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Sampling strategies for subsampled segmented EPI PRF thermometry in MR guided high intensity focused ultrasound
Author(s) -
Odéen Henrik,
Todd Nick,
Diakite Mahamadou,
Minalga Emilee,
Payne Allison,
Parker Dennis L.
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4892171
Subject(s) - sampling (signal processing) , imaging phantom , mean squared error , mathematics , algorithm , statistics , physics , optics , detector
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.