scholarly journals In vivo human whole-brain Connectom diffusion MRI dataset at 760 μm isotropic resolution

2020 ◽  
Author(s):  
Fuyixue Wang ◽  
Zijing Dong ◽  
Qiyuan Tian ◽  
Congyu Liao ◽  
Qiuyun Fan ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Parallel Imaging ◽  
Brain Structures ◽  
Test Bed ◽  
Whole Brain ◽  
Isotropic Resolution ◽  
Imaging Reconstruction ◽  
Further Development ◽  
Array Coil

AbstractWe present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy subject. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.

scholarly journals In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fuyixue Wang ◽  
Zijing Dong ◽  
Qiyuan Tian ◽  
Congyu Liao ◽  
Qiuyun Fan ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Parallel Imaging ◽  
Brain Structures ◽  
Test Bed ◽  
Whole Brain ◽  
Isotropic Resolution ◽  
Imaging Reconstruction ◽  
Further Development ◽  
Array Coil

AbstractWe present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.

scholarly journals Whole-brain 3D MR fingerprinting brain imaging: clinical validation and feasibility to patients with meningioma

2021 ◽  
Author(s):  
Thomaz R. Mostardeiro ◽  
Ananya Panda ◽  
Robert J. Witte ◽  
Norbert G. Campeau ◽  
Kiaran P. McGee ◽  
...  
Keyword(s):  
White Matter ◽  
Statistical Significance ◽  
Relaxation Times ◽  
Brain Structures ◽  
Centrum Semiovale ◽  
In Vivo Evaluation ◽  
Whole Brain ◽  
Mean Values ◽  
Caudate Head

Abstract Purpose MR fingerprinting (MRF) is a MR technique that allows assessment of tissue relaxation times. The purpose of this study is to evaluate the clinical application of this technique in patients with meningioma. Materials and methods A whole-brain 3D isotropic 1mm3 acquisition under a 3.0T field strength was used to obtain MRF T1 and T2-based relaxometry values in 4:38 s. The accuracy of values was quantified by scanning a quantitative MR relaxometry phantom. In vivo evaluation was performed by applying the sequence to 20 subjects with 25 meningiomas. Regions of interest included the meningioma, caudate head, centrum semiovale, contralateral white matter and thalamus. For both phantom and subjects, mean values of both T1 and T2 estimates were obtained. Statistical significance of differences in mean values between the meningioma and other brain structures was tested using a Friedman’s ANOVA test. Results MR fingerprinting phantom data demonstrated a linear relationship between measured and reference relaxometry estimates for both T1 (r2 = 0.99) and T2 (r2 = 0.97). MRF T1 relaxation times were longer in meningioma (mean ± SD 1429 ± 202 ms) compared to thalamus (mean ± SD 1054 ± 58 ms; p = 0.004), centrum semiovale (mean ± SD 825 ± 42 ms; p < 0.001) and contralateral white matter (mean ± SD 799 ± 40 ms; p < 0.001). MRF T2 relaxation times were longer for meningioma (mean ± SD 69 ± 27 ms) as compared to thalamus (mean ± SD 27 ± 3 ms; p < 0.001), caudate head (mean ± SD 39 ± 5 ms; p < 0.001) and contralateral white matter (mean ± SD 35 ± 4 ms; p < 0.001) Conclusions Phantom measurements indicate that the proposed 3D-MRF sequence relaxometry estimations are valid and reproducible. For in vivo, entire brain coverage was obtained in clinically feasible time and allows quantitative assessment of meningioma in clinical practice.

scholarly journals Whole-Brain High-Resolution Metabolite Mapping with 3D Compressed-Sensing-SENSE-LowRank 1H FID-MRSI

2020 ◽  
Author(s):  
Antoine Klauser ◽  
Paul Klauser ◽  
Frédéric Grouiller ◽  
Sebastien Courvoisier ◽  
Francois Lazeyras
Keyword(s):  
High Resolution ◽  
Brain Regions ◽  
Low Rank ◽  
Acquisition Time ◽  
Resonance Spectroscopy ◽  
Brain Anatomy ◽  
Reconstruction Technique ◽  
Whole Brain ◽  
Isotropic Resolution

There is a growing interest of the neuroscience community to map the distribution of brain metabolites in vivo. Magnetic resonance spectroscopy imaging (MRSI) is often limited by either a poor spatial resolution and/or a long acquisition time which severely limits its applications for clinical or research purposes. We developed a novel acquisition-reconstruction technique combining fast 1H-FID-MRSI sequence accelerated by random k-space undersampling and a low-rank and total-generalized variation (TGV) constrained model. This framework was applied to the brain of four healthy volunteers. Following 20 min acquisition, reconstruction and quantification, the resulting metabolic maps with a 5 mm isotropic resolution reflected the detailed neurochemical composition of all brain regions and revealed part of the underlying brain anatomy. Contrasts and features from the 3D metabolite distributions were in agreement with the literature and consistent across the four subjects. The successful combination of the 3D 1H-FID-MRSI with a constrained reconstruction enables the detailed mapping of metabolite concentrations at high-resolution in the whole brain and with an acquisition time that is compatible with clinical or research settings.

scholarly journals An integrated RF-receive/B0-shim array coil boosts performance of whole-brain MR spectroscopic imaging at 7 T

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Morteza Esmaeili ◽  
Jason Stockmann ◽  
Bernhard Strasser ◽  
Nicolas Arango ◽  
Bijaya Thapa ◽  
...  
Keyword(s):  
High Field ◽  
Human Subjects ◽  
Spectroscopic Imaging ◽  
Metabolic Imaging ◽  
Ultra High Field ◽  
Whole Brain ◽  
Metabolite Quantification ◽  
Noise Ratio ◽  
Array Coil

Abstract Metabolic imaging of the human brain by in-vivo magnetic resonance spectroscopic imaging (MRSI) can non-invasively probe neurochemistry in healthy and disease conditions. MRSI at ultra-high field (≥ 7 T) provides increased sensitivity for fast high-resolution metabolic imaging, but comes with technical challenges due to non-uniform B0 field. Here, we show that an integrated RF-receive/B0-shim (AC/DC) array coil can be used to mitigate 7 T B0 inhomogeneity, which improves spectral quality and metabolite quantification over a whole-brain slab. Our results from simulations, phantoms, healthy and brain tumor human subjects indicate improvements of global B0 homogeneity by 55%, narrower spectral linewidth by 29%, higher signal-to-noise ratio by 31%, more precise metabolite quantification by 22%, and an increase by 21% of the brain volume that can be reliably analyzed. AC/DC shimming provide the highest correlation (R2 = 0.98, P = 0.001) with ground-truth values for metabolite concentration. Clinical translation of AC/DC and MRSI is demonstrated in a patient with mutant-IDH1 glioma where it enables imaging of D-2-hydroxyglutarate oncometabolite with a 2.8-fold increase in contrast-to-noise ratio at higher resolution and more brain coverage compared to previous 7 T studies. Hence, AC/DC technology may help ultra-high field MRSI become more feasible to take advantage of higher signal/contrast-to-noise in clinical applications.

scholarly journals High‐fidelity, accelerated whole‐brain submillimeter in vivo diffusion MRI using gSlider‐spherical ridgelets (gSlider‐SR)

2020 ◽  
Vol 84 (4) ◽  
pp. 1781-1795 ◽  
Author(s):  
Gabriel Ramos‐Llordén ◽  
Lipeng Ning ◽  
Congyu Liao ◽  
Rinat Mukhometzianov ◽  
Oleg Michailovich ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
High Fidelity ◽  
Whole Brain

scholarly journals Metabolic connectomics targeting brain pathology in dementia with Lewy bodies

2016 ◽  
Vol 37 (4) ◽  
pp. 1311-1325 ◽  
Author(s):  
Silvia P Caminiti ◽  
Marco Tettamanti ◽  
Arianna Sala ◽  
Luca Presotto ◽  
Sandro Iannaccone ◽  
...  
Keyword(s):  
Dementia With Lewy Bodies ◽  
Estimation Method ◽  
Lewy Bodies ◽  
Occipital Cortex ◽  
Brain Structures ◽  
Brain Regions ◽  
Normal Brain ◽  
Whole Brain ◽  
Metabolic Connectivity

Dementia with Lewy bodies is characterized by α-synuclein accumulation and degeneration of dopaminergic and cholinergic pathways. To gain an overview of brain systems affected by neurodegeneration, we characterized the [18F]FDG-PET metabolic connectivity in 42 dementia with Lewy bodies patients, as compared to 42 healthy controls, using sparse inverse covariance estimation method and graph theory. We performed whole-brain and anatomically driven analyses, targeting cholinergic and dopaminergic pathways, and the α-synuclein spreading. The first revealed substantial alterations in connectivity indexes, brain modularity, and hubs configuration. Namely, decreases in local metabolic connectivity within occipital cortex, thalamus, and cerebellum, and increases within frontal, temporal, parietal, and basal ganglia regions. There were also long-range disconnections among these brain regions, all supporting a disruption of the functional hierarchy characterizing the normal brain. The anatomically driven analysis revealed alterations within brain structures early affected by α-synuclein pathology, supporting Braak’s early pathological staging in dementia with Lewy bodies. The dopaminergic striato-cortical pathway was severely affected, as well as the cholinergic networks, with an extensive decrease in connectivity in Ch1-Ch2, Ch5-Ch6 networks, and the lateral Ch4 capsular network significantly towards the occipital cortex. These altered patterns of metabolic connectivity unveil a new in vivo scenario for dementia with Lewy bodies underlying pathology in terms of changes in whole-brain metabolic connectivity, spreading of α-synuclein, and neurotransmission impairment.

scholarly journals Navigator-Free Submillimeter Diffusion Imaging Using Multishot-Encoded Simultaneous Multi-Slice (MUSIUM)

2020 ◽  
Author(s):  
Wei-Tang Chang ◽  
Khoi Huynh ◽  
Pew-Thian Yap ◽  
Weili Lin
Keyword(s):  
Signal To Noise Ratio ◽  
Diffusion Imaging ◽  
Brain Structures ◽  
Peak Amplitude ◽  
Acquisition Time ◽  
Rf Power ◽  
Isotropic Resolution ◽  
Low Snr ◽  
Scan Time

Abstract The ability to achieve submillimter isotropic resolution diffusion MR imaging (dMRI) is critically important to study fine-scale brain structures. One of the major challenges in submillimeter dMRI is the inherently low signal-to-noise ratio (SNR). While approaches capable of mitigating the low SNR have been proposed, namely simultaneous multi-slab (SMSlab) and generalized slice dithered enhanced resolution with simultaneous multislice (gSlider-SMS), limitations are associated with these approaches. The SMSlab sequences suffer from the slab boundary artifacts and require additional navigators for phase estimation. On the other hand, gSlider sequences require relatively high RF power and peak amplitude, which increase the SAR and complicate the RF excitation. In this work, we developed a navigator-free multishot-encoded simultaneous multi-slice (MUSIUM) imaging approach, achieving enhanced SNR, low RF power and peak amplitude, and being free from slab boundary artifacts. The dMRI with ultrahigh resolution (0.86 mm isotropic), whole brain coverage and ~12.5 minute acquisition time were achieved, revealing detailed structures at cortical and white matter areas. The simulated and in vivo results also demonstrated that the MUSIUM imaging was minimally affected by the motion. Taken together, the MUSIUM imaging is a promising approach to achieve submillimeter diffusion imaging on 3T scanner within clinically feasible scan time.

scholarly journals Automated characterization of noise distributions in diffusion MRI data

2019 ◽  
Author(s):  
Samuel St-Jean ◽  
Alberto De Luca ◽  
Chantal M. W. Tax ◽  
Max A. Viergever ◽  
Alexander Leemans
Keyword(s):  
Bias Correction ◽  
Diffusion Mri ◽  
Parallel Imaging ◽  
Accurate Estimation ◽  
Single Subject ◽  
Noise Distribution ◽  
Acquisition Process ◽  
Reconstruction Methods ◽  
Imaging Results

AbstractKnowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g., coils sensitivity maps or reconstruction coeffcients), which is usually not available. We introduce two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate noise distributions as they explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two diffusion weightings with SENSE or GRAPPA reconstruction and 8, 12 or 32 receiver coils. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired on a 3T Philips scanner along with noise-only measurements. Finally, experiments on freely available datasets from a single subject acquired on a 3T GE scanner are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset acquired on a 3T Siemens scanner. A generalized version of the bias correction framework for non integer degrees of freedom is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results on the acquired datasets showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results for the bias correction and denoising task show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps.

scholarly journals Oscillating-gradient diffusion magnetic resonance imaging detects acute subcellular structural changes in the mouse forebrain after neonatal hypoxia-ischemia

2018 ◽  
Vol 39 (7) ◽  
pp. 1336-1348 ◽  
Author(s):  
Dan Wu ◽  
Lee J Martin ◽  
Frances J Northington ◽  
Jiangyang Zhang
Keyword(s):  
Diffusion Mri ◽  
Structural Changes ◽  
Spatial Scales ◽  
Brain Structures ◽  
Contralateral Side ◽  
Neonatal Hypoxia ◽  
Gradient Diffusion ◽  
Hypoxia Ischemia ◽  
Apparent Diffusion

The recently developed oscillating-gradient diffusion MRI (OG-dMRI) technique extends our ability to examine brain structures at different spatial scales. In this study, we investigated the sensitivity of OG-dMRI in detecting cellular and subcellular structural changes in a mouse model of neonatal hypoxia ischemia (HI). Neonatal mice received unilateral HI injury or sham injury at postnatal day 10, followed by in vivo T2-weighted and diffusion MRI of the brains at 3–6 h and 24 h after HI. Apparent diffusion coefficient (ADC) maps were acquired using conventional pulsed-gradient dMRI (PG-dMRI) and OG-dMRI with oscillating frequencies from 50 to 200 Hz. Pathology at cellular and subcellular levels was evaluated using neuronal, glial, and mitochondrial markers. We found significantly higher rates of ADC increase with oscillating frequencies (Δ fADC) in the ipsilateral edema region, compared to the contralateral side, starting as early as 3 h after HI. Even in injured regions that showed no apparent change in PG-ADC or pseudo-normalized PG-ADC measurements, Δ fADC remained significantly elevated. Histopathology showed swelling of sub-cellular structures in these regions with no apparent whole-cell level change. These results suggest that OG-dMRI is sensitive to subcellular structural changes in the brain after HI and is less susceptible to pseudo-normalization than PG-dMRI.

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