Performance evaluation of the modified iterative closest point methods for intra-operative ultrasound and pre-operative MR image registration of brain

Purpose: Intra-operative ultrasound imaging as a non-ionized and being real time has been found very applicable as an intra-operative update of patient data in image guided neurosurgery system. The main point is the accurate registration of intra-operative with pre-operative images. Due to speckle noise in ultrasound images, scale differentiation between MR and ultrasound images and their different resolution, an accurate registration of ultrasound images with preoperative MR images is a challenging problem. Methods: In this paper the effect of different steps of the Iterative Closest Point is considered and, then, the best modified version of ICP is introduced for this type of data. To perform this study, a Poly Vinyl Alcohol-Cryogel brain phantom is used which allows simulating brain deformation. The performance of the best version of ICP is compared to a well-known point based algorithm, Coherent Point Drift in terms of accuracy and speed. Results: The results proved CPD algorithm was more robust than ICP algorithms in the presence of noise, although with a more computational cost. Changing different steps in conventional ICP led to improve the performance of the ICP. As the results of our phantom study confirm the best version of ICP has not only achieved an accuracy close to CPD method, but also in a much faster approach. Conclusion: According to a trade off between the speed and accuracy of nine implemented versions of ICP algorithms, using some modified version of ICP is preferred to CPD method.

Preoperative CT and intra-operative physical space registration of the spine using an articulated model: a phantom study

Due to the difference in spine curvature in intra-operative and preoperative situations in image guided spine surgery [IGSS], each vertebra needs to be registered separately. This can be done by collecting anatomical landmarks on the patient's anatomy using a pointer and a tracker, and registering them with corresponding points on the 3D model of CT images. Registering each vertebra using this method can be a tedious and time consuming task. Registering the spine using an articulated model, which incorporates the inter-vertebral transformation between consecutive vertebra, introduces a simple and efficient framework. In this paper, a deformation [extension] is applied to the lumbar vertebra of a spine phantom. Local coordinate systems are defined separately for each vertebra in the 3D model reconstructed from CT images before deformation and on the phantom after deformation. The intervertebral transformations for consecutive vertebrae are calculated in the 3D models. Registration is carried out using the local coordinate systems and the intervertebral transformations. This method was evaluated using point based registration with known corresponding points in both spaces. These results were also compared to those of surface based registration. As indicated by the results, an average improvement of 30% in the registration accuracy was achieved compared to the surface based registration method with acquired random points. The proposed method decreases the registration time by eliminating the need to acquire surface points on each individual vertebra. It is also applicable in situations with defected vertebrae where anatomical landmarks are difficult to distinguish

[Study of denoising in TEOAE signals using an appropriate mother wavelet function]

Matching a mother wavelet to class of signals can be of interest in signal analysis and denoising based on wavelet multiresolution analysis and decomposition. As transient evoked otoacoustic emissions [TEOAES] are contaminated with noise, the aim of this work was to provide a quantitative approach to the problem of matching a mother wavelet to TEOAE signals by using tuning curves and to use it for analysis and denoising TEOAE signals. Approximated mother wavelet for TEOAE signals was calculated using an algorithm for designing wavelet to match a specified signal. In this paper a tuning curve has used as a template for designing a mother wavelet that has maximum matching to the tuning curve. The mother wavelet matching was performed on tuning curves spectrum magnitude and phase independent of one another. The scaling function was calculated from the matched mother wavelet and by using these functions, lowpass and highpass filters were designed for a filter bank and otoacoustic emissions signal analysis and synthesis. After signal analyzing, denoising was performed by time windowing the signal time-frequency component. Aanalysis indicated more signal reconstruction improvement in comparison with coiflets mother wavelet and by using the purposed denoising algorithm it is possible to enhance signal to noise ratio up to dB. The wavelet generated from this algorithm was remarkably similar to the biorthogonal wavelets. Therefore, by matching a biorthogonal wavelet to the tuning curve and using wavelet packet analysis, a high resolution time-frequency analysis for the otoacoustic emission signals is possible