We propose an approach to analyzing functional neuroimages in which: (1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data; and (2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). In an on-off design we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a reversible-jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting).
In respiratory-gated cardiac SPECT with amplitude binning, the acquisition time can vary greatly both among respiratory gates and among acquisition angles within each gate. If not properly accounted for, this uneven distribution in acquired data statistics will lead to limited-angle artifacts in reconstruction, which in turn can impact on the accuracy of respiratory motion correction. We investigate a compensation scheme for this uneven distribution by directly taking into account in the imaging model the actual acquisition time at different projection angles. In the experiment, we demonstrated this approach with simulated NCAT imaging data, for which quantitative results were obtained on both the reconstructed myocardium and estimated motion; we also tested the proposed approach on a set of clinical acquisition. The results show that the proposed approach could effectively suppress the limited-angle artifacts and improve the reconstruction in terms of both image accuracy and lesion detectability.
Since the introduction of clinical x-ray phase-contrast mammography (PCM), a technique that exploits refractive-index variations to create edge enhancement at tissue boundaries, a number of optimization studies employing physical image-quality metrics have been performed. Ideally, task-based assessment of PCM would have been conducted with human readers. These studies have been limited, however, in part due to the large parameter-space of PCM system configurations and the difficulty of employing expert readers for large-scale studies. It has been proposed that numerical observers can be used to approximate the statistical performance of human readers, thus enabling the study of task-based performance over a large parameter-space.Methods are presented for task-based image quality assessment of PCM images with a numerical observer, the most significant of which is an adapted lumpy background from the conventional mammography literature that accounts for the unique wavefield propagation physics of PCM image formation and will be used with a numerical observer to assess image quality. These methods are demonstrated by performing a PCM task-based image quality study using a numerical observer. This study employs a signal-known-exactly, background-known-statistically Bayesian ideal observer method to assess the detectability of a calcification object in PCM images when the anode spot size and calcification diameter are varied.The first realistic model for the structured background in PCM images has been introduced. A numerical study demonstrating the use of this background model has compared PCM and conventional mammography detection of calcification objects. The study data confirm the strong PCM calcification detectability dependence on anode spot size. These data can be used to balance the trade-off between enhanced image quality and the potential for motion artifacts that comes with use of a reduced spot size and increased exposure time.A method has been presented for the incorporation of structured breast background data into task-based numerical observer assessment of PCM images. The method adapts conventional background simulation techniques to the wavefield propagation physics necessary for PCM imaging. This method is demonstrated with a simple detection task.
Positron emission tomography (PET) is a medical imaging modality which produces valuable functional information, but is limited by the poor image quality it provides. Considerable attention has been payed to the problem of reconstructing images in a way that produces better image resolution and noise properties. In dynamic imaging applications PET data are particularly noisy, thus preventing successful recovery of spatial resolution by signal processing applications. In this paper we show that smoothing of image data using a low-order approximation along the time axis can greatly enhance restoration performance.
Cone-beam (cb) collimation for single-photon emission computed tomography (spect) provides higher sensitivity than parallel-hole collimation, however it produces truncated projection data that can lead to undesirable image artifacts. In response, it has been suggested (Jaszczak 1992) that parallel-beam and cone-beam data be combined to obtain increased sensitivity while maintaining completeness of the data for accurate reconstruction. Herein, a 3-d filtered-backprojection (fbp) approach for reconstructing such data sets is proposed. The algorithm begins with a rebinning of the parallel- and cone-beam (p&cb) data to a common planeintegral projection space: the projection space of the 3-d Radon transform. A normalization step is then implemented to correct for the sampling pattern of the system, thus assuring that the rebinned data are proportional to the plane integrals of the source distribution of the object. Finally, a fully 3-D fbp reconstruction based on the inversion formula of the 3-Radon transform is performed using the rebinned plane integrals. The advantage of this algorithm is that, by making the necessary corrections prior to reconstruction, it avoids residual artifacts that can remain after other methods are employed. In addition, the computation time required by the proposed method is substantially shorter than that reported for maximum-likelihood reconstructions by the expectation-maximization (em) algorithm (Jaszczak 1992).
Positron Emission tomography (PET) is a method for imaging the concentration of positron-emitting radioisotopes introduced into the body of a patient. Unfortunately, like other forms of emission computed tomography (ECT), PET is plagued by serious technical problems that have severely restricted its potential in unlocking the answers to difficult questions in brain research. In particular, two problems inherent to ECT methods have hampered progress in PET: 1) Patient safety considerations and detector response characteristics impose limits on the permissible radiation dose, therefore PET images are invariably severely quantum-noise-limited; and 2) The resolution of PET images is extremely poor owing primarily to the size of the scintillation detectors used in the data-acquisition step.
In this work we explore a new technique for relevance feedback in a learning-based framework for retrieval of relevant mammogram images from a database, for purposes of aiding diagnoses. Our goal is to adapt online the learning procedure in accordance with user responses without the need to repeat the training procedure. Toward this end we develop a relevance feedback approach based on the concept of incremental learning developed recently in the theory of support vector machines. The proposed approach is demonstrated using clustered microcalcifications extracted from a database consisting of 76 mammograms.
In this paper we propose a fast image reconstruction procedure for dynamic images from gated SPECT acquisition. We divide the cardiac cycle into a number of gate intervals as in gated SPECT, but treat the tracer distribution for each gate as a time-varying signal. To speed up the reconstruction procedure, we derive an ordered-subset version of the dynamic expectation-maximization (dEM) algorithm, and apply motion-compensated temporal filtering to enforce the correlation among the different gates. We simulated gated cardiac perfusion imaging using the gated mathematical cardiac-torso(gMCAT) phantom with Tc99m-Teboroxime dynamics. Our experimental results demonstrate that the proposed method can reconstruct gated dynamic images faster than the regular dEM algorithm, and that temporal smoothing is more effective than spatial smoothing for noise reduction.
Down Syndrome (DS) adults experience accumulation of Alzheimer's disease (AD)-like amyloid plaques and tangles and a high incidence of dementia and could provide an enriched population to study AD-targeted treatments. However, to evaluate effects of therapeutic intervention, it is necessary to dissociate the contributions of DS and AD from overall phenotype. Imaging biomarkers offer the potential to characterize and stratify patients who will worsen clinically but have yielded mixed findings in DS subjects.We evaluated 18F fluorodeoxyglucose positron emission tomography (PET), florbetapir PET, and structural magnetic resonance (sMR) image data from 12 nondemented DS adults using advanced multivariate machine learning methods.Our results showed distinctive patterns of glucose metabolism and brain volume enabling dissociation of DS and AD effects. AD-like pattern expression corresponded to amyloid burden and clinical measures.These findings lay groundwork to enable AD clinical trials with characterization and disease-specific tracking of DS adults.
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