Sampling strategies for subsampled segmented EPI PRF thermometry in MR guided high intensity focused ultrasound

Abstract

Purpose: To investigate k-space subsampling strategies to achieve fast, large field-of-view (FOV) temperature monitoring using segmented echo planar imaging (EPI) proton resonance frequency shift thermometry for MR guided high intensity focused ultrasound (MRgHIFU) applications. Methods: Five different k-space sampling approaches were investigated, varying sample spacing (equally vs nonequally spaced within the echo train), sampling density (variable sampling density in zero, one, and two dimensions), and utilizing sequential or centric sampling. Three of the schemes utilized sequential sampling with the sampling density varied in zero, one, and two dimensions, to investigate sampling the k-space center more frequently. Two of the schemes utilized centric sampling to acquire the k-space center with a longer echo time for improved phase measurements, and vary the sampling density in zero and two dimensions, respectively. Phantom experiments and a theoretical point spread function analysis were performed to investigate their performance. Variable density sampling in zero and two dimensions was also implemented in a non-EPI GRE pulse sequence for comparison. All subsampled data were reconstructed with a previously described temporally constrained reconstruction (TCR) algorithm. Results: The accuracy of each sampling strategy in measuring the temperature rise in the HIFU focal spot was measured in terms of the root-mean-square-error (RMSE) compared to fully sampled "truth." For the schemes utilizing sequential sampling, the accuracy was found to improve with the dimensionality of the variable density sampling, giving values of 0.65• C, 0.49 • C, and 0.35• C for density variation in zero, one, and two dimensions, respectively. The schemes utilizing centric sampling were found to underestimate the temperature rise, with RMSE values of 1.05• C and 1.31• C, for variable density sampling in zero and two dimensions, respectively. Similar subsampling schemes with variable density sampling implemented in zero and two dimensions in a non-EPI GRE pulse sequence both resulted in accurate temperature measurements (RMSE of 0.70• C and 0.63 • C, respectively). With sequential sampling in the described EPI implementation, temperature monitoring over a 192 × 144 × 135 mm 3 FOV with a temporal resolution of 3.6 s was achieved, while keeping the RMSE compared to fully sampled "truth" below 0.35• C. Conclusions: When segmented EPI readouts are used in conjunction with k-space subsampling for MR thermometry applications, sampling schemes with sequential sampling, with or without variable density sampling, obtain accurate phase and temperature measurements when using a TCR reconstruction algorithm. Improved temperature measurement accuracy can be achieved with variable density sampling. Centric sampling leads to phase bias, resulting in temperature underestimations.

Multiecho pseudo‐golden angle stack of stars thermometry with high spatial and temporal resolution using k‐space weighted image contrast

“…The KWIC window used to reconstruct nearly all KWIC images in this work had 13 projections in the center, and each successive ring used the next higher Fibonacci number of projections up to 377 in the outermost ring. The outer radius of each ring (as a fraction of the total radius) was determined by the ratio of the number of projections in the ring and the number in the outermost ring: (13,21,34,55,89,144, 233, 377)/377 ¼ (0.035, 0.056, 0.09, 0.146, 0.236, 0.382, 0.618, 1). The sliding window was advanced 13 projections between each reconstructed time point.…”

“…3D MR thermometry can overcome many of the FOV, partial volume, and coverage gap limitations, which are inherent in 2D imaging but unfortunately, standard 3D sequences typically require too much time to acquire kspace to be clinically viable. Temporal resolution can be increased by methods involving undersampling, such as temporally constrained reconstruction (16), model predictive filtering (17), Kalman filtering (18), parallel imaging (19), or using a sequence designed for increased speed such as segmented echo-planar imaging (seg-EPI) (20,21).…”

Prior image based temporally constrained reconstruction algorithm for magnetic resonance guided high intensity focused ultrasound

“…For a detailed analysis of various sampling strategies, see Ref. 24. The image reconstruction was carried out on a machine with an Intel Xeon dual six core processor with a processor speed of 2.66 GHz and memory of 64 GB.…”

Correcting heat‐induced chemical shift distortions in proton resonance frequency‐shift thermometry

“…At 3 Tesla, the PRF changes 1.27 Hz/ C, so with a 29 Hz pixel BW the hot spot will shift by one pixel with a 22.7 C temperature rise; temperature increases in MRgFUS ablations are typically 20-30 C. Temperature magnitude errors also arise as hotter pixels blur into cooler pixels. Low pixel BWs in the phaseencoded dimension are also common in echo-planar imaging (EPI) sequences used to accelerate volumetric temperature scans (12)(13)(14)(15). Multishot acquisition schemes can reduce these errors to some degree, but do not eliminate them entirely (9,16).…”