2014
DOI: 10.1109/tmi.2014.2321285
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Three-Dimensional Sheaf of Ultrasound Planes Reconstruction (SOUPR) of Ablated Volumes

Abstract: This paper presents an algorithm for three dimensional reconstruction of tumor ablations using ultrasound shear wave imaging with electrode vibration elastography. Radiofrequency ultrasound data frames are acquired over imaging planes that form a subset of a sheaf of planes sharing a common axis of intersection. Shear wave velocity is estimated separately on each imaging plane using a piecewise linear function fitting technique with a fast optimization routine. An interpolation algorithm then computes velocity… Show more

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Cited by 16 publications
(20 citation statements)
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“…The 3D reconstruction for SOUPR is modeled as an optimization problem with an objective function consisting of data consistency and smoothness constraints. 31 The data smoothness constraints utilized may result in the 3D volume reconstructed with SOUPR having lower data variance when compared to wobbler based data acquisition where the reconstruction is accomplished with a 3D cubical interpolation. The standard deviation of the SNRe metric for each displacement tracking kernel size is larger for SOUPR when compared with wobbler based reconstructions as illustrated in Figs.…”
Section: Discussionmentioning
confidence: 99%
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“…The 3D reconstruction for SOUPR is modeled as an optimization problem with an objective function consisting of data consistency and smoothness constraints. 31 The data smoothness constraints utilized may result in the 3D volume reconstructed with SOUPR having lower data variance when compared to wobbler based data acquisition where the reconstruction is accomplished with a 3D cubical interpolation. The standard deviation of the SNRe metric for each displacement tracking kernel size is larger for SOUPR when compared with wobbler based reconstructions as illustrated in Figs.…”
Section: Discussionmentioning
confidence: 99%
“…Data consistency is aimed at keeping the reconstructed 3D strain or binary mask to be within a small variation from the known data measured at those 2D planes, and the data smoothness constraint was based on the assumption that the inclusion possessed a smooth ellipsoidal shape, which is common for thermal lesion. 31 The solution to this optimization problem and tuning of the regularization parameter η was described in detail in the paper by Ingle and Varghese. 31 A reconstructed 3D binary mask was used for inclusion volume estimation which is discussed in Sec.…”
Section: C 3d Strain Volume and Binary Mask Reconstructionmentioning
confidence: 99%
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“…Each image plane is manually aligned by using angle markers on the phantom container and the needle as a guide. Misalignment variations are averaged out by acquiring five independent datasets [8]. …”
Section: Methodsmentioning
confidence: 99%