Erkan U. Mumcuoglu

Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images

The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density. To ensure nonnegativity of the solution, a penalty function is used to convert the problem to one of unconstrained optimization. Preconditioners are used to enhance convergence rates. These methods generally achieve effective convergence in 15-25 iterations. Reconstructions are presented of an (18)FDG whole body scan from data collected using a Siemens/CTI ECAT931 whole body system. These results indicate significant improvements in emission image quality using the Bayesian approach, in comparison to filtered backprojection, particularly when reprojections of the MAP transmission image are used in place of the standard attenuation correction factors.

Bayesian reconstruction of PET images: Methodology and performance analysis

We describe a practical statistical methodology for the reconstruction of PET images. Our approach is based on a Bayesian formulation of the imaging problem. The data are modelled as independent Poisson random variables and the image is modelled using a Markov random field smoothing prior. We describe a sequence of calibration procedures which are performed before reconstruction: (i) calculation of accurate attenuation correction factors from re-projected Bayesian reconstructions of the transmission image; (ii) estimation of the mean of the randoms component in the data; and (iii) computation of the scatter component in the data using a Klein-Nishina-based scatter estimation method. The Bayesian estimate of the PET image is then reconstructed using a pre-conditioned conjugate gradient method. We performed a quantitation study with a multi-compartment chest phantom in a Siemens/CTI ECAT931 system. Using 40 1 min frames, we computed the ensemble mean and variance over several regions of interest from images reconstructed using the Bayesian and a standard filtered backprojection (FBP) protocol. The values for the region of interest were compared with well counter data for each compartment. These results show that the Bayesian protocol can produce substantial improvements in relative quantitation over the standard FBP protocol, particularly when short transmission scans are used. An example showing the application of the method to a clinical chest study is also given.

Optical fiber readout of scintillator arrays using a multi-channel PMT: a high resolution PET detector for animal imaging

The authors report the results from a new high resolution gamma ray imaging detector designed for use in a positron emission tomography (PET) system dedicated to small animal imaging. The detectors consist of an 8/spl times/8 array of 2/spl times/2/spl times/10 mm bismuth germanate (BGO) crystals coupled by 2 mm diameter double clad optical fibers to a 64 pixel multi-channel photomultiplier tube (MC-PMT). A charge division readout board is used to convert the 64 output channels into four position sensitive signals which determine the crystal of interaction. Measurements with a pair of these detectors demonstrate an intrinsic spatial resolution of 1.4 mm, a coincidence timing resolution of 15 ns and an energy resolution ranging between 35 and 60%. Based on these encouraging results, the design for a dedicated animal PET tomograph is proposed and simulations of this system project a reconstructed resolution of less than 2 mm within a 5 cm diameter transaxial field of view.

Accurate geometric and physical response modelling for statistical image reconstruction in high resolution PET

Accurate modeling of the data formation and detection process in PET is essential for optimizing resolution. Here, the authors develop a model in which the following factors are explicitly included: depth dependent geometric sensitivity, photon pair non-colinearity, attenuation, intrinsic detector sensitivity, non-uniform sinogram sampling, crystal penetration and inter-crystal scatter. Statistical reconstruction methods can include these modeling factors in the system matrix that represents the probability of detecting an emission from each image pixel at each detector-pair. The authors describe a method for computing these factors using a combination of calibration measurements, geometric modeling and Monte Carlo computation. By assuming that blurring effects and depth dependent sensitivities are separable, the authors are able to exploit rotational symmetries with respect to the sinogram. This results in substantial savings in both storage requirements and computational costs. Using phantom data the authors show that this system model can produce higher resolution near the center of the field of view, at a given SNR, than both simpler geometric models and reconstructions using filtered backprojection. The authors also show, using an off-centered phantom, that larger improvements in resolution occur towards the edge of the field of view due to the explicit modeling of crystal penetration effects.

Design of a CZT based breast SPECT system

A high-resolution SPECT instrument dedicated to breast imaging has been designed incorporating arrays of collimated Cadmium-Zinc-Telluride (CZT) detectors tiled on either a cylindrical surface or a hemispherical surface surrounding the breast. The performance characteristics of a three-segment rotating parallel-hole collimator as well as a stationary multiple pin-hole collimator were considered for the cylindrical system. A stationary multiple pin-hole collimated system was also considered for the hemispherical design. Monte Carlo studies suggest that at almost equal spatial resolution of 0.5 cm, the cylindrical design with parallel-hole collimator would have an approximately a factor of two higher geometrical efficiency than the hemispherical pinhole collimated system including effects of attenuation in the breast. However, whereas the parallel-hole collimator must be rotated to acquire data from multiple angles of view, the pin-hole version has the advantage of recording data from 112 views in a stationary mode. Monte Carlo studies of filtered backprojection as well as a Bayesian reconstruction approach including attenuation and scatter within the breast, where the breast was modeled as a 15 cm hemisphere of uniform activity distribution containing three spherical lesions of diameters 1.0 cm, 0.7 cm and 0.5 cm respectively suggest that the 0.5 cm could be detected with either design in a one-hour SPECT study assuming a 10:1 tumor to background ratio. The authors conclude that a high resolution breast SPECT instrument where the resolution is limited to about 0.5 cm is viable with CZT detectors.

A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets

In this study, n-peptide compositions are utilized for protein vectorization over a discriminative remote homology detection framework based on support vector machines (SVMs). The size of amino acid alphabet is gradually reduced for increasing values of n to make the method to conform with the memory resources in conventional workstations. A hash structure is implemented for accelerated search of n-peptides. The method is tested to see its ability to classify proteins into families on a subset of SCOP family database and compared against many of the existing homology detection methods including the most popular generative methods; SAM-98 and PSI-BLAST and the recent SVM methods; SVM-Fisher, SVM-BLAST and SVM-Pairwise. The results have demonstrated that the new method significantly outperforms SVM-Fisher, SVM-BLAST, SAM-98 and PSI-BLAST, while achieving a comparable accuracy with SVM-Pairwise. In terms of efficiency, it performs much better than SVM-Pairwise. It is shown that the information of n-peptide compositions with reduced amino acid alphabets provides an accurate and efficient means of protein vectorization for SVM-based sequence classification.

Computerized detection and segmentation of mitochondria on electron microscope images

Mitochondrial function plays an important role in the regulation of cellular life and death, including disease states. Disturbance in mitochondrial function and distribution can be accompanied by significant morphological alterations. Electron microscopy tomography (EMT) is a powerful technique to study the 3D structure of mitochondria, but the automatic detection and segmentation of mitochondria in EMT volumes has been challenging due to the presence of subcellular structures and imaging artifacts. Therefore, the interpretation, measurement and analysis of mitochondrial distribution and features have been time consuming, and development of specialized software tools is very important for high‐throughput analyses needed to expedite the myriad studies on cellular events. Typically, mitochondrial EMT volumes are segmented manually using special software tools. Automatic contour extraction on large images with multiple mitochondria and many other subcellular structures is still an unaddressed problem. The purpose of this work is to develop computer algorithms to detect and segment both fully and partially seen mitochondria on electron microscopy images. The detection method relies on mitochondrias approximately elliptical shape and double membrane boundary. Initial detection results are first refined using active contours. Then, our seed point selection method automatically selects reliable seed points along the contour, and segmentation is finalized by automatically incorporating a live‐wire graph search algorithm between these seed points. In our evaluations on four images containing multiple mitochondria, 52 ellipses are detected among which 42 are true and 10 are false detections. After false ellipses are eliminated manually, 14 out of 15 fully seen mitochondria and 4 out of 7 partially seen mitochondria are successfully detected. When compared with the segmentation of a trained reader, 91% Dice similarity coefficient was achieved with an average 4.9 nm boundary error.

A statistical approach to transmission image reconstruction from ring source calibration measurements in PET

The problem of reconstructing a transmission attenuation image is addressed. The estimation procedure is based on a Bayesian formulation incorporating a Poisson likelihood model that includes scatter for the transmission data and a Markov random field prior. The results obtained using this approach result in improved accuracy in computing the ACFs (attenuation correction factors). A transmission image is also useful for reconstructing anatomical landmarks for use in cross-modality registration. A third advantage of reconstruction of the transmission image is that, if the patient moves between the transmission and one or more emission scans, the reconstructed transmission image can be used to generate ACFs for any new position. Several candidate optimization techniques are investigated, and the quantitative accuracy of the reconstructed images is evaluated.<<ETX>>

Automatic segmentation of human facial tissue by MRI-CT fusion

The aim of this study was to develop automatic image segmentation methods to segment human facial tissue which contains very thin anatomic structures. The segmentation output can be used to construct a more realistic human face model for a variety of purposes like surgery planning, patient specific prosthesis design and facial expression simulation. Segmentation methods developed were based on Bayesian and Level Set frameworks, which were applied on three image types: magnetic resonance imaging (MRI), computerized tomography (CT) and fusion, in which case information from both modalities were utilized maximally for every tissue type. The results on human data indicated that fusion, thickness adaptive and postprocessing options provided the best muscle/fat segmentation scores in both Level Set and Bayesian methods. When the best Level Set and Bayesian methods were compared, scores of the latter were better. Number of algorithm parameters (to be trained) and computer run time measured were also in favour of the Bayesian method.

Subcellular Localization Prediction with New Protein Encoding Schemes

Subcellular localization is one of the key properties in functional annotation of proteins. Support vector machines (SVMs) have been widely used for automated prediction of subcellular localizations. Existing methods differ in the protein encoding schemes used. In this study, we present two methods for protein encoding to be used for SVM-based subcellular localization prediction: n-peptide compositions with reduced amino acid alphabets for larger values of n and pairwise sequence similarity scores based on whole sequence and N-terminal sequence. We tested the methods on a common benchmarking data set that consists of 2,427 eukaryotic proteins with four localization sites. As a result of 5-fold cross-validation tests, the encoding with n-peptide compositions provided the accuracies of 84.5, 88.9, 66.3, and 94.3 percent for cytoplasmic, extracellular, mitochondrial, and nuclear proteins, where the overall accuracy was 87.1 percent. The second method provided 83.6, 87.7, 87.9, and 90.5 percent accuracies for individual locations and 87.8 percent overall accuracy. A hybrid system, which we called PredLOC, makes a final decision based on the results of the two presented methods which achieved an overall accuracy of 91.3 percent, which is better than the achievements of many of the existing methods. The new system also outperformed the recent methods in the experiments conducted on a new-unique SWISSPROT test set