Wenchuan Wu

High-resolution diffusion MRI at 7T using a three-dimensional multi-slab acquisition.

High-resolution diffusion MRI can provide the ability to resolve small brain structures, enabling investigations of detailed white matter architecture. A major challenge for in vivo high-resolution diffusion MRI is the low signal-to-noise ratio. In this work, we combine two highly compatible methods, ultra-high field and three-dimensional multi-slab acquisition to improve the SNR of high-resolution diffusion MRI. As each kz plane is encoded using a single-shot echo planar readout, scan speeds of the proposed technique are similar to the commonly used two-dimensional diffusion MRI. In-plane parallel acceleration is applied to reduce image distortions. To reduce the sensitivity of auto-calibration signal data to subject motion and respiration, several new adaptions of the fast low angle excitation echo-planar technique (FLEET) that are suitable for 3D multi-slab echo planar imaging are proposed and evaluated. A modified reconstruction scheme is proposed for auto-calibration with the most robust method, Slice-FLEET acquisition, to make it compatible with navigator correction of motion induced phase errors. Slab boundary artefacts are corrected using the nonlinear slab profile encoding method recently proposed by our group. In vivo results demonstrate that using 7T and three-dimensional multi-slab acquisition with improved auto-calibration signal acquisition and nonlinear slab boundary artefacts correction, high-quality diffusion MRI data with ~1 mm isotropic resolution can be achieved.

Parallel imaging and compressed sensing combined framework for accelerating high-resolution diffusion tensor imaging using inter-image correlation.

Increasing acquisition efficiency is always a challenge in high‐resolution diffusion tensor imaging (DTI), which has low signal‐to‐noise ratio and is sensitive to reconstruction artifacts. In this study, a parallel imaging (PI) and compressed sensing (CS) combined framework is proposed, which features motion error correction, PI calibration, and sparsity model using inter‐image correlation tailored for high‐resolution DTI.

PROMISE: Parallel-imaging and compressed-sensing reconstruction of multicontrast imaging using SharablE information

A typical clinical MR examination includes multiple scans to acquire images with different contrasts for complementary diagnostic information. The multicontrast scheme requires long scanning time. The combination of partially parallel imaging and compressed sensing (CS‐PPI) has been used to reconstruct accelerated scans. However, there are several unsolved problems in existing methods. The target of this work is to improve existing CS‐PPI methods for multicontrast imaging, especially for two‐dimensional imaging.

Image formation in diffusion MRI: A review of recent technical developments

Diffusion magnetic resonance imaging (MRI) is a standard imaging tool in clinical neurology, and is becoming increasingly important for neuroscience studies due to its ability to depict complex neuroanatomy (eg, white matter connectivity). Single‐shot echo‐planar imaging is currently the predominant formation method for diffusion MRI, but suffers from blurring, distortion, and low spatial resolution. A number of methods have been proposed to address these limitations and improve diffusion MRI acquisition. Here, the recent technical developments for image formation in diffusion MRI are reviewed. We discuss three areas of advance in diffusion MRI: improving image fidelity, accelerating acquisition, and increasing the signal‐to‐noise ratio.

Reducing slab boundary artifacts in three-dimensional multislab diffusion MRI using nonlinear inversion for slab profile encoding (NPEN).

To propose a method to reduce the slab boundary artifacts in three‐dimensional multislab diffusion MRI.

Homologous black‐bright‐blood and flexible interleaved imaging sequence (HOBBI) for dynamic contrast‐enhanced MRI of the vessel wall

To present a HOmologous Black‐Bright‐blood and flexible Interleaved imaging (HOBBI) sequence for dynamic contrast‐enhanced magnetic resonance imaging (MRI) of the vessel wall.

Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER)

Purpose Image 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 Methods We 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. Results Simulations 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. Conclusion The proposed method provides improvements over existing methods for diffusion acceleration, particularly for high SMS acceleration with in-plane undersampling.

Multiscale coherence regularization reconstruction using a nonlocal operator for fast variable-density spiral imaging

PURPOSE Nonlinear reconstruction can suppress pseudo-incoherent aliasing artifacts from variable-density spiral (VDS) trajectories when interleaves are undersampled for acquisition acceleration during MR imaging. However, large-scale aliasing artifact suppression often conflicts with fine-scale structure preservation and may cause deterioration of image quality in the reconstructed images. To address this issue, a sequential, multiscale coherence regularization algorithm using a nonlocal operator (mCORNOL) is proposed. METHODS mCORNOL is formed by exploiting the scale-control capacity of nonlocal operators in image structure measurement. By changing the scale of the structure measurement, the smoothing constraint scales can be adjusted. Starting with a large value, mCORNOL gradually reduces the smoothing constraint scale until it reaches the same level as the noise. Therefore, the large-scale smoothing constraint dominates the first few iterations of the reconstruction and removes aliasing artifacts as well as fine structures. In the following iterations, the smoothing constraint is restricted to a smaller and smaller scale, so the fidelity term progressively dominates and restores lost structures. Thus, aliasing artifact removal and structure preservation can be decoupled and achieved sequentially, which alleviates the conflicts between them. RESULTS Numerical simulation and in vivo experiment results demonstrate the superiority of mCORNOL for aliasing artifact suppression and image structure preservation at high reduction factors, compared to SENSE, Total Variation and the original CORNOL reconstruction. CONCLUSIONS mCORNOL reconstruction provides an effective way to improve image quality for undersampled VDS acquisitions.

Iteratively refined nonlocal total variation regularization for Parallel variable density spiral imaging reconstruction

Parallel variable density spiral imaging is an efficient fast imaging technique that combines spiral-based fast k-space acquisition and sensitivity encoding reconstruction of parallel imaging. However, due to its ill-conditioned system matrix, sensitivity encoding reconstruction can suffer from severe noise amplification or residual aliasing artifacts. To solve the problem, a regularized reconstruction that is based on iteratively refined nonlocal total variation regularization was proposed in this study. The proposed method extends current popular total variation to non-adjacent pixels and iteratively improves the results based on Bregman scheme. The phantom simulation and in vivo experiments results demonstrate that this method can effectively suppress noise amplification in sensitivity encoding reconstruction while preserving image details. Compared with conventional total variation regularized images, images reconstructed by the new method are free of staircase artifacts and suffer less from structure loss.