Dataset Information

Motion-compensated gradient waveforms for tensor-valued diffusion encoding by constrained numerical optimization.

ABSTRACT:

Purpose

Diffusion-weighted MRI is sensitive to incoherent tissue motion, which may confound the measured signal and subsequent analysis. We propose a "motion-compensated" gradient waveform design for tensor-valued diffusion encoding that negates the effects bulk motion and incoherent motion in the ballistic regime.

Methods

Motion compensation was achieved by constraining the magnitude of gradient waveform moment vectors. The constraint was incorporated into a numerical optimization framework, along with existing constraints that account for b-tensor shape, hardware restrictions, and concomitant field gradients. We evaluated the efficacy of encoding and motion compensation in simulations, and we demonstrated the approach by linear and planar b-tensor encoding in a healthy heart in vivo.

Results

The optimization framework produced asymmetric motion-compensated waveforms that yielded b-tensors of arbitrary shape with improved efficiency compared with previous designs for tensor-valued encoding, and equivalent efficiency to previous designs for linear (conventional) encoding. Technical feasibility was demonstrated in the heart in vivo, showing vastly improved data quality when using motion compensation. The optimization framework is available online in open source.

Conclusion

Our gradient waveform design is both more flexible and efficient than previous methods, facilitating tensor-valued diffusion encoding in tissues in which motion would otherwise confound the signal. The proposed design exploits asymmetric encoding times, a single refocusing pulse or multiple refocusing pulses, and integrates compensation for concomitant gradient effects throughout the imaging volume.

SUBMITTER: Szczepankiewicz F

PROVIDER: S-EPMC7821235 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Motion-compensated gradient waveforms for tensor-valued diffusion encoding by constrained numerical optimization.

Szczepankiewicz Filip F Sjölund Jens J Dall'Armellina Erica E Plein Sven S Schneider Jürgen E JE Teh Irvin I Westin Carl-Fredrik CF

Magnetic resonance in medicine 20201013 4

<h4>Purpose</h4>Diffusion-weighted MRI is sensitive to incoherent tissue motion, which may confound the measured signal and subsequent analysis. We propose a "motion-compensated" gradient waveform design for tensor-valued diffusion encoding that negates the effects bulk motion and incoherent motion in the ballistic regime.<h4>Methods</h4>Motion compensation was achieved by constraining the magnitude of gradient waveform moment vectors. The constraint was incorporated into a numerical optimizatio ...[more]

PMID: 33048401

Similar Datasets

Project description:Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding-applied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/?m2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.

Project description:Monitoring time dependence with diffusion MRI yields observables sensitive to compartment sizes (restricted diffusion) and membrane permeability (water exchange). However, restricted diffusion and exchange have opposite effects on the diffusion-weighted signal, which can lead to errors in parameter estimates. In this work, we propose a signal representation that incorporates the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. The representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange. We demonstrate that these scalars span a two-dimensional space that can be used to choose waveforms that selectively probe restricted diffusion or exchange, eliminating the correlation between the two phenomena. We found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. We also show that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. For example, we found that the variation of mixing time in filter-exchange imaging corresponds to variation of our exchange-weighting scalar at a fixed value of the restriction-weighting scalar. The proposed signal representation was evaluated using Monte Carlo simulations in identical parallel cylinders with hexagonal and random packing as well as parallel cylinders with gamma-distributed radii. Results showed that the approach is sensitive to sizes in the interval 4-12 μm and exchange rates in the simulated range of 0 to 20 s-1 , but also that there is a sensitivity to the extracellular geometry. The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as a guide to protocol design capable of separating the two effects.

Dataset Information

Motion-compensated gradient waveforms for tensor-valued diffusion encoding by constrained numerical optimization.

Purpose

Methods

Results

Conclusion

Publications

Motion-compensated gradient waveforms for tensor-valued diffusion encoding by constrained numerical optimization.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets