Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method.

Thomas Guerrero; Geoffrey Zhang; Tzung-Chi Huang; Kang-Ping Lin

doi:10.1088/0031-9155/49/17/022

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Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method.

Thomas Guerrero·Geoffrey Zhang·Tzung-Chi Huang·Kang-Ping Lin

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Abstract

The purpose of this work was to develop and validate an automated method for intrathoracic tumour motion estimation from breath-hold computed tomography (BH CT) imaging using the three-dimensional optical flow method (3D OFM). A modified 3D OFM algorithm provided 3D displacement vectors for each voxel which were used to map tumour voxels on expiration BH CT onto inspiration BH CT images. A thoracic phantom and simulated expiration/inspiration BH CT pairs were used for validation. The 3D OFM was applied to the measured inspiration and expiration BH CT images from one lung cancer and one oesophageal cancer patient. The resulting displacements were plotted in histogram format and analysed to provide insight regarding the tumour motion. The phantom tumour displacement was measured as 1.20 and 2.40 cm with full-width at tenth maximum (FWTM) for the distribution of displacement estimates of 0.008 and 0.006 cm, respectively. The maximum error of any single voxel's motion estimate was 1.1 mm along the z-dimension or approximately one-third of the z-dimension voxel size. The simulated BH CT pairs revealed an rms error of less than 0.25 mm. The displacement of the oesophageal tumours was nonuniform and up to 1.4 cm, this was a new finding. A lung tumour maximum displacement of 2.4 cm was found in the case evaluated. In conclusion, 3D OFM provided an accurate estimation of intrathoracic tumour motion, with estimated errors less than the voxel dimension in a simulated motion phantom study. Surprisingly, oesophageal tumour motion was large and nonuniform, with greatest motion occurring at the gastro-oesophageal junction.

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Algorithms Esophageal Neoplasms Humans Image Processing, Computer-Assisted Imaging, Three-Dimensional Lung Neoplasms Models, Statistical Movement Phantoms, Imaging Radiotherapy Planning, Computer-Assisted Statistics as Topic Thoracic Neoplasms Time Factors Tomography, X-Ray Computed Journal Article

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Thomas Guerrero, Geoffrey Zhang, Tzung-Chi Huang, Kang-Ping Lin. 2004-09-07. Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method.. https://doi.org/10.1088/0031-9155%2F49%2F17%2F022

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