Stephen F. Cauley

Fast quantitative susceptibility mapping with L1-regularization and automatic parameter selection.

To enable fast reconstruction of quantitative susceptibility maps with total variation penalty and automatic regularization parameter selection.

Interslice leakage artifact reduction technique for simultaneous multislice acquisitions

Controlled aliasing techniques for simultaneously acquired echo‐planar imaging slices have been shown to significantly increase the temporal efficiency for both diffusion‐weighted imaging and functional magnetic resonance imaging studies. The “slice‐GRAPPA” (SG) method has been widely used to reconstruct such data. We investigate robust optimization techniques for SG to ensure image reconstruction accuracy through a reduction of leakage artifacts.

Wave‐CAIPI for highly accelerated 3D imaging

To introduce the wave‐CAIPI (controlled aliasing in parallel imaging) acquisition and reconstruction technique for highly accelerated 3D imaging with negligible g‐factor and artifact penalties.

Fast image reconstruction with L2‐regularization

We introduce L2‐regularized reconstruction algorithms with closed‐form solutions that achieve dramatic computational speed‐up relative to state of the art L1‐ and L2‐based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction.

MGH-USC Human Connectome Project datasets with ultra-high b-value diffusion MRI

The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.

Superfast and Stable Structured Solvers for Toeplitz Least Squares via Randomized Sampling

We present some superfast (

Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting

O((m+n)\log^{2}(m+n))

Fast Group Matching for MR Fingerprinting Reconstruction

complexity) and stable structured direct solvers for

Accelerating magnetic resonance fingerprinting (MRF) using t-blipped simultaneous multislice (SMS) acquisition.

m\times n

Image reconstruction by domain-transform manifold learning

Toeplitz least squares problems. Based on the displacement equation, a Toeplitz matrix