Citations

AbstractPurposeTo demonstrate a novel MR elastography (MRE) technique, termed here wavelet MRE. With this technique, broadband motion sensitivity is achievable. Moreover, the true tissue displacement can be reconstructed with a simple inverse transform.MethodsA wavelet MRE sequence was developed with motion‐encoding gradients based on Haar wavelets. From the phase images' displacement was estimated using an inverse transform. Simulations were performed using a frequency sweep and a transient as ground‐truth motions. A PVC phantom was scanned using wavelet MRE and standard MRE with both transient (one and 10 cycles of 90‐Hz motion) and steady‐state dual‐frequency motion (30 and 60 Hz) for comparison. The technique was tested in a human brain, and motion trajectories were estimated for each voxel.ResultsIn simulation, the displacement information estimated from wavelet MRE closely matched the true motion. In the phantom test, the MRE phase data generated from the displacement information derived from wavelet MRE agreed well with standard MRE data. Testing of wavelet MRE to assess transient motion waveforms in the brain was successful, and the tissue motion observed was consistent with a previous study.ConclusionThe uniform and broadband frequency response of wavelet MRE makes it a promising method for imaging transient, multifrequency motion, or motion with unknown frequency content. One potential application is measuring the response of brain tissue undergoing low‐amplitude, transient vibrations as a model for the study of traumatic brain injury.

Abstract: Objectives: Locally low-rank (LLR) denoising of functional magnetic resonance imaging (fMRI) time series image data is extended to multi-echo (ME) data. The proposed method extends the capabilities of non-physiologic noise suppression beyond single-echo applications with a dedicated ME algorithm. Materials and Methods: Following an institutional review board (IRB) approved protocol, resting-state fMRI data were acquired in 7 healthy subjects. A compact 3T scanner enabled whole-brain acquisition of multiband ME fMRI data at high spatial resolution (1.4 × 1.4 × 2.8 mm3) with a 1810 ms repetition time (TR). Image data were denoised with ME-LLR preceding functional processing. The results of connectivity maps generated from denoised data were compared with maps generated with equivalent processing of non-denoised images. To assess ME-LLR as a method to reduce scan time, comparisons were made between maps computed from image data with full and retrospectively truncated durations. Assessments were completed with seed-based connectivity analyses using echo-combined image data. In a feasibility assessment, nondenoised and denoised full-duration echo-combined data were equivalently processed with independent component analysis (ICA) and compared. Results: ME-LLR denoising yielded strengthened resting-state network connectivity maps after nuisance regression and seed-based connectivity analysis. In assessing ME-LLR as a scan reduction mechanism, maps generated from denoised data at half scan time showed comparable quality with maps generated from full-duration, non-denoised data, at both single subject and group levels. ME-LLR substantially increased temporal signal-to-noise ratio (tSNR) for image data respective to each individual echo and for image data after nuisance regression. Among echo-specific image volumes, increases in tSNR yielded by ME-LLR were most pronounced for image data with the longest echo time and thereby lowest SNR. ICA showed resting-state networks consistently identified between non-denoised and denoised data, with clearer demarcation of networks for ME-LLR. Conclusions: ME-LLR is demonstrated to suppress non-physiologic noise, enhance functional connectivity map quality, and could potentially facilitate scan time reduction in ME-fMRI.