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.
The authors investigated the performance of the iterative Steiglitz–McBride (SM) algorithm on an autoregressive moving average (ARMA) model of signals from a fast, sparsely sampled, multiecho, ...chemical shift imaging (CSI) acquisition using simulation, phantom, ex vivo, and in vivo experiments with a focus on its potential usage in magnetic resonance (MR)-guided interventions. The ARMA signal model facilitated a rapid calculation of the chemical shift, apparent spin-spin relaxation time
(
T
2
∗
)
, and complex amplitudes of a multipeak system from a limited number of echoes
(
≤
16
)
. Numerical simulations of one- and two-peak systems were used to assess the accuracy and uncertainty in the calculated spectral parameters as a function of acquisition and tissue parameters. The measured uncertainties from simulation were compared to the theoretical Cramer–Rao lower bound (CRLB) for the acquisition. Measurements made in phantoms were used to validate the
T
2
∗
estimates and to validate uncertainty estimates made from the CRLB. We demonstrated application to real-time MR-guided interventions ex vivo by using the technique to monitor a percutaneous ethanol injection into a bovine liver and in vivo to monitor a laser-induced thermal therapy treatment in a canine brain. Simulation results showed that the chemical shift and amplitude uncertainties reached their respective CRLB at a signal-to-noise ratio
(
SNR
)
≥
5
for echo train lengths
(
ETLs
)
≥
4
using a fixed echo spacing of 3.3 ms.
T
2
∗
estimates from the signal model possessed higher uncertainties but reached the CRLB at larger SNRs and/or ETLs. Highly accurate estimates for the chemical shift
(
<
0.01
ppm
)
and amplitude
(
<
1.0
%
)
were obtained with
≥
4
echoes and for
T
2
∗
(
<
1.0
%
)
with
≥
7
echoes. We conclude that, over a reasonable range of SNR, the SM algorithm is a robust estimator of spectral parameters from fast CSI acquisitions that acquire
≤
16
echoes for one- and two-peak systems. Preliminary ex vivo and in vivo experiments corroborated the results from simulation experiments and further indicate the potential of this technique for MR-guided interventional procedures with high spatiotemporal resolution
∼
1.6
×
1.6
×
4
mm
3
in
≤
5
s
.
A fast chemical shift imaging (CSI) technique based on a multiple gradient-recalled acquisition using a small number of echoes with intentional aliasing of the reference lipid peak is studied to ...determine its feasibility for temperature monitoring. Simulations were implemented to find parameters where the lipid and water peaks can be measured using a Fourier-based peak fitting approach as well as using an innovative autoregressive moving average technique. A phantom consisting of
50
%
mayonnaise/
50
%
lemon juice was calibrated to temperature and compared to literature values. A porcine kidney was treated ex vivo with an external laser and imaged with the CSI technique with comparisons to temperature readings from a fluoroptic monitoring system and complex phase difference (CPD) calculations. To demonstrate the technique in vivo, a Balb/c mouse with a CT26 xenograft in the subcutaneous lower back was treated using gold-coated, silica-core nanoshells heated with an 808 nm interstitial laser. Compared to standard CPD techniques using a two-dimensional fast spoiled gradient recalled echo, this technique maintains spatiotemporal resolution, has high signal-to-noise ratio and accuracy over a wide range of
T
2
*
tissue values, can separate water and lipid signals, and additionally can use the lipid peak, when present, as an internal reference.
Abstract Sampling water and fat signals symmetrically (i.e., at 0° and 180° relative phase angles) in a dual-echo Dixon technique offers high intrinsic tolerance to phase fluctuations in ...postprocessing and maximum signal-to-noise performance for the separated water and fat images. However, identification of which image is water and which image is fat after their separation is not possible based on the phase information alone. In this work, we proposed a semiempirical automatic image identification method that is based on the intrinsic asymmetry between the water and fat chemical shift spectra. Specifically, the approximately bimodal feature of the fat spectra and the observation that most in vivo tissues are either predominantly water or predominantly fat are used to construct a spectrum-based algorithm. Additional refinement is accomplished by considering the spatial distribution of the tissues that may have a coexistence of water and fat. The final improved algorithm was tested on a total of 131 three-dimensional patient datasets collected from different scanners and found to yield correct water and fat identification in all datasets.
Purpose To determine if tumor necrosis by pretreatment breast MRI and its quantitative imaging characteristics are associated with response to NAST in TNBC. Methods This retrospective study included ...85 TNBC patients (mean age 51.8 ± 13 years) with MRI before NAST and definitive surgery during 2010-2018. Each MRI included T2-weighted, diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging. For each index carcinoma, total tumor volume including necrosis (TTV), excluding necrosis (TV), and the necrosis-only volume (NV) were segmented on early-phase DCE subtractions and DWI images. NV and %NV were calculated. Percent enhancement on early and late phases of DCE and apparent diffusion coefficient were extracted from TTV, TV, and NV. Association between necrosis with pathological complete response (pCR) was assessed using odds ratio (OR). Multivariable analysis was used to evaluate the prognostic value of necrosis with T stage and nodal status at staging. Mann-Whitney U tests and area under the curve (AUC) were used to assess performance of imaging metrics for discriminating pCR vs non-pCR. Results Of 39 patients (46%) with necrosis, 17 had pCR and 22 did not. Necrosis was not associated with pCR (OR, 0.995; 95% confidence interval CI 0.4-2.3) and was not an independent prognostic factor when combined with T stage and nodal status at staging (P = 0.46). None of the imaging metrics differed significantly between pCR and non-pCR in patients with necrosis (AUC < 0.6 and P > 0.40). Conclusion No significant association was found between necrosis by pretreatment MRI or the quantitative imaging characteristics of tumor necrosis and response to NAST in TNBC.
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