Digital Image-based Elasto-Tomography Research Articles

Laser doppler vibrometer validation of an optical flow motion tracking algorithm

Abstract Background The use of single point laser Doppler vibrometer is well established for evaluating displacement or validation in many applications such as scientific research, defence, manufacturing, and biomedicine with other techniques. In this research, breast shaped fabricated phantoms are used in laser Doppler vibrometery to validate the optical flow motion measuring algorithm used in a mechanical vibration based breast cancer screening system. Methods Three different silicone phantoms were used: healthy; 10 mm inclusion and 20 mm inclusion. The overall goal was to use single point laser Doppler vibrometer data to validate digital image-based elasto-tomography (DIET) motion data from optical flow tracking at frequencies of 16 Hz, 24 Hz, 32 Hz, and 40 Hz. Summary of findings Results show excellent validation with errors less than 6% for healthy phantom, and errors less than 8% for 10 mm inclusion and 20 mm inclusion. Conclusions The overall results from the optical flow motion algorithm used with DIET and a laser Doppler vibrometer at every frequency show the optical flow algorithm captures surface motion of breast shaped silicone phantoms with good accuracy. The optical flow algorithm is thus suitable and robust enough for use in clinical breast screening. Finally, the errors presented quantify explicitly the error of this algorithm verses this laser-based gold standard.

Finite element modelling and validation for breast cancer detection using digital image elasto-tomography.

Finite element (FE) models are increasingly used to validate experimental data in breast cancer. This research constructed a biomechanical FE model for breast shaped phantoms used to develop and validate a mechanical vibration based screening system. Such models do not currently exist but would enhance development of this screening technology. Three phantoms were modelled: healthy, with 10 and 20mm inclusions. The overall goal was to create models with enough accuracy to replace experimental phantoms in providing data to optimize diagnostic algorithms for digital image-based elasto-tomography (DIET) screening technologies. FE model results were validating against experimental DIET phantom data for over 4000 collected points on each model and phantom using cross-correlation coefficients between experimental simulated data and direct comparison. Results showed good to strong correlation ranging from 0.7 to 1.0 in all cases with over 90% having a value over 0.9. Magnitudes for each frame of the dynamic response also matched well, indicating that the material properties and geometry were accurate enough to provide this level of correlation. These results justify the use of FE model generated data for in silico diagnostic algorithm development testing. The overall modelling and validation approach is not overly complex, and thus generalizable to similar problems using mechanical properties of silicone phantoms, and might be extensible to human cases with further work. Graphical abstract Validate that dynamic displacements show that the model can be used in place of phantoms for rapid development of diagnostic algorithms that use surface motion to detect underlying mechanical properties.

Reconstructing 3-D skin surface motion for the DIET breast cancer screening system.

Digital image-based elasto-tomography (DIET) is a prototype system for breast cancer screening. A breast is imaged while being vibrated, and the observed surface motion is used to infer the internal stiffness of the breast, hence identifying tumors. This paper describes a computer vision system for accurately measuring 3-D surface motion. A model-based segmentation is used to identify the profile of the breast in each image, and the 3-D surface is reconstructed by fitting a model to the profiles. The surface motion is measured using a modern optical flow implementation customized to the application, then trajectories of points on the 3-D surface are given by fusing the optical flow with the reconstructed surfaces. On data from human trials, the system is shown to exceed the performance of an earlier marker-based system at tracking skin surface motion. We demonstrate that the system can detect a 10 mm tumor in a silicone phantom breast.

Vision-based 3D surface motion capture for the DIET breast cancer screening system

Breast cancer is one of the most prevalent forms of cancer in the world today. The search for effective treatment and screening methods is a highly active area of research. The Digital Image-based Elasto-Tomography (DIET) project is a new breast cancer screening system under development, where surface motion from the mechanically actuated breast is measured in 3D, and used as input to an inverse problem solving for breast elasticity. Cancerous lesions appear as high contrast features, being an order of magnitude stiffer than healthy tissue. The 3D motion capture is measured by an array of digital cameras using computer vision techniques. This paper presents a computer vision imaging system for the capture of 3D breast surface motion for the DIET system, including the image acquisition system, camera calibration, and 3D surface and motion reconstruction. Results are presented for experiments performed with silicone gel phantoms, with conditions designed to replicate the clinical procedure. Full 3D surface motion is successfully captured using an array of five cameras. Some successful results from the DIET inverse problem are also presented to demonstrate the viability of the system in practice.

Strobe Imaging System for Digital Image-Based Elasto-Tomography Breast Cancer Screening

Digital image-based elasto-tomography technology relies on obtaining high-resolution images of a breasts surface under high-frequency actuation, typically in the range of 50-100 Hz. Off-the-shelf digital cameras and imaging elements are unable to capture images directly at these speeds. A method based on strobe imaging is presented for obtaining the required high-speed image capture at a resolution of 1280 times 1024 pixels and actuation frequency of 100 Hz. The final working system produced images that enabled effective 3-D motion tracking of the surface of a silicon phantom. The motion is tracked accurately using a novel Euclidean Invariant signature method.

Surface Reconstruction for a DIET breast cancer screening system

Abstract This paper outlines a method for accurately tracking surface motion on an actuated breast as part of a Digital Image-based Elasto-Tomography (DIET) breast cancer screening project. The tracking method is validated in both simulation and experiment on a silicon breast phantom which has similar elasticity characteristics to human breast tissue. Given mean reprojection error ranges between 0.3 and 0.5 pixels for 2M pixel cameras the end result is sub-millimetre surface tracking of a silicon phantom actuated at 100 Hz. This highly accurate, fast and low cost method of tracking is thus well suited to a DIET system.

Fast normalized cross correlation for motion tracking using basis functions

Digital image-based elasto-tomography (DIET) is an emerging method for non-invasive breast cancer screening. Effective clinical application of the DIET system requires highly accurate motion tracking of the surface of an actuated breast with minimal computation. Normalized cross correlation (NCC) is the most robust correlation measure for determining similarity between points in two or more images providing an accurate foundation for motion tracking. However, even using fast Fourier transform (FFT) methods, it is too computationally intense for rapidly managing several large images. A significantly faster method of calculating the NCC is presented that uses rectangular approximations in place of randomly placed landmark points or the natural marks on the breast. These approximations serve as an optimal set of basis functions that are automatically detected, dramatically reducing computational requirements. To prove the concept, the method is shown to be 37–150 times faster than the FFT-based NCC with the same accuracy for simulated data, a visco-elastic breast phantom experiment and human skin. Clinically, this approach enables thousands of randomly placed points to be rapidly and accurately tracked providing high resolution for the DIET system.

Digital Image-Based Elasto-Tomography: First Experiments in Surface Based Mechanical Property Estimation of Gelatine Phantoms

Digital Image-based Elasto-Tomography (DIET) is a novel surface-based elasticity reconstruction method for determining the elastic property distribution within the breast. Following on from proof of concept simulation studies, this research considers the motion evaluation and stiffness reconstruction of a soft tissue approximating gelatine phantom. This initial phantom work provides an intermediate stage between prior simulation studies more detailed phantom studies to follow. Reference points on the surface of a cylindrical phantom were successfully tracked and converted into a steady-state motion description. Motion error based mechanical property reconstruction allowed an estimation of the stiffness of the gelatine when actuated at 50Hz. The reconstructed stiffness compared favorably with independently measured stiffness properties of the gelatine material when experimental assumptions were considered. An experimental noise estimate of 50% was confirmed accurate by comparing experimental motions to simulated motion data with added noise.

Digital Image-Based Elasto-Tomography: Proof of Concept Studies for Surface Based Mechanical Property Reconstruction

Digital Image-based Elasto-Tomography (DIET) is a novel method of determining the distribution of elastic properties within the breast. Using an array of calibrated digital cameras and an inverse reconstruction algorithm, DIET allows reconstruction of the internal elastic stiffness distribution of the breast using only motions at the breast surface. This reconstructed stiffness should clearly show carcinoma based on their high elastic property contrast with healthy tissue. Proof of concept studies are presented for both the calibration of the digital imaging system and the inverse reconstruction algorithm. The reconstruction algorithm identified high stiffness tumors in the majority of test cases, even with the addition of random noise based on expected calibration accuracy.

Digital Image-Based Elasto-Tomography for Soft Tissue Imaging(Imaging & Measurement)

Digital Image-Based Elasto-Tomography for Soft Tissue Imaging(Imaging & Measurement)