Ashrani Aizzuddin Abd. Rahni

Respiratory motion tracking using the kinect camera

Respiratory motion presents a challenge in developing techniques to increase the accuracy of image acquisition or guided interventions in abdominal and thoracic organs. The use of distance cameras, such as that from Vision RT Ltd has been considered as a surrogate for respiratory motion tracking in external beam radiotherapy that does not require the use of markers. However, it is very costly and thus this project suggests using the Microsoft Xbox Kinect™ for tracking respiratory motion. The aim of the project is to utilize the Kinect camera which for instance, can be put inside the CT scanner room in the hospital. This system records the 3D motion data and further analyses the consistency of the respiratory motion. A Graphical User Interface (GUI) was developed to display the breathing signal and statistics of the data. This respiratory motion tracking system will facilitate clinicians in choosing a suitable treatment option for lung cancer patients with less cost.

Characterisation of respiratory motion extracted from 4D MRI

Nuclear Medicine (NM) imaging is currently the most sensitive approach for functional imaging of the human body. However, in order to achieve high-resolution imaging, one of the factors degrading the detail or apparent resolution in the reconstructed image, namely respiratory motion, has to be overcome. All respiratory motion correction approaches depend on some assumption or estimate of respiratory motion. In this paper, the respiratory motion found from 4D MRI is analysed and characterised. The characteristics found are compared with previous studies and will be incorporated into the process of estimating respiratory motion.

Recursive Bayesian estimation for respiratory motion correction in Nuclear Medicine imaging

Respiratory motion correction degrades quantitatively and qualitatively Nuclear Medicine images. We propose that adaptive approaches are required to correct for the irregular breathing patterns often encountered in the clinical setting, which can be addressed within a Bayesian tracking formulation. This allows inference of the hidden organ configurations using only knowledge of an external observation such as a parametrized external surface. The flexible framework described provides a method to correct for organ motion whilst accommodating for irregular unseen respiratory patterns. In this work we utilize a Kalman filter and compare it with a Particle filter. A novel adaptive state transition model is also introduced to describe the evolution of organ configurations. The Kalman filter marginally outperforms the Particle filter, both approaches however offer an effective motion correction mechanism, correcting for motion with errors of around 1-3mm. We present results of simulated PET images derived from XCAT to demonstrate the efficacy of the approach.

Thermal Noise Effect in FTTH Communication Systems

Thermal noise is generated naturally by thermal agitation of electrons in a conductor commonly found in opto-electronic devices. The fact that the optical medium is totally immune to noise does not exclude its occurrence in receiver parts. In communication, thermal noise has a major influence to the quality of the receiver. The lower the thermal noise the higher and more expensive is receiver sensitivity. This paper starts with a tutorial on thermal noise in common communication technologies. The major contribution factor to thermal noise power is also discussed analytically. For more comprehensive view, the includes a study on thermal noise effects network performance in terms of BER, maximum Q-factor, eye height and maximum distance. The fiber-to-the-home network is used as a test field to study the impact of two different values of thermal noise on network parameters. At the end of this paper, thermal noise minimizing techniques are also listed.

Extracting respiratory motion from 4D MRI using organ-wise registration

Nuclear Medicine (NM) imaging serves as a powerful diagnostic tool for imaging of biochemical and physiological processes in vivo. The degradation in spatial image resolution caused by the often irregular respiratory motion must be corrected to achieve high resolution imaging. In order perform motion correction more accurately, it is proposed that patient motion obtained from 4D MRI can be used to analyse respiratory motion. To extract motion from the dynamic MRI dataset an organ wise intensity based affine registration framework is proposed and evaluated. Comparison of the resultant motion obtained within selected organs is made against an open source free form deformation algorithm. For validation, the correlation of the results of both techniques to a previous study of motion in 20 patients is found. Organwise affine registration correlates very well (r≈0:9) with a previous study (Segars et al., 2007)1 whilst free form deformation shows little correlation (r ≈ 0:3). This increases the confidence of the organ wise affine registration framework being an effective tool to extract motion from dynamic anatomical datasets.

Development of a Particle Filter Framework for Respiratory Motion Correction in Nuclear Medicine Imaging

This research aims to develop a methodological framework based on a data driven approach known as particle filters, often found in computer vision methods, to correct the effect of respiratory motion on Nuclear Medicine imaging data. Particles filters are a popular class of numerical methods for solving optimal estimation problems and we wish to use their flexibility to make an adaptive framework. In this work we use the particle filter for estimating the deformation of the internal organs of the human torso, represented by X, over a discrete time index k. The particle filter approximates the distribution of the deformation of internal organs by generating many propositions, called particles. The posterior estimate is inferred from an observation Zk of the external torso surface. We demonstrate two preliminary approaches in tracking organ deformation. In the first approach, Xk represent a small set of organ surface points. In the second approach, Xk represent a set of affine organ registration parameters to a reference time index r. Both approaches are contrasted to a comparable technique using direct mapping to infer Xk from the observation Zk. Simulations of both approaches using the XCAT phantom suggest that the particle filter-based approaches, on average performs, better.

Development of a respiratory motion tracking system using a distance camera for diagnostic imaging and external beam radiotherapy delivery

This paper describes the development of a system that uses off the shelf ranging technology (distance cameras) to acquire a real-time multidimensional respiratory signal from a 3D surface reconstruction of the patients chest and abdomen without the use of markers. The preliminary measurements are discussed within the context of the use of such a system with an existing CT scanner.

PET Image Reconstruction Incorporating 3D Mean-Median Sinogram Filtering

Positron Emission Tomography (PET) projection data or sinogram contained poor statistics and randomness that produced noisy PET images. In order to improve the PET image, we proposed an implementation of pre-reconstruction sinogram filtering based on 3D mean-median filter. The proposed filter is designed based on three aims; to minimise angular blurring artifacts, to smooth flat region and to preserve the edges in the reconstructed PET image. The performance of the pre-reconstruction sinogram filter prior to three established reconstruction methods namely filtered-backprojection (FBP), Maximum likelihood expectation maximization-Ordered Subset (OSEM) and OSEM with median root prior (OSEM-MRP) is investigated using simulated NCAT phantom PET sinogram as generated by the PET Analytical Simulator (ASIM). The improvement on the quality of the reconstructed images with and without sinogram filtering is assessed according to visual as well as quantitative evaluation based on global signal to noise ratio (SNR), local SNR, contrast to noise ratio (CNR) and edge preservation capability. Further analysis on the achieved improvement is also carried out specific to iterative OSEM and OSEM-MRP reconstruction methods with and without pre-reconstruction filtering in terms of contrast recovery curve (CRC) versus noise trade off, normalised mean square error versus iteration, local CNR versus iteration and lesion detectability. Overall, satisfactory results are obtained from both visual and quantitative evaluations.

A composite registration framework for respiratory motion modelling from 4D MRI

4D MRI is current gaining attention as an imaging modality which is able to capture inter-cycle variability of respiratory motion. Such information is beneficial for example in radiotherapy planning and delivery. However the motion extracted from 4D MRI is dependant on the method of processing the images. Typically a deformable registration algorithm is used so that the 3D motion of every point in the image can be found. We have used several popular registration algorithms which have been recently published with publicly available data. Our findings suggest that due to the low contrast of 4D MRI, the registration is only accurate near clear boundaries, and less accurate elsewhere, to different degrees depending on the algorithm used. We thus suggest modelling organ deformation as an affine transformation, which is then embedded into the deformation field for more accurate modelling of respiratory motion.

Automatic volumetric localization of the liver in abdominal CT scans using low level processing and shape priors

In this paper we present an automatic volumetric liver localization method as an approach for liver segmentation. In the proposed method the aim is to localise a mean shape model of the liver in the target CT scan. The framework consists of three main steps: shape model construction, low level processing and shape model registration. We evaluated our method on the MICCAI 2007 liver segmentation challenge dataset. The Leave-one-out validation results demonstrate the effectiveness of the proposed method. The average volume overlap between our method and the ground truth, using the Jaccard index, is 0.64±0.11 which is acceptable for an initial localisation of the liver prior to further refinement.