Ashrani Aizzuddin Abd. Rahni

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Extract imaging data in reference registration to Respiratory (MR) frame target frames Motion ABSTRACT 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(More)
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(More)
The continual improvement in spatial resolution of Nuclear Medicine (NM) scanners has made accurate compensation of patient motion increasingly important. A major source of corrupting motion in NM acquisition is due to respiration. Therefore a particle filter (PF) approach has been proposed as a powerful method for motion correction in NM. The probabilistic(More)
In this paper, we aim to assess the accuracy as well as compare between the Microsoft Kinect™ version 1 and Microsoft Kinect™ version 2 with regards to the purpose of respiratory motion tracking. We find that both correlate well to an alternative method of respiratory motion measurement i.e. a respiratory belt, up to a distance of around 2 m.(More)
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(More)
Compensation for respiratory motion has been identified as a crucial factor in achieving high resolution Nuclear Medicine (NM) imaging. Many motion correction approaches have been studied and they are seen to have advantages over simpler approaches such as respiratory gating. However, all motion correction approaches rely on an assumption or estimation of(More)
Due to the increasing amount of data available from medical imaging procedures and also the increase in computing power, there has been a rise in the automation of the analysis of such data. A crucial step in the automation of such procedures is accurate segmentation of anatomy. Popular approaches include model based segmentation. However, these approaches(More)
Deformable image registration is a key part of modern medical image processing and analysis. The aim of image registration is to align one image to another image. In this paper, three deformable image registration methods (NiftyReg, MRF-based and lreg) are compared based on their estimated motion field from 4D MRI data for respiratory motion modelling. The(More)
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