T₂ shuffling: Sharp, multicontrast, volumetric fast spin‐echo imaging

Abstract

Purpose A new acquisition and reconstruction method called T2 Shuffling is presented for volumetric fast spin-echo (3D FSE) imaging. T2 Shuffling reduces blurring and recovers many images at multiple T2 contrasts from a single acquisition at clinically feasible scan times (6 to 7 minutes). Theory and Methods The parallel imaging forward model is modified to account for temporal signal relaxation during the echo train. Scan efficiency is improved by acquiring data during the transient signal decay and by increasing echo train lengths without loss in SNR. By (1) randomly shuffling the phase encode view ordering, (2) constraining the temporal signal evolution to a low-dimensional subspace, and (3) promoting spatio-temporal correlations through locally low rank regularization, a time series of virtual echo time images is recovered from a single scan. A convex formulation is presented that is robust to partial voluming and RF field inhomogeneity. Results Retrospective under-sampling and in vivo scans confirm the increase in sharpness afforded by T2 Shuffling. Multiple image contrasts are recovered and used to highlight pathology in pediatric patients. A proof-of-principle method is integrated into a clinical musculoskeletal imaging workflow. Conclusion The proposed T2 Shuffling method improves the diagnostic utility of 3D FSE by reducing blurring and producing multiple image contrasts from a single scan.

Improved magnetic resonance fingerprinting reconstruction with low‐rank and subspace modeling

“…Rather than directly solving a generic low‐rank matrix recovery problem, we can further incorporate a temporal subspace constraint to simplify the image reconstruction problem. Specifically, in the context of MRF, a dictionary can be constructed using Bloch simulations, which contains all possible signal evolutions.…”

“…In this work, we introduce an alternative model‐based reconstruction method for MRF based on low‐rank and subspace modeling. This builds on the previous low‐rank reconstruction methods for dynamic MRI and conventional MR relaxometry . Here the proposed method enforces an explicit low‐rank constraint to capture strong spatiotemporal correlation within MRF time‐series images.…”

GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation

“…For example, a T 1 or T 2 relaxation model with additional B 1 and/or B 0 considerations can be used to generate an ensemble of possible signal curves covering a range of to‐be‐estimated parameters, from which a temporal basis can be generated using principal component analysis (PCA) or singular value decomposition (SVD). Several novel approaches belonging to this category have shown great promise in accelerated MR parameter mapping or MR fingerprinting and contrast‐resolved 3D FSE imaging . However, in certain dynamic imaging applications, such as cine imaging or DCE‐MRI, a signal model may not be available or it is not sufficient to accurately represent the underlying temporal signal evolution.…”

“…A method of reconstructing golden‐angle radial images with extra motion dimensions, called XD‐GRASP (GRASP imaging with extra dimensions), has been proposed as a new way of handling respiratory motion with superior imaging performance to standard motion‐averaged reconstruction . Such an imaging and reconstruction strategy is not limited to the management of motion and can also be explored to generate new information of potential clinical value or to address other unwanted effects such as contrast blurring in 3D fast spin‐echo (FSE) MRI . In a more recent study, GRASP has also been extended to a further optimized framework called RACER‐GRASP (respiratory‐weighted, aortic contrast‐enhancement‐guided and coil unstreaking GRASP) for improved dynamic contrast‐enhanced MRI (DCE‐MRI) of the liver, which enables contrast enhancement curve‐guided data sorting, reduced motion blurring, and less residual streaking artifacts.…”

Multicontrast reconstruction using compressed sensing with low rank and spatially varying edge-preserving constraints for high-resolution MR characterization of myocardial infarction

“…Pedersen et al proposed k‐t principal component analysis to temporally constrain the reconstruction using the temporal basis estimated from training data via principal component analysis; the method was then applied for accelerating MR parameter mapping . Tamir et al used the subspace and locally low rank constraints in T 2 Shuffling to reduce the reconstruction dimensionality and sampling complexity for T 2 imaging using 3D fast spin‐echo. To take advantage of spatial correlations in multicontrast data, Zhao et al extended the Partially Separable Function model and presented a generalized formulation for the jointly low rank and spatial sparsity constrained reconstruction and demonstrated its feasibility for accelerating MR parameter mapping .…”