images are respectively reconstructed and analyzed to produce a field map for each diffusion direction. In the second stage, the blip‐reversed data and the field map are incorporated into a joint reconstruction to produce images that are corrected for distortion and boundary slice aliasing. Results We conducted experiments at 7T in six healthy subjects. Stage 1 reconstruction produces images from highly under‐sampled data ( R = 7.2) with sufficient quality to provide accurate field map estimation. Stage 2 joint reconstruction substantially reduces distortion artifacts with comparable quality to fully‐sampled blip‐reversed results (2.4× scan time). Whole‐brain in‐vivo results acquired at 1.22 mm and 1.05 mm isotropic resolutions demonstrate improved anatomical fidelity compared to conventional 3D multi‐slab imaging. Data demonstrate good reliability and reproducibility of the proposed method over multiple subjects. Conclusion The proposed acquisition and reconstruction framework provide major reductions in distortion and boundary slice aliasing for 3D multi‐slab diffusion MRI without increasing the scan time, which can potentially produce high‐quality, high‐resolution diffusion MRI.
Short- and long-term modulation of rat prefrontal cortical activity following single doses of psilocybin
Abstract We quantify cellular- and circuit-resolution neural network dynamics following therapeutically relevant doses of the psychedelic psilocybin. Using chronically implanted Neuropixels probes, we recorded local field potentials (LFP) alongside action potentials from hundreds of neurons spanning infralimbic, prelimbic and cingulate subregions of the medial prefrontal cortex of freely-behaving adult rats. Psilocybin (0.3 mg/kg or 1 mg/kg i.p.) unmasked 100 Hz high frequency oscillations that were most pronounced within the infralimbic cortex, persisted for approximately 1 h post-injection and were accompanied by decreased net neuronal firing rates and reduced spike-train complexity. These acute effects were more prominent during resting behaviour than during performance of a sustained attention task. LFP 1-, 2- and 6-days post-psilocybin showed gradually-emerging increases in beta and low-gamma (20–60 Hz) power, specific to the infralimbic cortex. These findings reveal features of psychedelic action not readily detectable in human brain imaging, implicating infralimbic network oscillations as potential biomarkers of psychedelic-induced network plasticity over multi-day timescales.