Three-dimensional freehand ultrasound: Image reconstruction and volume analysis

Barry, C.D.; Allott, C.P.; John, Nigel W.; Mellor, P; Arundel, Paul; Thomson, Duncan; Waterton, John C.

doi:10.1016/s0301-5629(97)00123-3

Cited by 179 publications

(107 citation statements)

References 29 publications

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“…The different volume reconstruction algorithms have been classified into three groups in [13]: Voxel-Based Methods (VBM), Pixel-Based Methods (PBM), and Function-Based Methods (FBM). More papers about volume reconstruction methods can be found in references [15][16][17][18].…”

Section: Volume Reconstruction For Freehand 3d Ultrasound Imagingmentioning

confidence: 99%

Quantitative Assessment of Variational Surface Reconstruction from Sparse Point Clouds in Freehand 3D Ultrasound Imaging during Image-Guided Tumor Ablation

Deng

Jiang

et al. 2016

Applied Sciences

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Surface reconstruction for freehand 3D ultrasound is used to provide 3D visualization of a VOI (volume of interest) during image-guided tumor ablation surgery. This is a challenge because the recorded 2D B-scans are not only sparse but also non-parallel. To solve this issue, we established a framework to reconstruct the surface of freehand 3D ultrasound imaging in 2011. The key technique for surface reconstruction in that framework is based on variational interpolation presented by Greg Turk for shape transformation and is named Variational Surface Reconstruction (VSR). The main goal of this paper is to evaluate the quality of surface reconstructions, especially when the input data are extremely sparse point clouds from freehand 3D ultrasound imaging, using four methods: Ball Pivoting, Power Crust, Poisson, and VSR. Four experiments are conducted, and quantitative metrics, such as the Hausdorff distance, are introduced for quantitative assessment. The experiment results show that the performance of the proposed VSR method is the best of the four methods at reconstructing surface from sparse data. The VSR method can produce a close approximation to the original surface from as few as two contours, whereas the other three methods fail to do so. The experiment results also illustrate that the reproducibility of the VSR method is the best of the four methods.

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Section: Volume Reconstruction For Freehand 3d Ultrasound Imagingmentioning

confidence: 99%

Quantitative Assessment of Variational Surface Reconstruction from Sparse Point Clouds in Freehand 3D Ultrasound Imaging during Image-Guided Tumor Ablation

Deng

Jiang

et al. 2016

Applied Sciences

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“…• 3D ultrasound offers the potential for improving the quality of a conventional 2D image, by compounding images acquired from different perspectives [18,159].…”

Section: Why 3d Ultrasound?mentioning

confidence: 99%

Volume Measurement in Sequential Freehand 3-D Ultrasound

Treece

Prager

Gee

et al. 1999

Lecture Notes in Computer Science

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SummaryThree-dimensional (3D) acquisition and visualisation techniques are increasingly being incorporated into commercial ultrasound scanners. The diagnostic benefit of such techniques is not yet convincing, compared to the added inconvenience of using them. Although it is straightforward to use special probes to acquire 3D data, these are more restrictive than conventional 2D probes. The only 3D technique which retains the scanning flexibility and wide area of application of 2D ultrasound, is freehand 3D ultrasound, where a 2D probe is moved manually and its location sensed remotely. However, it is hard to generate a regular volume of data from the resulting non-parallel ultrasound images.Probably the largest body of evidence justifying the use of 3D ultrasound relates to the accurate measurement of volume. However, volume measurement with 3D ultrasound requires segmentation (i.e. outlining of the area of interest), which is a time consuming, manual task. Both this problem, and that of creating a regular volume of data, are eased by the use of sequential freehand 3D ultrasound, a recent technique amplified in this thesis, where all the processing is performed on the original ultrasound images. Thus a regular array of data is not required, and segmentation is performed on images whose features and artifacts are recognisable to the clinician.In this thesis, novel volume measurement and surface visualisation algorithms are developed for sequential freehand 3D ultrasound. A particular emphasis is placed on limiting the number of segmented cross-sections required for a given measurement accuracy. Inherent accuracy is demonstrated by simulation to be within ±2%, from 10 or fewer cross-sections. In vivo precision, including registration and segmentation errors, is shown to be within ±7%, even on complicated objects such as the liver or a foetus, from similar numbers of cross-sections. The volume can be updated in real-time as each image is segmented, and surfaces can be interpolated and visualised within a few seconds. Such visualisation can reveal errors in the segmentation which are otherwise hard to see.In addition, a framework for multiple-sweep data is developed, which allows volume measurement and surface visualisation of anatomy that can only be scanned by several sweeps of the ultrasound probe. Accurate volume measurements can therefore be made of all areas observable by conventional ultrasound. These measurements can be accomplished within approximately 5 minutes of examining the patient.The surface visualisation algorithms can also produce high quality renderings of data from a wide range of sources in medical imaging and other fields. The interpolation of cross-sections is an improvement on shape-based interpolation, and the triangular mesh created from the data is of a much higher quality than that using marching cubes. An extension of the surface interpolation algorithm can also be used to gradually change, or 'morph' one 3D surface to another, a technique commonly used in the film industry.

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“…There are varieties of geometrical phantoms proposed in the literature mainly to facilitate the calibration procedure. Phantoms with sparse set of wires are proposed in [1,3]. In these procedures, intersection points of the wires are being imaged and are related to the known 3D position of the same point in the phantom.…”

Section: Introductionmentioning

confidence: 99%

A Novel Phantom-Less Spatial and Temporal Ultrasound Calibration Method

Khamene

Sauer

2005

Lecture Notes in Computer Science

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Abstract. This paper introduces a novel method for ultrasound calibration for both spatial and temporal parameters. The main advantage of this method is that it does not require a phantom, which is usually expensive to fabricate. Furthermore, the method does not require extensive image processing. For spatial calibration, we solve an optimization problem established by a set of equations that relate the orientations of a line (i.e., calibration pointer) to the intersection points appearing in the ultrasound image. The line orientation is provided through calibration of both ends of the calibration pointer. Temporal calibration is achieved by processing of the captured pointer orientations and the corresponding image positions of intersection along with the timing information. The effectiveness of the unified method for both spatial and temporal calibration is apparent from the quality of the 3D reconstructions of a known object.

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Three-dimensional freehand ultrasound: Image reconstruction and volume analysis

Cited by 179 publications

References 29 publications

Quantitative Assessment of Variational Surface Reconstruction from Sparse Point Clouds in Freehand 3D Ultrasound Imaging during Image-Guided Tumor Ablation

Quantitative Assessment of Variational Surface Reconstruction from Sparse Point Clouds in Freehand 3D Ultrasound Imaging during Image-Guided Tumor Ablation

Volume Measurement in Sequential Freehand 3-D Ultrasound

A Novel Phantom-Less Spatial and Temporal Ultrasound Calibration Method

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