Difference between revisions of "2010 Summer Project Week Liver Ablation"

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* BWH: Haiying Liu, Noby Hata, Sota Oguro
 
* BWH: Haiying Liu, Noby Hata, Sota Oguro
 
* Georgetown University: Ziv Yaniv
 
* Georgetown University: Ziv Yaniv
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3D Ultrasound (US) is an emerging diagnostic technique that is non-invasive, fast and comparably cheap. It is commonly used for acquiring images of fetuses or anatomical regions and organs near the skin, for example the thyroid gland or other parenchymatous organs. 3D US has some advantages compared to 2D US, as it the diagnosis may become largely observer-independent and accurate volumetric measurements become possible. Compared to alternative imaging techniques, like CT or MRI, it features a much better spatial resolution and images can be acquired faster and a lot easier. It may, therefore, be combined with another imaging technique in order to enable ongoing screening during treatment or control studies. The combination of MR and US is desirable, as both techniques are very feasible for the analysis of soft tissue. However, this is a challenging task and quite a few problems have to be addressed.
 
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[[Image:US_Thyroid_Bias.png‎|600px|Bias Field on Ultrasound Image and Segmentation of the Thyroid]]
 
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The figure above shows a slice taken from a 3D ultrasound image of the human thyroid (upper left), together with the intensity profile along a line through the tyhroid (upper right, lower left) and a segmentation (lower right). US images suffer from speckle noise, sonic attenuation and also regions of sound shadows (insufficient gel layer). The sonic attenuation cannot be fully compensated by the imaging devices and the effect is clearly visible when examining the intensity profile along a line through the thyroid region (lower left image), which shows decreasing intensities with increasing distance to the surface.
 
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In previous work, we have examined several pre-processing techniques for 3D US denoising, however, we did not find a satisfying solution for the bias field correction of the images. Since the intensity-based registration of MR with 3D US is a very hard problem and only few work has been done on the registration of 3D US with MR images, we will try to come up with some ideas for a combined approach for the bias field correction, segmentation and registration during the project weak.
 
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<h3>Objectives</h3>
 
<h3>Objectives</h3>

Revision as of 15:47, 17 June 2010

Home < 2010 Summer Project Week Liver Ablation

  • Liver with fiducials

  • Liver with fiducials and segmentation

  • Liver model

Key Investigators

  • BWH: Haiying Liu, Noby Hata, Sota Oguro
  • Georgetown University: Ziv Yaniv

Objectives

  • denoising and bias field correction of 3D US data
  • model for the combined image correction, segmentation and registration between 3D US and MRI
  • visualization of all processing steps within Slicer

Approach, Plan

The bias field on the 3D US data varies spatially with a very low frequency. It is similar to the bias field effects in MR caused by magnetic inhomogeneities. We will investigate whether an EM-based approach may be used for the correction, similar to the previous work by Wells et. al. and Pohl et. al., and try to adapt the already existing Slicer code to this problem.

For the registration, we may have to come up with a new formulation that uses not only the statistics of the intensities, but also some additional information about the tissue boundaries. The tissue boundaries are directly influencing the visible content of the 3D US images, however, they have a different appearance and structure within the MRI.

Progress

Fill this at the end of the week

Delivery Mechanism

The work will be provided as ITK modules and it intended to integrate them into a Slicer module.

References

  • W.M. Wells, R. Kikinis, W.E.L. Grimson, F. Jolesz. Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging, 15, pp. 429-442, 1996
  • K.M. Pohl, J. Fisher, W.E.L. Grimson, R. Kikinis, and W.M. Wells. A Bayesian model for joint segmentation and registration. NeuroImage, 31(1), pp. 228-239, 2006
  • E.N.K. Kollorz, D.A. Hahn, R. Linke, T.W. Goecke, J. Hornegger, and T. Kuwert. Quantification of Thyroid Volume using 3-D Ultrasound Imaging. IEEE Transactions on Medical Imaging, 27(4), pp. 457-466, 2008
  • W. Wein, S. Brunke, A. Khamene, M.R. Callstrom, N. Navab. Automatic CT-Ultrasound Registration for Diagnostic Imaging and Image-guided Intervention. Medical Image Analysis, 12(5), pp. 577-585, 2008
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