Neta Stern1, Dvir Radunsky1, Tamar Blumenfeld-Katzir1, and Noam Ben-Eliezer1,2,3
1Bio-medical Engineering, Tel Aviv University, Tel Aviv, Israel, 2Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, 3Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States
Synopsis
Quantitative T2
mapping using Bloch simulations offers high mapping accuracy, albeit may suffer
from reduced precision due to noisy data when operating at low SNR.
In this
work we tested the utility of PCA-based complex image denoising for increasing
T2 mapping precision. Mapping was done using the echo modulation curve
algorithm. Denoising was tested on phantom and in vivo scans. Comparing T2
maps before and after PCA complex image denoising showed an increase in T2
precision with no apparent loss of spatial resolution.
Introduction
The echo modulation curve (EMC)
algorithm allows highly accurate mapping of T2 values in vivo1,2. The algorithm is based on fitting experimental curves extracted
from multi spin echo (MSE) protocols to a dictionary of curves generated
using Bloch simulations3. Scans performed at low signal to noise ratios (SNR), e.g., using large
matrix sizes, or thin slices, generate noisy data, which reduces T2 fitting
accuracy. Principal component analysis (PCA) image denoising has been
previously shown to reduce noise and increase accuracy and precision of
bi-exponential T2 mapping4. In this work we tested the utility of PCA-based complex
image denoising, for increasing T2 mapping precision at low SNR. Methods
Phantom Scans
ISMRM/NIST system phantom (HPD Inc.) was
scanned 10 times using a standard MSE sequence on a 3T Siemens scanner. Each
scan used a different combination of slice thickness and matrix size: slice
thickness = 1 or 2 mm, matrix size = 100x100; 192x192; 256x256; 380x380;
512x512. Remaining scan parameters were: NEchoes=25; TE/TR=13/3000
ms; FOV = 160x160 mm2. No undersampling was applied.
In vivo Scans
Brain imaging was done using a standard MSE
sequence on a 3T Siemens scanner. Scan parameters were: slice thickness=3 mm,
matrix size=216x180, FOV=216x180 mm2, TE/TR=10/3000 ms, NEchoes=15,
no undersampling was applied.
Data Analysis
PCA was applied on a sliding window over a set
of complex images reconstructed from the images’ raw data. For each window, only
a subset of principal components was kept, based on the distribution of the
eigenvalues of the data covariance matrix. Detailed description of the PCA
algorithm used in this study can be found at Does et al4 and Veraart et al5. Windows of 5x5 and 7x7 were tested. EMC fitting was done before,
and after the denoising process, producing two sets of T2 maps. For
the phantom T2 maps, T2 mean value, standard deviation (SD)
and coefficient of variation (CV) were calculated for selected internal
region of interests (ROIs) inside 7 spheres.Results
Figure 1 shows T2 maps of the HPD phantom, before and after applying PCA complex image
denoising using a 5x5 and a 7x7 sliding window.
Figure 2 describes T2 mean
value, SD and CV for the seven
marked spheres in the attached phantom image. A consistent decrease in both SD
and CV appears in all 7 spheres when using PCA complex image denoising with
either a 5x5 or a 7x7 window.
Figure 3 shows a P-map: the number of PCs
found per each pixel, divided by the number of windows including that pixel (extracted
from data of a single channel, out of a total of 20 receiver channels). We note
that the number of principal components appears to increase in pixels located on
edges.
Figure
4 demonstrates the performance of the algorithm on in vivo brain scan,
exemplifying the effectiveness of the denoising process. We note that no loss
of spatial resolution is apparent in these images.Discussion
T2 precision appears to increase in both phantom and in vivo scan after applying
PCA complex image denoising. Results of in vivo scans show no significant loss
of resolution or blurring of edges. The ability of the
PCA complex image denoising to maintain high resolution can be explained by the
selectivity of the principle components calculated per window according to its
content.Acknowledgements
ISF Grant 2009/17References
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