Improved sensitivity and temporal resolution in perfusion FMRI using velocity selective inversion ASL

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146889/1/mrm27461_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146889/2/mrm27461.pd

Monte Carlo SURE‐based parameter selection for parallel magnetic resonance imaging reconstruction

Purpose Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein's unbiased risk estimate that minimizes the multichannel k‐space mean squared error (MSE). We automatically tune parameters for image reconstruction methods that preserve the undersampled acquired data, which cannot be accomplished using existing techniques. Theory We derive a weighted MSE criterion appropriate for data‐preserving regularized parallel imaging reconstruction and the corresponding weighted Stein's unbiased risk estimate. We describe a Monte Carlo approximation of the weighted Stein's unbiased risk estimate that uses two evaluations of the reconstruction method per candidate parameter value. Methods We reconstruct images using the denoising sparse images from GRAPPA using the nullspace method (DESIGN) and L 1 iterative self‐consistent parallel imaging (L 1 ‐SPIRiT). We validate Monte Carlo Stein's unbiased risk estimate against the weighted MSE. We select the regularization parameter using these methods for various noise levels and undersampling factors and compare the results to those using MSE‐optimal parameters. Results Our method selects nearly MSE‐optimal regularization parameters for both DESIGN and L 1 ‐SPIRiT over a range of noise levels and undersampling factors. Conclusion The proposed method automatically provides nearly MSE‐optimal choices of regularization parameters for data‐preserving nonlinear parallel MRI reconstruction methods. Magn Reson Med 71:1760–1770, 2014. © 2013 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106872/1/mrm24840.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/106872/2/mrm24840-sup-0001-suppinfo.pd

Stochastic Optimization of 3D Non-Cartesian Sampling Trajectory (SNOPY)

Optimizing 3D k-space sampling trajectories for efficient MRI is important yet challenging. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patterns via data-driven optimization. We built a differentiable MRI system model to enable gradient-based methods for sampling trajectory optimization. By combining training losses, the algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method can either optimize the gradient waveform (spline-based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method optimizes sampling trajectories synergistically with either model-based or learning-based reconstruction methods. We proposed several strategies to alleviate the severe non-convexity and huge computation demand posed by the high-dimensional optimization. The corresponding code is organized as an open-source, easy-to-use toolbox. We applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the reconstruction PSNR of a 3D kooshball trajectory was increased by 4 dB with SNOPY optimization. In the prospective studies, by optimizing the rotation angles of a stack-of-stars (SOS) trajectory, SNOPY improved the PSNR by 1.4dB compared to the best empirical method. Optimizing the gradient waveform of a rotational EPI trajectory improved subjects' rating of the PNS effect from 'strong' to 'mild.' In short, SNOPY provides an efficient data-driven and optimization-based method to tailor non-Cartesian sampling trajectories.Comment: 13 pages, 8 figure

Practical considerations for territorial perfusion mapping in the cerebral circulation using super-selective pseudo-continuous arterial spin labeling

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151967/1/mrm27936.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151967/2/mrm27936_am.pd

Myelin water fraction estimation using small- tip fast recovery MRI

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155960/1/mrm28259.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155960/2/mrm28259-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155960/3/mrm28259_am.pd

Design of spectralâ spatial phase prewinding pulses and their use in smallâ tip fast recovery steadyâ state imaging

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141089/1/mrm26794_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141089/2/mrm26794.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141089/3/mrm26794-sup-0001-suppinfo.pd

A Simple Method for Constrained Optimal Control RF Pulse Design

Optimal control (OC) methods for RF pulse design are useful in cases where the small-tip angle (STA) approximation is violated. Furthermore, designs with physically meaningful constraints (e.g., RF peak amplitude and integrated power) eliminate the need for parameter tuning to create realizable pulses. In this abstract we introduce a constrained fast OC method that easily generalizes to a variety of RF pulse designs. We demonstrate with examples of SMS and spectral prewinding pulses in simulation and in vivo. The constrained fast OC method guarantees that RF pulses will meet physical constraints while outperforming their non-OC counterparts

Pulseq: A rapid and hardwareâ independent pulse sequence prototyping framework

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136354/1/mrm26235.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136354/2/mrm26235_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136354/3/mrm26235-sup-0001-suppinfo.pd