Dataset Information

4D reconstruction of the beating embryonic heart from two orthogonal sets of parallel optical coherence tomography slice-sequences.

ABSTRACT: Current methods to build dynamic optical coherence tomography (OCT) volumes of the beating embryonic heart involve synchronization of 2D+time slice-sequences acquired over separate heartbeats. Temporal registration of these sequences is performed either through gating or postprocessing. While synchronization algorithms that exclusively rely on image- intrinsic signals allow forgoing external gating hardware, they are prone to error accumulation, require operator-supervised correction, or lead to nonisotropic resolution. Here, we propose an image-based, retrospective reconstruction technique that uses two sets of parallel 2D+T slice-sequences, acquired perpendicularly to each other, to yield accurate and automatic reconstructions with isotropic resolution. The method utilizes the similarity of the data at the slice intersections to spatio-temporally register the two sets of slice sequences and fuse them into a high-resolution 4D volume. We characterize our method by using 1) simulated heart phantom datasets and 2) OCT datasets acquired from the beating heart of live cultured E9.5 mouse and E10.5 rat embryos. We demonstrate that while our method requires greater acquisition and reconstruction time compared to methods that use slices from a single direction, it produces more accurate and self-validating reconstructions since each set of reconstructed slices acts as a reference for the slices in the perpendicular set.

SUBMITTER: Bhat S

PROVIDER: S-EPMC4114225 | biostudies-other | 2013 Mar

REPOSITORIES: biostudies-other

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Publications

4D reconstruction of the beating embryonic heart from two orthogonal sets of parallel optical coherence tomography slice-sequences.

Bhat Sandeep S Larina Irina V IV Larin Kirill V KV Dickinson Mary E ME Liebling Michael M

IEEE transactions on medical imaging 20121204 3

Current methods to build dynamic optical coherence tomography (OCT) volumes of the beating embryonic heart involve synchronization of 2D+time slice-sequences acquired over separate heartbeats. Temporal registration of these sequences is performed either through gating or postprocessing. While synchronization algorithms that exclusively rely on image- intrinsic signals allow forgoing external gating hardware, they are prone to error accumulation, require operator-supervised correction, or lead to ...[more]

PMID: 23221816

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Project description:BackgroundMotion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling.PurposeThis study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images.MethodsA 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC).ResultsThe trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average (± standard deviation) RMSE in mm-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040 ± 0.0009, 0.0029 ± 0.0002, 0.0024 ± 0.0003 and 0.0021 ± 0.0003. Corresponding NCC were 0.84 ± 0.05, 0.91 ± 0.05, 0.91 ± 0.05 and 0.93 ± 0.04.ConclusionsCNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.

Dataset Information

4D reconstruction of the beating embryonic heart from two orthogonal sets of parallel optical coherence tomography slice-sequences.

Publications

4D reconstruction of the beating embryonic heart from two orthogonal sets of parallel optical coherence tomography slice-sequences.

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