Multiparametric fat–water separation method for fast chemical‐shift imaging guidance of thermal therapies
Authors: Lin Jonathan S. , Hwang Ken‐Pin , Jackson Edward F. , Hazle John D. , Jason Stafford R. , Taylor Brian A. 
Published: September 2013

Purpose:

A k‐means‐based classification algorithm is investigated to assess suitability for rapidly separating and classifying fat/water spectral peaks from a fast chemical shift imaging technique for magnetic resonance temperature imaging. Algorithm testing is performed in simulated mathematical phantoms and agar gel phantoms containing mixed fat/water regions.

Methods:

Proton resonance frequencies (PRFs), apparent spin‐spin relaxation (T2*) times, and T1‐weighted (T1‐W) amplitude values were calculated for each voxel using a single‐peak autoregressive moving average (ARMA) signal model. These parameters were then used as criteria for k‐means sorting, with the results used to determine PRF ranges of each chemical species cluster for further classification. To detect the presence of secondary chemical species, spectral parameters were recalculated when needed using a two‐peak ARMA signal model during the subsequent classification steps. Mathematical phantom simulations involved the modulation of signal‐to‐noise ratios (SNR), maximum PRF shift (MPS) values, analysis window sizes, and frequency expansion factor sizes in order to characterize the algorithm performance across a variety of conditions. In agar, images were collected on a 1.5T clinical MR scanner using acquisition parameters close to simulation, and algorithm performance was assessed by comparing classification results to manually segmented maps of the fat/water regions.

Results:

Performance was characterized quantitatively using the Dice Similarity Coefficient (DSC), sensitivity, and specificity. The simulated mathematical phantom experiments demonstrated good fat/water separation depending on conditions, specifically high SNR, moderate MPS value, small analysis window size, and low but nonzero frequency expansion factor size. Physical phantom results demonstrated good identification for both water (0.997 ± 0.001, 0.999 ± 0.001, and 0.986 ± 0.001 for DSC, sensitivity, and specificity, respectively) and fat (0.763 ± 0.006, 0.980 ± 0.004, and 0.941 ± 0.002 for DSC, sensitivity, and specificity, respectively). Temperature uncertainties, based on PRF uncertainties from a 5 × 5‐voxel ROI, were 0.342 and 0.351 °C for pure and mixed fat/water regions, respectively. Algorithm speed was tested using 25 × 25‐voxel and whole image ROIs containing both fat and water, resulting in average processing times per acquisition of 2.00 ± 0.07 s and 146 ± 1 s, respectively, using uncompiled MATLAB scripts running on a shared CPU server with eight Intel XeonTM E5640 quad‐core processors (2.66 GHz, 12 MB cache) and 12 GB RAM.

Conclusions:

Results from both the mathematical and physical phantom suggest the k‐means‐based classification algorithm could be useful for rapid, dynamic imaging in an ROI for thermal interventions. Successful separation of fat/water information would aid in reducing errors from the nontemperature sensitive fat PRF, as well as potentially facilitate using fat as an internal reference for PRF shift thermometry when appropriate. Additionally, the T1‐W or R2* signals may be used for monitoring temperature in surrounding adipose tissue.