scholarly journals Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER)

2018 ◽  
Author(s):  
Wenchuan Wu ◽  
Peter J Koopmans ◽  
Jesper Andersson ◽  
Karla L Miller
Keyword(s):  
Diffusion Mri ◽  
Parallel Imaging ◽  
Angular Resolution ◽  
Pulse Sequences ◽  
Superior Performance ◽  
Reconstruction Method ◽  
Scan Time ◽  
Standard Pulse ◽  
A Minor

AbstractPurposeImage acceleration provides multiple benefits to diffusion MRI (dMRI), with in-plane acceleration reducing distortion and slice-wise acceleration increasing the number of directions that can be acquired in a given scan time. However, as acceleration factors increase, the reconstruction problem becomes ill-conditioned, particularly when using both in-plane acceleration and simultaneous multi-slice (SMS) imaging. In this work, we develop a novel reconstruction method for in-vivo MRI acquisition that provides acceleration beyond what conventional techniques can achieve.Theory and MethodsWe propose to constrain the reconstruction in the spatial (k) domain by incorporating information from the angular (q) domain. This approach exploits smoothness of the signal in q-space using Gaussian processes, as has previously been exploited in post-reconstruction analysis. We demonstrate in-plane acceleration exceeding the theoretical parallel imaging limits, and SMS combined with in-plane acceleration at a total factor of 12. This reconstruction is cast within a Bayesian framework that incorporates estimation of smoothness hyper-parameters, with no need for manual tuning.ResultsSimulations and in vivo results demonstrate superior performance of the proposed method compared with conventional parallel imaging methods. These improvements are achieved without loss of spatial or angular resolution and require only a minor modification to standard pulse sequences.ConclusionThe proposed method provides improvements over existing methods for diffusion acceleration, particularly for high SMS acceleration with in-plane undersampling.

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 Highly Accelerated Vessel-Selective Arterial Spin Labelling Angiography using Sparsity and Smoothness Constraints

2019 ◽  
Author(s):  
Sophie Schauman ◽  
Mark Chiew ◽  
Thomas W. Okell
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Arterial Spin Labeling ◽  
Parallel Imaging ◽  
Spin Labeling ◽  
Dominant Factor ◽  
Image Domain ◽  
In Vivo Experiments ◽  
Scan Time

AbstractPurposeTo demonstrate that vessel-selectivity in arterial spin labeling angiography can be achieved without any scan time penalty or noticeable loss of image quality compared to conventional arterial spin labeling angiography.MethodsSimulations on a numerical phantom were used to assess whether the increased sparsity of vessel-encoded angiograms compared to non-vessel-encoded angiograms alone can improve reconstruction results in a compressed sensing framework. Further simulations were performed to study whether the difference in relative sparsity between non-selective and vessel-selective dynamic angiograms were sufficient to achieve similar image quality at matched scan times in the presence of noise. Finally, data were acquired from 5 healthy volunteers to validate the technique in vivo. All data, both simulated and in vivo, were sampled in 2D using a golden angle radial trajectory and reconstructed by enforcing both image domain sparsity and temporal smoothness on the angiograms in a parallel imaging and compressed sensing framework.ResultsRelative sparsity was established as a primary factor governing the reconstruction fidelity. Using the proposed reconstruction scheme, differences between vessel-selective and non-selective angiography were negligible compared to the dominant factor of total scan time in both simulations and in vivo experiments at acceleration factors up to R = 34. The reconstruction quality was not heavily dependent on hand-tuning the parameters of the reconstruction.ConclusionThe increase in relative sparsity of vessel-selective angiograms compared to non-selective angiograms can be leveraged to achieve higher acceleration without loss of image quality, resulting in the acquisition of vessel-selective information at no scan time cost.

scholarly journals Motion Detection in Diffusion MRI via Online ODF Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Emmanuel Caruyer ◽  
Iman Aganj ◽  
Christophe Lenglet ◽  
Guillermo Sapiro ◽  
Rachid Deriche
Keyword(s):  
Kalman Filter ◽  
Diffusion Mri ◽  
Angular Resolution ◽  
Real Data ◽  
Orientation Distribution ◽  
High Angular Resolution ◽  
Ratio Test ◽  
Small Motion ◽  
Scan Time ◽  
Robustness To Noise

The acquisition of high angular resolution diffusion MRI is particularly long and subject motion can become an issue. The orientation distribution function (ODF) can be reconstructed online incrementally from diffusion-weighted MRI with a Kalman filtering framework. This online reconstruction provides real-time feedback throughout the acquisition process. In this article, the Kalman filter is first adapted to the reconstruction of the ODF in constant solid angle. Then, a method called STAR (STatistical Analysis of Residuals) is presented and applied to the online detection of motion in high angular resolution diffusion images. Compared to existing techniques, this method is image based and is built on top of a Kalman filter. Therefore, it introduces no additional scan time and does not require additional hardware. The performance of STAR is tested on simulated and real data and compared to the classical generalized likelihood ratio test. Successful detection of small motion is reported (rotation under 2°) with no delay and robustness to noise.

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 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 Toward personalised diffusion MRI in psychiatry: improved delineation of fibre bundles with the highest-ever angular resolution in vivo tractography

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Fraser Callaghan ◽  
Jerome J. Maller ◽  
Thomas Welton ◽  
Matthew J. Middione ◽  
Ajit Shankaranarayanan ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Angular Resolution ◽  
Fibre Bundles

scholarly journals Density-Weighted Concentric Ring Trajectory using simultaneous multi-band acceleration: 3D Metabolite-cycled Magnetic Resonance Spectroscopy Imaging at 3 T

2019 ◽  
Author(s):  
Pingyu Xia ◽  
Mark Chiew ◽  
Xiaopeng Zhou ◽  
Albert Thomas ◽  
Ulrike Dydak ◽  
...  
Keyword(s):  
Parallel Imaging ◽  
Structural Similarity ◽  
Single Band ◽  
Resonance Spectroscopy ◽  
Spectral Quality ◽  
Concentric Ring ◽  
In Vivo Measurements ◽  
Scan Time ◽  
Multi Band

AbstractSimultaneous multi-slice or multi-band parallel imaging is frequently used in fMRI to accelerate acquisition. In this study, we propose a new a non-water-suppressed multi-band MRSI using a density weighted concentric ring trajectory (DW-CRT) acquisition. The properties of multi-band acquisition scheme were compared against single-band acquisition in phantoms and in-vivo experiments. High-quality spectra were acquired for all simultaneously acquired three slices from a limited volume of interest with an in-plane resolution of 5 × 5 mm2 in 19.2 min. The achieved in vivo spectral quality of the multi-band method allowed the reliable metabolic mapping of four major metabolites with Cramér-Rao lower bounds below 50%, using a LCModel analysis. Metabolite maps and LCModel quality metrics were compared between multi-band and single-band acquisition schemes for both phantom and in-vivo measurements. Structural similarity (SSIM) index and conventional coefficient of variance analysis of both phantom and in-vivo measurements revealed a SSIM index higher than 0.83, corresponding to a coefficient of variance of less than 30%. These findings demonstrate that our multi-band DW-CRT MRSI demonstrate a multi-band acceleration, simultaneously acquired 3 slices, with no apparently loss in spectral quality, reducing scan time by a factor of 3 without any significant penalty.

scholarly journals Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison

2017 ◽  
Vol 30 (9) ◽  
pp. e3734 ◽  
Author(s):  
Uran Ferizi ◽  
Benoit Scherrer ◽  
Torben Schneider ◽  
Mohammad Alipoor ◽  
Odin Eufracio ◽  
...  
Keyword(s):  
Human Brain ◽  
Diffusion Mri ◽  
Group Comparison ◽  
Brain Connectome

scholarly journals Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Yin ◽  
Kai Yu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Prototype System ◽  
Reconstruction Method ◽  
Compressed Sampling ◽  
Data Volume ◽  
Simulation Results ◽  
Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

scholarly journals In vivo lung morphometry with hyperpolarized 3 He diffusion MRI: Reproducibility and the role of diffusion-sensitizing gradient direction

2014 ◽  
Vol 73 (3) ◽  
pp. 1252-1257 ◽  
Author(s):  
James D. Quirk ◽  
Yulin V. Chang ◽  
Dmitriy A. Yablonskiy
Keyword(s):  
Diffusion Mri ◽  
Gradient Direction ◽  
Lung Morphometry
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