SNR AND BANDING ARTIFACT REDUCTION ANALYSIS OF PHASE...
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Synopsis Balanced steady-state free precession (bSSFP) provides high signal in short scan times. A new method for combining four or more phase-cycled bSSFP acquisitions was recently proposed that uses an elliptical signal model (ESM) of the bSSFP signal. In this study, we compare the SNR per- formance and effectiveness at reducing banding artifact of the ESM, com- plex sum, and sum-of-squares techniques across a range of T 1 and T 2 values, flip angles, and base SNR levels. Although ESM produces near perfect band removal in high SNR situations, it breaks down for certain tissues and in low SNR situations. Purpose Balanced steady-state free precession (bSSFP) provides high signal in short scan times and useful contrast for visualizing and characterizing many tis- sues in the body. Despite these benefits, bSSFP scans are susceptible to B 0 in- homogeneity, which causes dark bands of signal loss across the images at cer- tain off-resonance frequencies (“banding artifacts”). To mitigate these arti- facts, multiple bSSFP acquisitions are often combined, each with a different RF phase increment from TR to TR. This effectively shifts the off-resonance banding artifact position to different locations for each acquisition. These images can then be combined to reduce banding artifact in the final recon- structed image. The individual acquisitions are commonly combined using a sum of squares (SOS) or complex sum (CS) of the constituent images [1]. A new method for combining four or more phase-cycled bSSFP acquisitions was recently proposed by Xiang and Hoff that uses an elliptical signal model (ESM) of the bSSFP signal [2]. It achieves near perfect banding artifact re- moval under certain conditions. However, an analysis of the SNR perfor- mance and banding reduction of the new ESM technique across a range of tissue types, flip angles, and base SNR levels has not yet been performed, nor has the performance of the new technique been quantitatively compared to the SOS and CS methods. In this study, we follow the statistical analysis framework outlined in [1] to compare the SNR performance and effectiveness at reducing banding arti- fact of the ESM, CS, and SOS techniques across a range of T 1 and T 2 values, flip angles, and base SNR levels. Methods Simulations: A Monte-Carlo simulation was performed as described in [1] to determine how noise propagates when using SOS, CS, and ESM to com- bine four phase-cycled images. The simulation was performed at flip angles ranging from 10 to 90 degrees in increments of 10 degrees, across a range of tissue T 1 and T 2 values. In addition to the Monte-Carlo simulations, a simu- lated phantom image composed of 7 simulated tissues similar to blood, gray matter, white matter, fat, synovial fluid, cartilage, and muscle (T 1 /T 2 = 1500/200, 1800/100, 1000/70, 300/85, 4800/325, 1200/30, 1500/32 ms) was used [3]. A TR/TE of 10/5 ms was assumed. Base SNR (as defined in [1]) was varied from 5 to 30 in increments of 5. SNR performance and residual band- ing artifact were determined from the simulations, again using the tech- niques outlined in [1]. Actual Data: Four 2D bSSFP phase-cycled acquisitions were acquired (phase increments of 0, 90, 180, and 270 degrees) on a 3T Siemens Trio scan- ner of both a water/oil phantom and the knee of a normal volunteer. Scan parameters for both scans were: TR/TE = 10/5 ms, α = 30 degrees, FOV = 200 mm, slice thickness = 5 mm, and matrix = 256x256. The data was in each case combined with all three techniques (SOS, CS, and ESM), and SNR per- formance compared to the performance predicted by simulation. Results Simulations: The ESM technique achieves similar SNR performance to the CS technique, while the SOS technique resulted in the greatest SNR across all tissues and parameters tested. A summary of these results is shown in Figure 1. When looking at banding artifact reduction, the new ESM tech- nique yields by far the best performance when base SNR is above 30 for most tissues (Figure 2). However, for certain tissues and at low SNR for many tissues, the ESM technique actually performs worse than both the CS and SOS techniques in reducing banding artifact. This can be understood by looking at the shape of the ellipses in the elliptical signal model across a range of tissue T 1 and T 2 values (Figure 3). When the ellipses become too small the magnitude of the noise is comparable to the size of the ellipses in the complex plane, so the signal level estimated by the ESM can vary signifi- cantly. Actual Data: Corrected SNR values for the water/oil phantom can be seen in Table 1 for each of the combination methods. The real data matches the simulation results: SOS has the highest SNR, whereas ESM and CS have similar SNR values. In vivo knee data can be seen in Figure 4. No residual banding can be seen in any of the methods in this experiment. Discussion Although ESM produces near perfect band removal in high SNR situations, it breaks down for certain tissues and in low SNR situations. In extremely low SNR cases SOS can outperform CS and ESM in banding reduction. In these cases SOS results in the highest SNR and least banding artifact. References [1] N. K. Bangerter, et al., MRM, vol. 51, no. 5, pp. 1038–1047, 2004. [2] Q. S. Xiang et al., MRM., vol. 71, no. 3, pp. 927–933, 2014. [3] G. J. Stanisz, et al., MRM., vol. 54, no. 3, pp. 507–512, 2005. Figure 3: Plots of tissue el- lipses for various T 1 and T 2 values. Small T 2 values narrow the ellipse, while small T 1 values elongate the ellipse. Table 1: Corrected SNR comparison of CS, SOS, and ESM from a wa- ter/oil phantom. SOS has the highest SNR, and CS and ESM have similar SNR values. Figure 4: Comparison of CS, SOS, and ESM in a knee. No residual banding can be seen in any of the methods in this experiment. SNR Fat Water Complex Sum 95.9 39.6 Sum of Squares 150.3 60.1 Elliptical Signal Model 86.4 36.7 Figure 1: Average SNR as a function of flip angle for cartilage, gray matter, and fat. SOS produces the highest SNR values, while CS and ESM result in similar SNR values across all the simulated tissues. Figure 2: Percent ripple as a function of base SNR for cartilage, gray matter, and fat. ESM performs worse when the signal ellipse (see Figure 3) is small compared to the noise. Certain T 1 and T 2 combinations, such as in cartilage, yield these small ellipses. SNR AND BANDING ARTIFACT REDUCTION ANALYSIS OF PHASE-CYCLED ELLIPTICAL SIGNAL MODEL bSSFP Steven T. Whitaker 1 , Meredith Taylor 1 , Haonan Wang 1 , and Neal K. Bangerter 1 1 Electrical Engineering, Brigham Young University, Provo, UT, United States 0 20 40 60 80 100 0 2 4 6 8 10 12 14 16 18 20 22 Cartilage Flip Angle in Degrees Average SNR CS SOS ESM 0 20 40 60 80 100 0 2 4 6 8 10 12 14 16 18 20 22 Gray Matter Flip Angle in Degrees 0 20 40 60 80 100 0 2 4 6 8 10 12 14 16 18 20 22 Fat Flip Angle in Degrees 5 10 15 20 25 30 0 0.5 1 1.5 Cartilage Flip Angle = 30 Degrees Percent Ripple CS SOS ESM 5 10 15 20 25 30 0 0.5 1 1.5 2 2.5 3 Gray Matter 1 5 10 15 20 25 30 0 2 4 6 8 10 12 14 16 Fat 1 5 10 15 20 25 30 0 0.1 0.2 0.3 0.4 0.5 Base SNR Flip Angle = 60 Degrees Percent Ripple 5 10 15 20 25 30 0 0.1 0.2 0.3 0.4 0.5 Base SNR 1 5 10 15 20 25 30 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Base SNR 1 2 0 0.2 0.4 4 0 0.4 1800 ms 0 0.2 0.4 4 0 0.4 0 0.2 0.4 4 0 0.4 0 0.2 0.4 4 0 0.4 0 0.2 0.4 4 0 0.4 1300 ms 0 0.2 0.4 4 0 0.4 0 0.2 0.4 4 0 0.4 0 0.2 0.4 4 0 0.4 0 0.2 0.4 4 0 0.4 800 ms 0 0.2 0.4 4 0 0.4 0 0.2 0.4 4 0 0.4 0 0.2 0.4 4 0 0.4 0 0.2 0.4 4 0 0.4 T 1 300 ms 30 ms 0 0.2 0.4 4 0 0.4 T 80 ms 0 0.2 0.4 4 0 0.4 130 ms 0 0.2 0.4 4 0 0.4 180 ms Real Imaginary Cartilage Fat White Matter Gray Matter Blood Complex Sum Single Phase Cycle Sum of Squares Elliptical Signal Model
Transcript of SNR AND BANDING ARTIFACT REDUCTION ANALYSIS OF PHASE...
SynopsisBalanced steady-state free precession (bSSFP) provides high signal in short scan times. A new method for combining four or more phase-cycled bSSFP acquisitions was recently proposed that uses an elliptical signal model (ESM) of the bSSFP signal. In this study, we compare the SNR per-formance and effectiveness at reducing banding artifact of the ESM, com-plex sum, and sum-of-squares techniques across a range of T1 and T2 values, flip angles, and base SNR levels. Although ESM produces near perfect band removal in high SNR situations, it breaks down for certain tissues and in low SNR situations.
PurposeBalanced steady-state free precession (bSSFP) provides high signal in short scan times and useful contrast for visualizing and characterizing many tis-sues in the body. Despite these benefits, bSSFP scans are susceptible to B0 in-homogeneity, which causes dark bands of signal loss across the images at cer-tain off-resonance frequencies (“banding artifacts”). To mitigate these arti-facts, multiple bSSFP acquisitions are often combined, each with a different RF phase increment from TR to TR. This effectively shifts the off-resonance banding artifact position to different locations for each acquisition. These images can then be combined to reduce banding artifact in the final recon-structed image. The individual acquisitions are commonly combined using a sum of squares (SOS) or complex sum (CS) of the constituent images [1]. A new method for combining four or more phase-cycled bSSFP acquisitions was recently proposed by Xiang and Hoff that uses an elliptical signal model (ESM) of the bSSFP signal [2]. It achieves near perfect banding artifact re-moval under certain conditions. However, an analysis of the SNR perfor-mance and banding reduction of the new ESM technique across a range of tissue types, flip angles, and base SNR levels has not yet been performed, nor has the performance of the new technique been quantitatively compared to the SOS and CS methods.
In this study, we follow the statistical analysis framework outlined in [1] to compare the SNR performance and effectiveness at reducing banding arti-fact of the ESM, CS, and SOS techniques across a range of T1 and T2 values, flip angles, and base SNR levels.
MethodsSimulations: A Monte-Carlo simulation was performed as described in [1] to determine how noise propagates when using SOS, CS, and ESM to com-bine four phase-cycled images. The simulation was performed at flip angles ranging from 10 to 90 degrees in increments of 10 degrees, across a range of tissue T1 and T2 values. In addition to the Monte-Carlo simulations, a simu-lated phantom image composed of 7 simulated tissues similar to blood, gray matter, white matter, fat, synovial fluid, cartilage, and muscle (T1/T2 = 1500/200, 1800/100, 1000/70, 300/85, 4800/325, 1200/30, 1500/32 ms) was used [3]. A TR/TE of 10/5 ms was assumed. Base SNR (as defined in [1]) was varied from 5 to 30 in increments of 5. SNR performance and residual band-ing artifact were determined from the simulations, again using the tech-niques outlined in [1].
Actual Data: Four 2D bSSFP phase-cycled acquisitions were acquired (phase increments of 0, 90, 180, and 270 degrees) on a 3T Siemens Trio scan-ner of both a water/oil phantom and the knee of a normal volunteer. Scan parameters for both scans were: TR/TE = 10/5 ms, α = 30 degrees, FOV = 200 mm, slice thickness = 5 mm, and matrix = 256x256. The data was in each case combined with all three techniques (SOS, CS, and ESM), and SNR per-formance compared to the performance predicted by simulation.
ResultsSimulations: The ESM technique achieves similar SNR performance to the CS technique, while the SOS technique resulted in the greatest SNR across all tissues and parameters tested. A summary of these results is shown in Figure 1. When looking at banding artifact reduction, the new ESM tech-nique yields by far the best performance when base SNR is above 30 for most tissues (Figure 2). However, for certain tissues and at low SNR for many tissues, the ESM technique actually performs worse than both the CS and SOS techniques in reducing banding artifact. This can be understood by looking at the shape of the ellipses in the elliptical signal model across a range of tissue T1 and T2 values (Figure 3). When the ellipses become too small the magnitude of the noise is comparable to the size of the ellipses in the complex plane, so the signal level estimated by the ESM can vary signifi-cantly.
Actual Data: Corrected SNR values for the water/oil phantom can be seen in Table 1 for each of the combination methods. The real data matches the simulation results: SOS has the highest SNR, whereas ESM and CS have similar SNR values. In vivo knee data can be seen in Figure 4. No residual banding can be seen in any of the methods in this experiment.
DiscussionAlthough ESM produces near perfect band removal in high SNR situations, it breaks down for certain tissues and in low SNR situations. In extremely low SNR cases SOS can outperform CS and ESM in banding reduction. In these cases SOS results in the highest SNR and least banding artifact.
References[1] N. K. Bangerter, et al., MRM, vol. 51, no. 5, pp. 1038–1047, 2004.
[2] Q. S. Xiang et al., MRM., vol. 71, no. 3, pp. 927–933, 2014.
[3] G. J. Stanisz, et al., MRM., vol. 54, no. 3, pp. 507–512, 2005.
Figure 3: Plots of tissue el-lipses for various T1 and T2 values. Small T2 values narrow the ellipse, while small T1 values elongate the ellipse.
Table 1: Corrected SNR comparison of CS, SOS, and ESM from a wa-ter/oil phantom. SOS has the highest SNR, and CS and ESM have similar SNR values.
Figure 4: Comparison of CS, SOS, and ESM in a knee. No residual banding can be seen in any of the methods in this experiment.
SNR Fat Water
Complex Sum 95.9 39.6Sum of Squares 150.3 60.1Elliptical Signal Model 86.4 36.7
Figure 1: Average SNR as a function of flip angle for cartilage, gray matter, and fat. SOS produces the highest SNR values, while CS and ESM result in similar SNR values across all the simulated tissues.
Figure 2: Percent ripple as a function of base SNR for cartilage, gray matter, and fat. ESM performs worse when the signal ellipse (see Figure 3) is small compared to the noise. Certain T1 and T2 combinations, such as in cartilage, yield these small ellipses.
SNR AND BANDING ARTIFACT REDUCTION ANALYSIS OF PHASE-CYCLED ELLIPTICAL SIGNAL MODEL bSSFPSteven T. Whitaker1, Meredith Taylor1, Haonan Wang1, and Neal K. Bangerter1
1Electrical Engineering, Brigham Young University, Provo, UT, United States
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