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A 3D freehand ultrasound system for multi-view reconstructions from sparse 2D scanning planes.

Yu H, Pattichis MS, Agurto C, Beth Goens M - Biomed Eng Online (2011)

Bottom Line: Volume measurements from multi-view 3D reconstructions are found to be consistently and significantly more accurate than measurements from single view reconstructions.In clinical in-vivo cardiac experiments, we show that volume estimates of the left ventricle from multi-view reconstructions are found to be in better agreement with clinical measures than measures from single view reconstructions.Multi-view 3D reconstruction from sparse 2D freehand B-mode images leads to more accurate volume quantification compared to single view systems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA. pattichis@ece.unm.edu.

ABSTRACT

Background: A significant limitation of existing 3D ultrasound systems comes from the fact that the majority of them work with fixed acquisition geometries. As a result, the users have very limited control over the geometry of the 2D scanning planes.

Methods: We present a low-cost and flexible ultrasound imaging system that integrates several image processing components to allow for 3D reconstructions from limited numbers of 2D image planes and multiple acoustic views. Our approach is based on a 3D freehand ultrasound system that allows users to control the 2D acquisition imaging using conventional 2D probes.For reliable performance, we develop new methods for image segmentation and robust multi-view registration. We first present a new hybrid geometric level-set approach that provides reliable segmentation performance with relatively simple initializations and minimum edge leakage. Optimization of the segmentation model parameters and its effect on performance is carefully discussed. Second, using the segmented images, a new coarse to fine automatic multi-view registration method is introduced. The approach uses a 3D Hotelling transform to initialize an optimization search. Then, the fine scale feature-based registration is performed using a robust, non-linear least squares algorithm. The robustness of the multi-view registration system allows for accurate 3D reconstructions from sparse 2D image planes.

Results: Volume measurements from multi-view 3D reconstructions are found to be consistently and significantly more accurate than measurements from single view reconstructions. The volume error of multi-view reconstruction is measured to be less than 5% of the true volume. We show that volume reconstruction accuracy is a function of the total number of 2D image planes and the number of views for calibrated phantom. In clinical in-vivo cardiac experiments, we show that volume estimates of the left ventricle from multi-view reconstructions are found to be in better agreement with clinical measures than measures from single view reconstructions.

Conclusions: Multi-view 3D reconstruction from sparse 2D freehand B-mode images leads to more accurate volume quantification compared to single view systems. The flexibility and low-cost of the proposed system allow for fine control of the image acquisition planes for optimal 3D reconstructions from multiple views.

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Related in: MedlinePlus

Segmentation parameter optimization for a cardiac image (Hausdorff criterion). (a) Best segmentation for threshold value Tres = 5, (b) Best segmentation result for Tres = 125, (c) Best segmentation result for Tres = 20, (d) Hausdorff distance as a function of ε and β =β1 = β2 for 3 different thresholds: Tres = 5 with minimum error value of 7.32 (ε = 0.464,β = 1.29), Tres = 125 with minimum error value of 6.14 (ε = 0.1,β = 0.6), Tres = 20 with minimum error value of 2.30 (ε = 0.1,β = 1.29).
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Figure 5: Segmentation parameter optimization for a cardiac image (Hausdorff criterion). (a) Best segmentation for threshold value Tres = 5, (b) Best segmentation result for Tres = 125, (c) Best segmentation result for Tres = 20, (d) Hausdorff distance as a function of ε and β =β1 = β2 for 3 different thresholds: Tres = 5 with minimum error value of 7.32 (ε = 0.464,β = 1.29), Tres = 125 with minimum error value of 6.14 (ε = 0.1,β = 0.6), Tres = 20 with minimum error value of 2.30 (ε = 0.1,β = 1.29).

Mentions: Two typical ultrasound images (a phantom image and a cardiac image) for estimating the optimal segmentation parameters are selected. Figures 4 and 5 show the images used in the optimization and their results. Hausdorff distance and MAD are used to measure the difference between the manual and automatic segmentation boundaries. Similar results are obtained when optimising for the minimal Hausdorff distance or MAD. In what follows, we present and discuss results for the Hausdorff distance.


A 3D freehand ultrasound system for multi-view reconstructions from sparse 2D scanning planes.

Yu H, Pattichis MS, Agurto C, Beth Goens M - Biomed Eng Online (2011)

Segmentation parameter optimization for a cardiac image (Hausdorff criterion). (a) Best segmentation for threshold value Tres = 5, (b) Best segmentation result for Tres = 125, (c) Best segmentation result for Tres = 20, (d) Hausdorff distance as a function of ε and β =β1 = β2 for 3 different thresholds: Tres = 5 with minimum error value of 7.32 (ε = 0.464,β = 1.29), Tres = 125 with minimum error value of 6.14 (ε = 0.1,β = 0.6), Tres = 20 with minimum error value of 2.30 (ε = 0.1,β = 1.29).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037343&req=5

Figure 5: Segmentation parameter optimization for a cardiac image (Hausdorff criterion). (a) Best segmentation for threshold value Tres = 5, (b) Best segmentation result for Tres = 125, (c) Best segmentation result for Tres = 20, (d) Hausdorff distance as a function of ε and β =β1 = β2 for 3 different thresholds: Tres = 5 with minimum error value of 7.32 (ε = 0.464,β = 1.29), Tres = 125 with minimum error value of 6.14 (ε = 0.1,β = 0.6), Tres = 20 with minimum error value of 2.30 (ε = 0.1,β = 1.29).
Mentions: Two typical ultrasound images (a phantom image and a cardiac image) for estimating the optimal segmentation parameters are selected. Figures 4 and 5 show the images used in the optimization and their results. Hausdorff distance and MAD are used to measure the difference between the manual and automatic segmentation boundaries. Similar results are obtained when optimising for the minimal Hausdorff distance or MAD. In what follows, we present and discuss results for the Hausdorff distance.

Bottom Line: Volume measurements from multi-view 3D reconstructions are found to be consistently and significantly more accurate than measurements from single view reconstructions.In clinical in-vivo cardiac experiments, we show that volume estimates of the left ventricle from multi-view reconstructions are found to be in better agreement with clinical measures than measures from single view reconstructions.Multi-view 3D reconstruction from sparse 2D freehand B-mode images leads to more accurate volume quantification compared to single view systems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA. pattichis@ece.unm.edu.

ABSTRACT

Background: A significant limitation of existing 3D ultrasound systems comes from the fact that the majority of them work with fixed acquisition geometries. As a result, the users have very limited control over the geometry of the 2D scanning planes.

Methods: We present a low-cost and flexible ultrasound imaging system that integrates several image processing components to allow for 3D reconstructions from limited numbers of 2D image planes and multiple acoustic views. Our approach is based on a 3D freehand ultrasound system that allows users to control the 2D acquisition imaging using conventional 2D probes.For reliable performance, we develop new methods for image segmentation and robust multi-view registration. We first present a new hybrid geometric level-set approach that provides reliable segmentation performance with relatively simple initializations and minimum edge leakage. Optimization of the segmentation model parameters and its effect on performance is carefully discussed. Second, using the segmented images, a new coarse to fine automatic multi-view registration method is introduced. The approach uses a 3D Hotelling transform to initialize an optimization search. Then, the fine scale feature-based registration is performed using a robust, non-linear least squares algorithm. The robustness of the multi-view registration system allows for accurate 3D reconstructions from sparse 2D image planes.

Results: Volume measurements from multi-view 3D reconstructions are found to be consistently and significantly more accurate than measurements from single view reconstructions. The volume error of multi-view reconstruction is measured to be less than 5% of the true volume. We show that volume reconstruction accuracy is a function of the total number of 2D image planes and the number of views for calibrated phantom. In clinical in-vivo cardiac experiments, we show that volume estimates of the left ventricle from multi-view reconstructions are found to be in better agreement with clinical measures than measures from single view reconstructions.

Conclusions: Multi-view 3D reconstruction from sparse 2D freehand B-mode images leads to more accurate volume quantification compared to single view systems. The flexibility and low-cost of the proposed system allow for fine control of the image acquisition planes for optimal 3D reconstructions from multiple views.

Show MeSH
Related in: MedlinePlus