Interpretable Dimension Reduction for MRI Channel Suppression.

Channel suppression can reduce the redundant information in multiple channel receiver coils and accelerate reconstruction speed to meet real-time imaging requirements. The principal component analysis has been used for channel suppression, but it is difficult to be interpreted because all channels contribute to principal components. Furthermore, the importance of interpretability in machine learning has recently attracted increasing attention in radiology.

Virtual Conjugate Coil for Improving KerNL Reconstruction.

Ker NL is a general kernel-based framework for auto calibrated reconstruction method, which does not need any explicit formulas of the kernel function for characterizing nonlinear relationships between acquired and unacquired k-space data. It is non-iterative without requiring a large amount of computational costs. Since the limited autocalibration signals (ACS) are acquired to perform KerNL calibration and the calibration suffers from the overfitting problem, more training data can improve the kernel model accuracy.

Augmented T -weighted steady state magnetic resonance imaging.

T contrasts obtained using short-TR incoherent steady state gradient echo (GRE) methods are generally suboptimal, to which non-T factors in the signals play a major part. In this work, we proposed an augmented T -weighted (aT W) method to extract the signal ratio between routine GRE T W and proton density-weighted signals that effectively removes the non-T effects from the original T W signals, including proton density, T * decay, and coil sensitivity. A recently proposed multidimensional integration (MDI) technique was incorporated in the aT W calculation for better signal-to-noise ratio (SNR) performance.

MULTI-parametric MR imaging with fLEXible design (MULTIPLEX).

Purpose: To introduce a gradient echo (GRE) -based method, namely MULTIPLEX, for single-scan 3D multi-parametric MRI with high resolution, signal-to-noise ratio (SNR), accuracy, efficiency, and acquisition flexibility.

Theory: With a comprehensive design with dual-repetition time (TR), dual flip angle (FA), multi-echo, and optional flow modulation features, the MULTIPLEX signals contain information on radiofrequency (RF) B fields, proton density, T , susceptibility and blood flows, facilitating multiple qualitative images and parametric maps.

Methods: MULTIPLEX was evaluated on system phantom and human brains, via visual inspection for image contrasts and quality or quantitative evaluation via simulation, phantom scans and literature comparison.

A multi-dimensional integration (MDI) strategy for MR T * mapping.

MRI signals are intrinsically multi-dimensional, and signal behavior may be orthogonal among different dimensions. Such dimensional orthogonality can be utilized to eliminate unwanted effects and facilitate mathematical simplicity during image processing for improved outcomes. In this work, we will demonstrate and analyze the principles and performance of a newly developed multi-dimensional integration (MDI) strategy in MR T * mapping.

Seed prioritized unwrapping (SPUN) for MR phase imaging.

Background: Region-growing-based phase unwrapping methods have the potential for lossless phase aliasing removal, but generally suffer from unwrapping error propagation associated with discontinuous phase and/or long calculation times. The tradeoff point between robustness and efficiency of phase unwrapping methods in the region-growing category requires improvement.

Purpose: To demonstrate an accurate, robust, and efficient region-growing phase unwrapping method for MR phase imaging applications.

KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction.

The conventional calibration-based parallel imaging method assumes a linear relationship between the acquired multi-channel k-space data and the unacquired missing data, where the linear coefficients are estimated using some auto-calibration data. In this paper, we first analyze the model errors in the conventional calibration-based methods and demonstrate the nonlinear relationship. Then, a much more general nonlinear framework is proposed for auto-calibrated parallel imaging.

A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI.

While many low rank and sparsity-based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub-Nyquist data. Within this framework, many existing algorithms can be extended to kernel framework with nonlinear models.

M-MRI: A Manifold-based Framework to Highly Accelerated Dynamic Magnetic Resonance Imaging.

High-dimensional signals, including dynamic magnetic resonance (dMR) images, often lie on low dimensional manifold. While many current dynamic magnetic resonance imaging (dMRI) reconstruction methods rely on priors which promote low-rank and sparsity, this paper proposes a novel manifold-based framework, we term M-MRI, for dMRI reconstruction from highly undersampled -space data. Images in dMRI are modeled as points on or close to a smooth manifold, and the underlying manifold geometry is learned through training data, called "navigator" signals.

ACCELERATING DYNAMIC MAGNETIC RESONANCE IMAGING BY NONLINEAR SPARSE CODING.

Although being high-dimensional, dynamic magnetic resonance images usually lie on low-dimensional manifolds. Nonlinear models have been shown to capture well that latent low-dimensional nature of data, and can thus lead to improvements in the quality of constrained recovery algorithms. This paper advocates a novel reconstruction algorithm for dynamic magnetic resonance imaging (dMRI) based on nonlinear dictionary learned from low-spatial but high-temporal resolution images.

Fast GRAPPA reconstruction with random projection.

Purpose: To address the issue of computational complexity in generalized autocalibrating partially parallel acquisition (GRAPPA) when several calibration data are used.

Method: GRAPPA requires fully sampled data for accurate calibration with increasing data needed for higher reduction factors to maintain accuracy, which leads to longer computational time, especially in a three-dimensional (3D) setting and with higher channel count coils. Channel reduction methods have been developed to address this issue when massive array coils are used.