Temporal differences (TED) compressed sensing: a method for fast MRgHIFU temperature imaging. (8th July 2020)

• Record Type: Journal Article
• Authors: Shimron, Efrat; Grissom, William; Azhari, Haim
• Is Part Of: NMR in biomedicine. Volume 33:Number 9(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-08
• Subjects: animal model study -- compressed sensing -- human study -- MR guided focused ultrasound -- parallel imaging -- reconstruction; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4352
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract : This work proposes the Temporal Differences (TED) Compressed Sensing (CS) method for accelerating thermal monitoring in MR‐guided High‐Intensity Focused Ultrasound (MRgHIFU) treatments. TED combines k‐space subsampling, parallel imaging, and a unique CS recovery of the temporal differences between pre‐heating and post‐heating multi‐coil data. TED was validated through retrospective experiments with (i) two phantom datasets acquired with 1.5 T and 3 T MRgHIFU systems from different vendors, (ii) data from an in vivo animal model experiment, and (iii) four datasets from clinical in vivo MRgHIFU treatments of prostate cancer in humans. TED produced highly accurate temperature change maps from subsampled k‐space data for all datasets. For the clinical in vivo data, an analysis of 105 time frames showed that the average TED reconstruction error is 1.06‐1.67 °C. Furthermore, TED consistently outperforms two state‐of‐the‐art methods, l 1 ‐SPIRiT and the K‐space Hybrid Method, and offers errors that are significantly lower, by 29% or more. Moreover, TED offers robust performance over a range of its tunable parameters, stability across MRgHIFU systems from different vendors, and a short runtime of 1.7 s. In summary, TED enables k‐space subsampling while retaining high‐temperature mapping accuracy. Abstract : This work proposes the Temporal Differences (TED) method for accelerating temperature monitoring in MRgHIFU treatments. TED combines k‐space subsampling, parallel imaging and Compressed Sensing, and solves an optimization problem that exploits the natural spatio‐temporal sparsity of MRgHIFU data. TED was validated thoroughly by experiments with phantom datasets from different vendors, an animal model, and in vivo clinical human data. The results demonstrate that TED offers high accuracy with short runtimes, and that it significantly outperforms l 1 ‐SPIRiT and the recent K‐space Hybrid Method.