Minimum‐norm reconstruction for sensitivity‐encoded magnetic resonance spectroscopic imaging

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246

Despite continuous hardware advances, MRI is frequently subject to field perturbations that are of higher than first order in space and thus violate the traditional k-space picture of spatial encoding. Sources of higher order perturbations include eddy currents, concomitant fields, thermal drifts, and imperfections of higher order shim systems. In conventional MRI with Fourier reconstruction, they give rise to geometric distortions, blurring, artifacts, and error in quantitative data. This work describes an alternative approach in which the entire field evolution, including higher order effects, is accounted for by viewing image reconstruction as a generic inverse problem. The relevant field evolutions are measured with a third-order NMR field camera. Algebraic reconstruction is then formulated such as to jointly minimize artifacts and noise in the resulting image. It is solved by an iterative conjugate-gradient algorithm that uses explicit matrix-vector multiplication to accommodate arbitrary net encoding. The feasibility and benefits of this approach are demonstrated by examples of diffusion imaging. In a phantom study, it is shown that higher order reconstruction largely overcomes variable image distortions that diffusion gradients induce in EPI data. In vivo experiments then demonstrate that the resulting geometric consistency permits straightforward tensor analysis without coregistration. Magn Reson Med 65:1690-1701, 2011.

“…Nevertheless, as shown in Ref. 40 and also later in this work, finer discretization can serve to optimize depiction characteristics. Combining temporal and spatial discretization, Eq.…”

Section: Higher Order Reconstructionmentioning

confidence: 60%

Higher order reconstruction for MRI in the presence of spatiotemporal field perturbations

Wilm

Barmet

Pavan

et al. 2011

149

246

“…One key advantage of this algorithm over other methods, e.g., the Householder algorithm, is that it does not generate any large matrices. The Lanczos algorithm is closely related to the method of conjugate gradients, which has been used in MRI reconstruction (24)(25)(26)(27)(28), as well as RF pulse design (29,30). Both kinds of algorithms belong to the family of Krylov space-based methods and share beneficial convergence and recovery properties (31).…”

Section: Lanczos Iterationmentioning

confidence: 99%

Optimal design of multiple‐channel RF pulses under strict power and SAR constraints

Brunner

Pruessmann

2010

Parallel radio frequency transmission has recently been explored as a means of tailoring the spatial response of MR excitation. In particular, parallel transmission is increasingly used to accelerate radio frequency pulses that rely on time-varying gradient fields to achieve selectivity in multiple dimensions. The design of the underlying multiple-channel radio frequency waveforms is mostly based on regularized least-squares optimization in close analogy with image reconstruction in parallel imaging. However, this analogy has important limitations. Unlike image reconstruction, the design of radio frequency waveforms is subject to multiple strict constraints, which arise from technical power limits, as well as safety limits on local and global energy deposition in vivo. To optimize excitation profiles under such strict constraints, it is proposed to depart from the regularization strategy and rely on semidefinite programming instead. To render this approach fast, it is performed in a reduced search space, which is obtained by initial Lanczos iteration. The proposed algorithm is demonstrated to enable efficient pulse optimization within exactly the given constraints, including local specific absorption rate limits for multiple compartments. It is also shown that the proposed approach readily accommodates advanced forward models of the excitation process, including the effects of local off-resonance and transverse relaxation.

“…While this is not a problem when scanning at high resolution, it becomes an issue when SENSE is utilized to unfold low-resolution images. This side-lobe aliasing, first encountered in SENSE spectroscopic imaging, can be mitigated by reconstructing using the minimum-norm formalism (16).…”

Section: Sense Reconstructionmentioning

confidence: 99%

“…Effects from coil sensitivity variations across the relatively large voxels of the training data are addressed by using the minimum-norm formalism for SENSE (16). The performance of the modified k-t SENSE method including noise propagation effects was evaluated relative to conventional k-t SENSE by using computer simulations and in vivo perfusion data.…”

mentioning

confidence: 99%

Accelerated cardiac perfusion imaging using k‐t SENSE with SENSE training

Vitanis

Manka

Boesiger

et al. 2009

Self Cite

In k-t sensitivity encoding (SENSE), MR data acquisition performed in parallel by multiple coils is accelerated by sparsely sampling the k-space over time. The resulting aliasing is resolved by exploiting spatiotemporal correlations inherent in dynamic images of natural objects. In this article, a modified k-t SENSE reconstruction approach is presented, which aims at improving the temporal fidelity of first-pass, contrast-enhanced myocardial perfusion images at high accelerations.