Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this paper, we propose a semi-automated segmentation method for extracting lung lesions from thoracic PET/CT images by combining low level processing and active contour techniques. The lesions are first segmented in PET images which are first converted to standardised uptake values (SUVs). The segmented PET images then serve as an initial contour for subsequent active contour segmentation of corresponding CT images. To evaluate its accuracy, the Jaccard Index (JI) was used as a measure of the accuracy of the segmented lesion compared to alternative segmentations from the QIN lung CT segmentation challenge, which is possible by registering the whole body PET/CT images to the corresponding thoracic CT images. The results show that our proposed technique has acceptable accuracy in lung lesion segmentation with JI values of around 0.8, especially when considering the variability of the alternative segmentations.
In this paper we present preliminary results of comparison of automatic segmentations of the infarct core, between that obtained from CT perfusion (based on time to peak parameter) and diffusion weighted imaging (DWI). For each patient, the two imaging volumes were automatically co-registered to a common frame of reference based on an acquired CT angiography image. The accuracy of image registration is measured by the overlap of the segmented brain from both images (CT perfusion and DWI), measured within their common field of view. Due to the limitations of the study, DWI was acquired as a follow up scan up to a week after initial CT based imaging. However, we found significant overlap of the segmented brain (Jaccard indices of approximately 0.8) and the percentage of infarcted brain tissue from the two modalities were still fairly highly correlated (correlation coefficient of approximately 0.9). The results are promising with more data needed in future for clinical inference.
Accurate, Respiratory Motion Modelling of the abdominal-thoracic organs serves as a pre-requisite for motion correction of Nuclear Medicine (NM) Images. Many respiratory motion models to date build a static correspondence between a parametrized external surrogate signal and internal motion. Mean drifts in respiratory motion, changes in respiratory style and noise conditions of the external surrogate signal motivates a more adaptive approach to capture non-stationary behavior. To this effect we utilize the application of our novel Kalman model with an incorporated expectation maximization step to allow adaptive learning of model parameters with changing respiratory observations. A comparison is made with a popular total least squares (PCA) based approach. It is demonstrated that in the presence of noisy observations the Kalman framework outperforms the static PCA model, however, both methods correct for respiratory motion in the computational anthropomorphic phantom to < 2mm. Motion correction performed on 3 dynamic MRI patient datasets using the Kalman model results in correction of respiratory motion to ≈ 3mm.
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.
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 respiratory motion. This paper builds upon previous work in recursive
Bayesian estimation of respiratory motion assuming a stereo camera observation of the motion of the external
torso surface. This paper compares the performance of a modified autoregressive transition model against the
previously presented linear transition model used when estimating motion within a 4D dataset generated from
the XCAT phantom.
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.
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 view of the system in the PF is seen as an advantage that considers the complexity and uncertainties in
estimating respiratory motion. Previous tests using XCAT has shown the possibility of estimating unseen organ
configuration using training data that only consist of a single respiratory cycle. This paper augments application specific
adaptation methods that have been implemented for better PF estimates with an iterative model update step. Results
show that errors are further reduced to an extent up to a small number of iterations and such improvements will be
advantageous for the PF to cope with more realistic and complex applications.
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.
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