Head motion is one of major concerns in current resting-state functional MRI studies. Image realignment including motion estimation and spatial resampling is often applied to achieve rigid-body motion correction. While the accurate estimation of motion parameters has been addressed in most studies, spatial resampling could also produce spurious variance, and lead to unexpected errors on the amplitude of BOLD signal. In this study, two simulation experiments were designed to characterize these variance related with spatial resampling. The fluctuation amplitude of spurious variance was first investigated using a set of simulated images with estimated motion parameters from a real dataset, and regions more likely to be affected by spatial resampling were found around the peripheral regions of the cortex. The other simulation was designed with three typical types of motion parameters to represent different extents of motion. It was found that areas with significant correlation between spurious variance and head motion scattered all over the brain and varied greatly from one motion type to another. In the last part of this study, four popular motion regression approaches were applied respectively and their performance in reducing spurious variance was compared. Among them, Friston 24 and Voxel-specific 12 model (Friston et al., 1996), were found to have the best outcomes. By separating related effects during fMRI analysis, this study provides a better understanding of the characteristics of spatial resampling and the interpretation of motion-BOLD relationship.

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High peak RF amplitude and excessive specific absorption rate (SAR) are two critical concerns for hardware implementation and patient safety in scientific and clinical research for high field MRI using parallel transmissions (pTX). In this paper, we introduce a squeezing strategy to reduce peak RF amplitude and integrated RF power via direct reshaping of the k-space trajectory. In the existing peak RF / integrated RF power optimization methods gradient amplitude or slew rate is reduced, but the k-space trajectory remains unchanged. Unlike these traditional methods, we worked directly in the excitation k-space to reshape k-space traversal by a squeezing vector in order to achieve peak RF and total RF power optimization, using a particle swarm optimization algorithm. The squeezing strategy was applied to the conventional variable density spiral (CVDS) and the variable rate selective excitation (VERSE) trajectories, dubbed SVDS (squeezed variable density spiral) and SVERSE (squeezing trajectory with VERSE), respectively, for different excitation profiles of small or large tip angles. Pulse acceleration and off-resonance effects were evaluated for an 8-ch pTX via Bloch simulation. CVDS, VERSE, SVDS, and SVERSE pulses were implemented on a 3T scanner with a 2-ch pTX. Phantom and in vivo experiments were performed for reduced FOV (rFOV) imaging. The results show that SVDS pulses simultaneously reduce integrated RF power and peak RF by about 30% on average compared to CVDS pulses for a square pattern ( $80\times80$ mm2) with flip angles of 30°, 90°, and 180°. Compared with the VERSE method under the same peak RF constraints, the SVDS method reduces integrated RF power by an average of 20% for small tip excitations for profiles of slice, rectangular, square, and circle, and has slightly reduced excitation accuracy slightly (about 0.6%, from 6.8% to 7.4%). The SVERSE method shortens the duration of the VERSE pulse by 12.8% at large ti p angle (180°). Feasibility for rFOV imaging was demonstrated with phantom and in vivo experiments with squeezed pulses.

To derive accurate diffusion metrics, both imaging and diffusion-sensitizing gradient pulses should be accounted for when calculating the diffusion-weighted b-matrix. However, it is complex to derive analytical solutions due to complicated interactions between gradient pulses, including orthogonal directions. This study proposes a general framework to calculate the b-matrix automatically (dubbed as Auto-b). Based on the divide-and-conquer approach, the b-matrix calculation is appropriately segmented, and the symbolic mathematical library is applied to handle integration operations for each interval. If the specifications of all gradient pulses are provided to Auto-b, an accurate b-matrix can be obtained. Three examples are explored to validate the accuracy of Auto-b and to detect b-value errors when using approximate calculations. (1) In the conventional spin-echo example, Auto-b exhibits high accuracy, as indicated by the maximum relative deviation of 1.68‰ between its calculated b-matrices and those obtained from analytical expressions. (2) Auto-b is applied to investigate the contribution of imaging gradients to the b-matrix in an optimized spin-echo echo planar imaging sequence at submillimeter resolution. Specifically, ignoring the contribution of imaging gradients results in a b-value error of 12.16 s/mm2 at the 0.8 × 0.8 × 0.8 mm3 resolution and 22.47 s/mm2 at the 0.6 × 0.6 × 0.8 mm3 resolution, respectively, when nominal b = 0. (3) Auto-b is also utilized to analyze the influence of approximate calculations in the spatiotemporally encoded sequence. The results showed that neglecting the contribution of phase-encoding blips causes large b-value errors up to 11.02 s/mm2. In addition, the rectangularization of trapezoidal waveforms led to a high relative b-value error of 39.91%. This study validates the high accuracy of Auto-b and underscores the importance of accurate b-value calculations in both submillimeter imaging and spatiotemporally encoded sequences. Attributed to its automation, accuracy, and broad applicability, Auto-b is helpful for developers of diffusion sequences.