A comparison of freehand three-dimensional ultrasound reconstruction techniques

Rohling, Robert; Gee, Andrew H.; Berman, Laurence

doi:10.1016/s1361-8415(99)80028-0

Cited by 147 publications

(129 citation statements)

References 16 publications

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“…Please refer to [13,14] for a review of freehand 3D US volume reconstruction algorithms. 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).…”

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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“…The sets of reslices with rigid and with non-rigid correction are clearly smoother than their original uncorrected counterparts. In order to investigate the variation in the original and corrected data, each of the ten data sets was resampled onto a cubic 3D array, using pixel nearest neighbour interpolation [4]. The spatial location and extent of this array was fixed over all the data sets, to enable subsequent comparison.…”

Section: Repeated Scans In the Same Directionmentioning

confidence: 99%

Correction of probe pressure artifacts in freehand 3D ultrasound

Treece

Prager

Gee

et al. 2002

Medical Image Analysis

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An algorithm has been presented in [5] which combines non-rigid image-based registration and conventional position sensing to correct probe-pressure-induced registration errors in freehand three-dimensional (3D) ultrasound volumes. Visual results indicated that the algorithm increased both the clarity and accuracy of reslices through in vivo volumetric data sets. These initial findings are verified in this report by more rigorous experiments leading to numerical results. In addition, it is shown how the original algorithm can be extended to apply to convex probes which generate compressions varying in the lateral direction as well as in depth.

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“…Both spherical [138] and ellipsoidal [120] Gaussian kernels have been used; the latter is more representative of the actual sampling resolution. A more accurate method has been suggested in which the data is smoothly approximated by using radial basis functions [161]; however the small gain in reconstruction accuracy is matched by a significantly increased processing overhead.…”

Section: Data Interpolationmentioning

confidence: 99%

“…Interpolating such data to a regular voxel array, using one of the standard techniques discussed in Section 1.2.2, produces undesirable results. Figure 4.1(a), for instance, shows two sweeps from a liver examination which have been interpolated using the voxel nearest neighbour [161] interpolation. The poor image quality is due to a combination of treating the black regions around each B-scan as 'real' data, and misregistration of the data itself.…”

Section: Visualisation Of Implicit Surfacesmentioning

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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A comparison of freehand three-dimensional ultrasound reconstruction techniques

Cited by 147 publications

References 16 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

Correction of probe pressure artifacts in freehand 3D ultrasound

Volume Measurement in Sequential Freehand 3-D Ultrasound

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