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Optimization of tagged MRI for quantification of liver stiffness using computer simulated data.

Monti S, Palma G, Ragucci M, Mannelli L, Mancini M, Prinster A - PLoS ONE (2014)

Bottom Line: We reproduced a study of cardiac-induced strain in the liver at 3T and simulated tagged MR images with different grid tag patterns to evaluate the performance of the Harmonic Phase (HARP) image analysis method and its dependence on the parameters of tag spacing and grid angle.The Partial Volume Effect (PVE), T1 relaxation, and different levels of noise were taken into account.The accuracy of the estimation decreased for increasing levels of added noise; as the level increased, the improved estimation caused by decreasing the tag spacing tended to zero.

View Article: PubMed Central - PubMed

Affiliation: IRCCS SDN Foundation, Naples, Italy.

ABSTRACT
The heartbeat has been proposed as an intrinsic source of motion that can be used in combination with tagged Magnetic Resonance Imaging (MRI) to measure displacements induced in the liver as an index of liver stiffness. Optimizing a tagged MRI acquisition protocol in terms of sensitivity to these displacements, which are in the order of pixel size, is necessary to develop the method as a quantification tool for staging fibrosis. We reproduced a study of cardiac-induced strain in the liver at 3T and simulated tagged MR images with different grid tag patterns to evaluate the performance of the Harmonic Phase (HARP) image analysis method and its dependence on the parameters of tag spacing and grid angle. The Partial Volume Effect (PVE), T1 relaxation, and different levels of noise were taken into account. Four displacement fields of increasing intensity were created and applied to the tagged MR images of the liver. These fields simulated the deformation at different liver stiffnesses. An Error Index (EI) was calculated to evaluate the estimation accuracy for various parameter values. In the absence of noise, the estimation accuracy of the displacement fields increased as tag spacings decreased. EIs for each of the four displacement fields were lower at 0° and the local minima of the EI were found to correspond to multiples of pixel size. The accuracy of the estimation decreased for increasing levels of added noise; as the level increased, the improved estimation caused by decreasing the tag spacing tended to zero. The optimal tag spacing turned out to be a compromise between the smallest tag period that is a multiple of the pixel size and is achievable in a real acquisition and the tag spacing that guarantees an accurate liver displacement measure in the presence of realistic levels of noise.

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Ground truth and computed displacement maps.(a) Map of the deformation field Dg2 applied in both the x and y directions (Dg2x  =  Dg2y  =  [−1.2, 1.7] pixel) of the undeformed image. (b) Estimated displacement map in the x direction (Dcx) and (c) in the y direction (Dcy), obtained from tagged MRI images with grid angle of 0°, tag spacing of 5 pixels, and deformed by Dg2. (d) Estimated displacement maps in the x direction (Dcx) and (e) in the y direction (Dcy) obtained from tagged MRI images with a grid angle of 0°, tag spacing of 5 pixels, and deformed by Dg2 in the presence of a noise level of 3.5%.
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pone-0111852-g002: Ground truth and computed displacement maps.(a) Map of the deformation field Dg2 applied in both the x and y directions (Dg2x  =  Dg2y  =  [−1.2, 1.7] pixel) of the undeformed image. (b) Estimated displacement map in the x direction (Dcx) and (c) in the y direction (Dcy), obtained from tagged MRI images with grid angle of 0°, tag spacing of 5 pixels, and deformed by Dg2. (d) Estimated displacement maps in the x direction (Dcx) and (e) in the y direction (Dcy) obtained from tagged MRI images with a grid angle of 0°, tag spacing of 5 pixels, and deformed by Dg2 in the presence of a noise level of 3.5%.

Mentions: To obtain the deformed images, four 2D continuous displacements maps (Figure 2a) of increasing amplitude were created by generating two smooth and well behaved functions for each amplitude using MATLAB's “peaks” function to describe the horizontal and vertical displacements, respectively (ground truth Dg1x  =  Dg1y  =  [−0.4, 0.6] pixels; Dg2x  =  Dg2y  =  [−1.2, 1.7] pixels; Dg3x  =  Dg3y  =  [−2.0, 2.8] pixels; Dg4x  =  Dg4y  =  [−2.7, 3.9] pixels). For each displacement map, the corresponding image was generated by interpolating the original image on the deformed grids.


Optimization of tagged MRI for quantification of liver stiffness using computer simulated data.

Monti S, Palma G, Ragucci M, Mannelli L, Mancini M, Prinster A - PLoS ONE (2014)

Ground truth and computed displacement maps.(a) Map of the deformation field Dg2 applied in both the x and y directions (Dg2x  =  Dg2y  =  [−1.2, 1.7] pixel) of the undeformed image. (b) Estimated displacement map in the x direction (Dcx) and (c) in the y direction (Dcy), obtained from tagged MRI images with grid angle of 0°, tag spacing of 5 pixels, and deformed by Dg2. (d) Estimated displacement maps in the x direction (Dcx) and (e) in the y direction (Dcy) obtained from tagged MRI images with a grid angle of 0°, tag spacing of 5 pixels, and deformed by Dg2 in the presence of a noise level of 3.5%.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4216130&req=5

pone-0111852-g002: Ground truth and computed displacement maps.(a) Map of the deformation field Dg2 applied in both the x and y directions (Dg2x  =  Dg2y  =  [−1.2, 1.7] pixel) of the undeformed image. (b) Estimated displacement map in the x direction (Dcx) and (c) in the y direction (Dcy), obtained from tagged MRI images with grid angle of 0°, tag spacing of 5 pixels, and deformed by Dg2. (d) Estimated displacement maps in the x direction (Dcx) and (e) in the y direction (Dcy) obtained from tagged MRI images with a grid angle of 0°, tag spacing of 5 pixels, and deformed by Dg2 in the presence of a noise level of 3.5%.
Mentions: To obtain the deformed images, four 2D continuous displacements maps (Figure 2a) of increasing amplitude were created by generating two smooth and well behaved functions for each amplitude using MATLAB's “peaks” function to describe the horizontal and vertical displacements, respectively (ground truth Dg1x  =  Dg1y  =  [−0.4, 0.6] pixels; Dg2x  =  Dg2y  =  [−1.2, 1.7] pixels; Dg3x  =  Dg3y  =  [−2.0, 2.8] pixels; Dg4x  =  Dg4y  =  [−2.7, 3.9] pixels). For each displacement map, the corresponding image was generated by interpolating the original image on the deformed grids.

Bottom Line: We reproduced a study of cardiac-induced strain in the liver at 3T and simulated tagged MR images with different grid tag patterns to evaluate the performance of the Harmonic Phase (HARP) image analysis method and its dependence on the parameters of tag spacing and grid angle.The Partial Volume Effect (PVE), T1 relaxation, and different levels of noise were taken into account.The accuracy of the estimation decreased for increasing levels of added noise; as the level increased, the improved estimation caused by decreasing the tag spacing tended to zero.

View Article: PubMed Central - PubMed

Affiliation: IRCCS SDN Foundation, Naples, Italy.

ABSTRACT
The heartbeat has been proposed as an intrinsic source of motion that can be used in combination with tagged Magnetic Resonance Imaging (MRI) to measure displacements induced in the liver as an index of liver stiffness. Optimizing a tagged MRI acquisition protocol in terms of sensitivity to these displacements, which are in the order of pixel size, is necessary to develop the method as a quantification tool for staging fibrosis. We reproduced a study of cardiac-induced strain in the liver at 3T and simulated tagged MR images with different grid tag patterns to evaluate the performance of the Harmonic Phase (HARP) image analysis method and its dependence on the parameters of tag spacing and grid angle. The Partial Volume Effect (PVE), T1 relaxation, and different levels of noise were taken into account. Four displacement fields of increasing intensity were created and applied to the tagged MR images of the liver. These fields simulated the deformation at different liver stiffnesses. An Error Index (EI) was calculated to evaluate the estimation accuracy for various parameter values. In the absence of noise, the estimation accuracy of the displacement fields increased as tag spacings decreased. EIs for each of the four displacement fields were lower at 0° and the local minima of the EI were found to correspond to multiples of pixel size. The accuracy of the estimation decreased for increasing levels of added noise; as the level increased, the improved estimation caused by decreasing the tag spacing tended to zero. The optimal tag spacing turned out to be a compromise between the smallest tag period that is a multiple of the pixel size and is achievable in a real acquisition and the tag spacing that guarantees an accurate liver displacement measure in the presence of realistic levels of noise.

Show MeSH
Related in: MedlinePlus