2D Reconstruction of Small Intestine's Interior Wall

Examining and interpreting of a large number of wireless endoscopic images from the gastrointestinal tract is a tiresome task for physicians. A practical solution is to automatically construct a two dimensional representation of the gastrointestinal tract for easy inspection. However, little has been done on wireless endoscopic image stitching, let alone systematic investigation. The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration. First, the keypoints are extracted by Principle Component Analysis and Scale Invariant Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable keypoints. Second, the optimal transformation parameters obtained from first step are fed to the Normalised Mutual Information (NMI) algorithm as an initial solution. With modified Marquardt-Levenberg search strategy in a multiscale framework, the NMI can find the optimal transformation parameters in the shortest time. The proposed methodology has been tested on two different datasets - one with real wireless endoscopic images and another with images obtained from Micro-Ball (a new wireless cubic endoscopy system with six image sensors). The results have demonstrated the accuracy and robustness of the proposed methodology both visually and quantitatively.Comment: Journal draf

A Hybrid Similarity Measure Framework for Multimodal Medical Image Registration

Medical imaging is widely used today to facilitate both disease diagnosis and treatment planning practice, with a key prerequisite being the systematic process of medical image registration (MIR) to align either mono or multimodal images of different anatomical parts of the human body. MIR utilises a similarity measure (SM) to quantify the level of spatial alignment and is particularly demanding due to the presence of inherent modality characteristics like intensity non-uniformities (INU) in magnetic resonance images and large homogeneous non-vascular regions in retinal images. While various intensity and feature-based SMs exist for MIR, mutual information (MI) has become established because of its computational efficiency and ability to register multimodal images. It is however, very sensitive to interpolation artefacts in the presence of INU with noise and can be compromised when overlapping areas are small. Recently MI-based hybrid variants which combine regional features with intensity have emerged, though these incur high dimensionality and large computational overheads. To address these challenges and secure accurate, efficient and robust registration of images containing high INU, noise and large homogeneous regions, this thesis presents a new hybrid SM framework for 2D multimodal rigid MIR. The framework consistently provides superior quantitative and qualitative performance, while offering a uniquely flexible design trade-off between registration accuracy and computational time. It makes three significant technical contributions to the field: i) An expectation maximisation-based principal component analysis with mutual information (EMPCA-MI) framework incorporating neighbourhood feature information; ii) Two innovative enhancements to reduce information redundancy and improve MI computational efficiency; and iii) an adaptive algorithm to select the most significant principal components for feature selection. The thesis findings conclusively confirm the hybrid SM framework offers an accurate and robust 2D registration solution for challenging multimodal medical imaging datasets, while its inherent flexibility means it can also be extended to the 3D registration domain

Attenuation compensation in cerebral 3D PET: effect of the attenuation map on absolute and relative quantitation

It is generally well accepted that transmission (TX)-based non-uniform attenuation correction can supply more accurate absolute quantification; however, whether it provides additional benefits in routine clinical diagnosis based on qualitative interpretation of 3D brain positron emission tomography (PET) images is still the subject of debate. The aim of this study was to compare the effect of the two major classes of method for determining the attenuation map, i.e. uniform versus non-uniform, using clinical studies based on qualitative assessment as well as absolute and relative quantitative volume of interest-based analysis. We investigated the effect of six different methods for determining the patient-specific attenuation map. The first method, referred to as the uniform fit-ellipse method (UFEM), approximates the outline of the head by an ellipse assuming a constant linear attenuation factor (μ=0.096cm−1) for soft tissue. The second, referred to as the automated contour detection method (ACDM), estimates the outline of the head from the emission sinogram. Attenuation of the skull is accounted for by assuming a constant uniform skull thickness (0.45cm) within the estimated shape and the correct μ value (0.151cm−1) is used. The usual measured transmission method using caesium-137 single-photon sources was used without (MTM) and with segmentation of the TX data (STM). These techniques were finally compared with the segmented magnetic resonance imaging method (SMM) and an implementation of the inferring attenuation distributions method (IADM) based on the digital Zubal head atlas. Several image quality parameters were compared, including absolute and relative quantification indexes, and the correlation between them was checked. The qualitative evaluation showed no significant differences between the different attenuation correction techniques as assessed by expert physicians, with the exception of ACDM, which generated artefacts in the upper edges of the head. The mean squared error between the different attenuation maps was also larger when using this latter method owing to the fact that the current implementation of the method significantly overestimated the head contours on the external slices. Correlation between the mean regional cerebral glucose metabolism (rCGM) values obtained with the various attenuation correction methods and those obtained with the gold standard (MTM) was good, except in the case of ACDM (R 2=0.54). The STM and SMM methods showed the best correlation (R 2=0.90) and the regression lines agreed well with the line of identity. Relative differences in mean rCGM values were in general less than 8%. Nevertheless, ANOVA results showed statistically significant differences between the different methods for some regions of the brain. It is concluded that the attenuation map influences both absolute and relative quantitation in cerebral 3D PET. Transmission-less attenuation correction results in a reduced radiation dose and makes a dramatic difference in acquisition time, allowing increased patient throughpu

3-D data handling and registration of multiple modality medical images

The many different clinical imaging modalities used in diagnosis and therapy deliver two different types of information: morphological and functional. Clinical interpretation can be assisted and enhanced by combining such information (e.g. superimposition or fusion). The handling of such data needs to be performed in 3-D. Various methods for registration developed by other authors are reviewed and compared. Many of these are based on registering external reference markers, and are cumbersome and present significant problems to both patients and operators. Internal markers have also been used, but these may be very difficult to identify. Alternatively, methods based on the external surface of an object have been developed which eliminate some of the problems associated with the other methods. Thus the methods which have been extended, developed, and described here, are based primarily on the fitting of surfaces, as determined from images obtained from the different modalities to be registered. Annex problems to that of the surface fitting are those of surface detection and display. Some segmentation and surface reconstruction algorithms have been developed to identify the surface to be registered. Surface and volume rendering algorithms have also been implemented to facilitate the display of clinical results. An iterative surface fitting algorithm has been developed based on the minimization of a least squares distance (LSD) function, using the Powell method and alternative minimization algorithms. These algorithms and the qualities of fit so obtained were intercompared. Some modifications were developed to enhance the speed of convergence, to improve the accuracy, and to enhance the display of results during the process of fitting. A common problem with all such methods was found to be the choice of the starting point (the initial transformation parameters) and the avoidance of local minima which often require manual operator intervention. The algorithm was modified to apply a global minimization by using a cumulative distance error in a sequentially terminated process in order to speed up the time of evaluating of each search location. An extension of the algorithm into multi-resolution (scale) space was also implemented. An initial global search is performed at coarse resolution for the 3-D surfaces of both modalities where an appropriate threshold is defined to reject likely mismatch transformations by testing of only a limited subset of surface points. This process is used to define the set of points in the transformation space to be used for the next level of resolution, again with appropriately chosen threshold levels, and continued down to the finest resolution level. All these processes were evaluated using sets of well defined image models. The assessment of this algorithm for 3-D surface registration of data from (3-D) MRI with MRI, MRI with PET, MRI with SPECT, and MRI with CT data is presented, and clinical examples are illustrated and assessed. In the current work, the data from multi-modality imaging of two different types phantom (e.g. Hoffman brain phantom, Jaszczak phantom), thirty routinely imaged patients and volunteer subjects, and ten patients with setting external markers on their head were used to assess and verify 3-D registration. The accuracy of the sequential multi-resolution method obtained by the distance values of 4-10 selected reference points on each data set gave an accuracy of 1.44±0.42 mm for MR-MR, 1.82±0.65 for MR-CT, 2.38±0.88 for MR-PET, and 3.17±1.12 for MR-SPECT registration. The cost of this process was determined to be of the order of 200 seconds (on a Micro-VAX II), although this is highly dependent on some adjustable parameters of the process (e.g. threshold and the size of the geometrical transformation space) by which the accuracy is aimed

Image quality of coronary CT angiography (CCTA) using 640-slice scanner: qualitative and quantitative assessments of coronary arteries visibility

The purpose of this study was to evaluate the image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA) using 640-slice scanner. Advancement of multidetector computed tomography (MDCT) technology with higher spatial, temporal resolution, and increasing detector array have improved the image quality and diagnostic accuracy of CCTA. A total of 25 patients (12 men and 13 women) underwent CCTA was chosen and data was acquired by 640-slice scanner. All 16 segments of coronary arteries were evaluated by two reviewers using a 4-likert scale for qualitative assessment. In quantitative assessment, the evaluation of 4 main coronary arteries were analysed in terms of signal intensity (SI), image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). All 25 patients with a mean age of 52.88 ± 14.75 years old and body mass index (BMI) of 24.24 ± 3.28 kg/m2 were analysed. In qualitative assessment, from the total of 400 segments, 379 segments (95 %) have diagnostic value while 21 segments do not have diagnostic value, which means 5 % artefact was detected. In quantitative assessment, there was no statistical differences in gender, race, and BMI (p>0.05). Overall evaluation showed that higher SI at the left main artery (LM) at 393.7 ± 47.19. Image noise was higher at right coronary artery (RCA) at 39.01 ± 13.97. SNR and CNR showed higher at left anterior descending (LAD) with 12.73 ± 5.17 and LM 9.14 ± 4.2, respectively. In conclusion, this study indicates that 640-slice MDCT has higher diagnostic value in CCTA examination with 95 % vessel visibility with 5 % artefact detection

An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images

We have developed an algorithm for the rigid-body registration of a CT volume to a set of C-arm images. The algorithm uses a gradient-based iterative minimization of a least-squares measure of dissimilarity between the C-arm images and projections of the CT volume. To compute projections, we use a novel method for fast integration of the volume along rays. To improve robustness and speed, we take advantage of a coarse-to-fine processing of the volume/image pyramids. To compute the projections of the volume, the gradient of the dissimilarity measure, and the multiresolution data pyramids, we use a continuous image/volume model based on cubic B-splines, which ensures a high interpolation accuracy and a gradient of the dissimilarity measure that is well defined everywhere. We show the performance of our algorithm on a human spine phantom, where the true alignment is determined using a set of fiducial markers

Practical application of contrast-enhanced magnetic resonance mammography [CE-MRM] by an algorithm combining morphological and enhancement patterns

The purpose of this article is to report our practical utilization of dynamic contrast-enhanced magnetic resonance mammography [DCE-MRM] in the diagnosis of breast lesions. In many European centers, was preferred a high-temporal acquisition of both breasts simultaneously in a large FOV. We preferred to scan single breasts, with the aim to combine the analysis of the contrast intake and washout with the morphological evaluation of breast lesions. We followed an interpretation model, based upon a diagnostic algorithm, which combined contrast enhancement with morphological evaluation, in order to increase our confidence in diagnosis. DCE-MRM with our diagnostic algorithm has identified 179 malignant and 41 benign lesions; final outcome has identified 178 malignant and 42 benign lesions, 3 false positives and 2 false negatives. Sensitivity of CE-MRM was 98.3%; specificity, 95.1%; positive predictive value 98.9%; negative predictive,. value, 92.8% and accuracy, 97.7%. (C) 2008 Elsevier Ltd. All rights reserve

Validation of vessel size imaging (VSI) in high-grade human gliomas using magnetic resonance imaging, image-guided biopsies, and quantitative immunohistochemistry.

To evaluate the association between a vessel size index (VSIMRI) derived from dynamic susceptibility contrast (DSC) perfusion imaging using a custom spin-and-gradient echo echoplanar imaging (SAGE-EPI) sequence and quantitative estimates of vessel morphometry based on immunohistochemistry from image-guided biopsy samples. The current study evaluated both relative cerebral blood volume (rCBV) and VSIMRI in eleven patients with high-grade glioma (7 WHO grade III and 4 WHO grade IV). Following 26 MRI-guided glioma biopsies in these 11 patients, we evaluated tissue morphometry, including vessel density and average radius, using an automated procedure based on the endothelial cell marker CD31 to highlight tumor vasculature. Measures of rCBV and VSIMRI were then compared to histological measures. We demonstrate good agreement between VSI measured by MRI and histology; VSIMRI = 13.67 μm and VSIHistology = 12.60 μm, with slight overestimation of VSIMRI in grade III patients compared to histology. rCBV showed a moderate but significant correlation with vessel density (r = 0.42, p = 0.03), and a correlation was also observed between VSIMRI and VSIHistology (r = 0.49, p = 0.01). The current study supports the hypothesis that vessel size measures using MRI accurately reflect vessel caliber within high-grade gliomas, while traditional measures of rCBV are correlated with vessel density and not vessel caliber

Improving Attenuation Correction in Hybrid Positron Emission Tomography

Hybrid positron emission tomography imaging techniques such as PET/CT and PET/MR have undergone significant developments over the last two decades and have played increasingly more important roles both in research and in the clinic. A unique advantage PET has over other clinical imaging modalities is its capability of accurate quantification. However, as the most critical component of PET quantification, attenuation correction in hybrid PET systems is challenged in several different aspects, including the spatial- temporal mismatch between the PET emission images and the associated attenuation images provided by the complementary modality, and the difficulty in bone identification in the MR-based attenuation correction approaches. These problems, if left unaddressed, can limit the potential of the hybrid PET systems. This research developed solutions to overcome the spatial-temporal mismatch in PET/CT and PET/MR, and established the requirements for bone identification in PET/MR. An automatic registration algorithm based on a modified fuzzy c-means clustering method and gradient correlation was developed and validated to perform automatic registration in cardiac PET/CT data of different breathing protocols. A free- breathing MR protocol and post-process algorithm were developed to provide MR-based attenuation images that also match the temporal resolution of PET and were evaluated in a feasibility study. The relationship between the sensitivity of bone identification in attenuation images and PET quantification of bone lesions uptake was evaluated in a simulated study using data from 18F-sodium fluoride PET/CT exams