2,961 research outputs found
Convolutional Framework for Accelerated Magnetic Resonance Imaging
Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that
provides exquisite soft-tissue contrast without using ionizing radiation. The
clinical application of MRI may be limited by long data acquisition times;
therefore, MR image reconstruction from highly undersampled k-space data has
been an active area of research. Many works exploit rank deficiency in a Hankel
data matrix to recover unobserved k-space samples; the resulting problem is
non-convex, so the choice of numerical algorithm can significantly affect
performance, computation, and memory. We present a simple, scalable approach
called Convolutional Framework (CF). We demonstrate the feasibility and
versatility of CF using measured data from 2D, 3D, and dynamic applications.Comment: IEEE ISBI 2020, International Symposium on Biomedical Imagin
Recommended from our members
Coil combination using linear deconvolution in k-space for phase imaging
Background: The combination of multi-channel data is a critical step for the imaging of phase and susceptibility contrast in magnetic resonance imaging (MRI). Magnitude-weighted phase combination methods often produce noise and aliasing artifacts in the magnitude images at accelerated imaging sceneries. To address this issue, an optimal coil combination method through deconvolution in k-space is proposed in this paper.
Methods: The proposed method firstly employs the sum-of-squares and phase aligning method to yield a complex reference coil image which is then used to calculate the coil sensitivity and its Fourier transform. Then, the coil k-space combining weights is computed, taking into account the truncated frequency data of coil sensitivity and the acquired k-space data. Finally, combining the coil k-space data with the acquired weights generates the k-space data of proton distribution, with which both phase and magnitude information can be obtained straightforwardly. Both phantom and in vivo imaging experiments were conducted to evaluate the performance of the proposed method.
Results: Compared with magnitude-weighted method and MCPC-C, the proposed method can alleviate the phase cancellation in coil combination, resulting in a less wrapped phase.
Conclusions: The proposed method provides an effective and efficient approach to combine multiple coil image in parallel MRI reconstruction, and has potential to benefit routine clinical practice in the future
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous
quantification of multiple properties of biological tissues. It relies on a
pseudo-random acquisition and the matching of acquired signal evolutions to a
precomputed dictionary. However, the dictionary is not scalable to
higher-parametric spaces, limiting MRF to the simultaneous mapping of only a
small number of parameters (proton density, T1 and T2 in general). Inspired by
diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF
sequence with embedded diffusion-encoding gradients along all three axes to
efficiently encode orientational diffusion and T1 and T2 relaxation. We take
advantage of a convolutional neural network (CNN) to reconstruct multiple
quantitative maps from this single, highly undersampled acquisition. We bypass
expensive dictionary matching by learning the implicit physical relationships
between the spatiotemporal MRF data and the T1, T2 and diffusion tensor
parameters. The predicted parameter maps and the derived scalar diffusion
metrics agree well with state-of-the-art reference protocols. Orientational
diffusion information is captured as seen from the estimated primary diffusion
directions. In addition to this, the joint acquisition and reconstruction
framework proves capable of preserving tissue abnormalities in multiple
sclerosis lesions
- …