Data‐driven motion‐corrected brain MRI incorporating pose‐dependent B 0 fields

PurposeThe 3D multi‐shot EPI imaging offers several benefits including higher SNR and high isotropic resolution compared to 2D single shot EPI. However, it suffers from shot‐to‐shot inconsistencies arising from physiologically induced phase variations and bulk motion. This work proposed a motion compensated structured low‐rank (mcSLR) reconstruction method to address both issues for 3D multi‐shot EPI.MethodsStructured low‐rank reconstruction has been successfully used in previous work to deal with inter‐shot phase variations for 3D multi‐shot EPI imaging. It circumvents the estimation of phase variations by reconstructing an individual image for each phase state which are then sum‐of‐squares combined, exploiting their linear interdependency encoded in structured low‐rank constraints. However, structured low‐rank constraints become less effective in the presence of inter‐shot motion, which corrupts image magnitude consistency and invalidates the linear relationship between shots. Thus, this work jointly models inter‐shot phase variations and motion corruptions by incorporating rigid motion compensation for structured low‐rank reconstruction, where motion estimates are obtained in a fully data‐driven way without relying on external hardware or imaging navigators.ResultsSimulation and in vivo experiments at 7T have demonstrated that the mcSLR method can effectively reduce image artifacts and improve the robustness of 3D multi‐shot EPI, outperforming existing methods which only address inter‐shot phase variations or motion, but not both.ConclusionThe proposed mcSLR reconstruction compensates for rigid motion, and thus improves the validity of structured low‐rank constraints, resulting in improved robustness of 3D multi‐shot EPI to both inter‐shot motion and phase variations.

“…However, the phase variations at the intra‐shot group level are not considered currently. Some recent work 57 has considered motion induced

{B}_0

variations to improve the performance of mcSENSE for anatomical imaging at 7T. This method uses a physics‐inspired

{B}_0

model, which describes the

{B}_0

variation map as a linear combination of the pitch and roll rotation angles, and thus only two additional linear coefficient maps instead of an individual

{B}_0

variation map for each motion state need to be estimated.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

{B}_0

field changes for structural imaging at 7T 57 . However, none of the existing motion correction methods compensate for physiologically induced phase variations for 3D multi‐shot EPI.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Motion compensated structured low‐rank reconstruction for 3D multi‐shot EPI

Chen,

Wu,

Chiew

2024

“…This motivates the implementation of motion compensation (real‐time or retrospective) in future work. Although effective motion correction 43 might enable longer TR MP2RAGE times and correspondingly scan durations, CNR gains are likely to be modest, and motion correction remains challenging at 7T owing to concomitant changes in static B 0 44 and B 1 − fields 45 with head pose. A larger sample size is also needed to verify which images (FLAWS min , FLAWS hc , FLAWS hco , and MP2RAGE UNI) provide contrast improvement and confer clinical utility.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous Optimization of MP2RAGE T₁‐weighted (UNI) and FLuid And White matter Suppression (FLAWS) brain images at 7T using Extended Phase Graph (EPG) Simulations

Dokumacı

Aitken

Sedlacik

et al. 2022

Self Cite

Purpose The MP2RAGE sequence is typically optimized for either T1‐weighted uniform image (UNI) or gray matter–dominant fluid and white matter suppression (FLAWS) contrast images. Here, the purpose was to optimize an MP2RAGE protocol at 7 Tesla to provide UNI and FLAWS images simultaneously in a clinically applicable acquisition time at <0.7 mm isotropic resolution. Methods Using the extended phase graph formalism, the signal evolution of the MP2RAGE sequence was simulated incorporating T2 relaxation, diffusion, RF spoiling, and B1+ variability. Flip angles and TI were optimized at different TRs (TRMP2RAGE) to produce an optimal contrast‐to‐noise ratio for UNI and FLAWS images. Simulation results were validated by comparison to MP2RAGE brain scans of 5 healthy subjects, and a final protocol at TRMP2RAGE = 4000 ms was applied in 19 subjects aged 8–62 years with and without epilepsy. Results FLAWS contrast images could be obtained while maintaining >85% of the optimal UNI contrast‐to‐noise ratio. Using TI1/TI2/TRMP2RAGE of 650/2280/4000 ms, 6/8 partial Fourier in the inner phase‐encoding direction, and GRAPPA factor = 4 in the other, images with 0.65 mm isotropic resolution were produced in <7.5 min. The contrast‐to‐noise ratio was around 20% smaller at TRMP2RAGE = 4000 ms compared to that at TRMP2RAGE = 5000 ms; however, the 20% shorter duration makes TRMP2RAGE = 4000 ms a good candidate for clinical applications example, pediatrics. Conclusion FLAWS and UNI images could be obtained in a single scan with 0.65 mm isotropic resolution, providing a set of high‐contrast images and full brain coverage in a clinically applicable scan time. Images with excellent anatomical detail were demonstrated over a wide age range using the optimized parameter set.

“…[11][12][13][14][15] As a second category, data-driven estimation methods estimate changes in motion and 𝛿B 0 by fitting a motion-and 𝛿B 0 -informed signal model to the acquired imaging data. 2,7,16,17 As a third category, navigators are dedicated encoding blocks added to the sequence that encode relative changes in motion and/or 𝛿B 0 that can be retrieved using different strategies. Inter-navigator comparison of the acquired navigator signals is usually performed for 2D/3D navigators using image registration and B 0 fitting, to estimate relative motion and 𝛿B 0 .…”

Section: Introductionmentioning

confidence: 99%

Rapid and accurate navigators for motion and B₀ tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators

Brackenier,

Wang,

Liao

et al. 2024

Self Cite

PurposeTo develop a framework that jointly estimates rigid motion and polarizing magnetic field (B0) perturbations () for brain MRI using a single navigator of a few milliseconds in duration, and to additionally allow for navigator acquisition at arbitrary timings within any type of sequence to obtain high‐temporal resolution estimates.Theory and MethodsMethods exist that match navigator data to a low‐resolution single‐contrast image (scout) to estimate either motion or . In this work, called QUEEN (QUantitatively Enhanced parameter Estimation from Navigators), we propose combined motion and estimation from a fast, tailored trajectory with arbitrary‐contrast navigator data. To this end, the concept of a quantitative scout (Q‐Scout) acquisition is proposed from which contrast‐matched scout data is predicted for each navigator. Finally, navigator trajectories, contrast‐matched scout, and are integrated into a motion‐informed parallel‐imaging framework.ResultsSimulations and in vivo experiments show the need to model to obtain accurate motion parameters estimated in the presence of strong . Simulations confirm that tailored navigator trajectories are needed to robustly estimate both motion and . Furthermore, experiments show that a contrast‐matched scout is needed for parameter estimation from multicontrast navigator data. A retrospective, in vivo reconstruction experiment shows improved image quality when using the proposed Q‐Scout and QUEEN estimation.ConclusionsWe developed a framework to jointly estimate rigid motion parameters and from navigators. Combing a contrast‐matched scout with the proposed trajectory allows for navigator deployment in almost any sequence and/or timing, which allows for higher temporal‐resolution motion and estimates.