Showing papers by "Richard M. Leahy published in 2014"

Patlak Image Estimation From Dual Time-Point List-Mode PET Data

Abstract: We investigate using dual time-point PET data to perform Patlak modeling. This approach can be used for whole body dynamic PET studies in which we compute voxel-wise estimates of Patlak parameters using two frames of data for each bed position. Our approach directly uses list-mode arrival times for each event to estimate the Patlak parametric image. We use a penalized likelihood method in which the penalty function uses spatially variant weighting to ensure a count independent local impulse response. We evaluate performance of the method in comparison to fractional changes in SUV values (%DSUV) between the two frames using Cramer Rao analysis and Monte Carlo simulation. Receiver operating characteristic (ROC) curves are used to compare performance in differentiating tumors relative to background based on the dynamic data sets. Using area under the ROC curve as a performance metric, we show superior performance of Patlak relative to %DSUV over a range of dynamic data sets and parameters. These results suggest that Patlak analysis may be appropriate for analysis of dual time-point whole body PET data and could lead to superior detection of tumors relative to %DSUV metrics.

An equal start: absence of group differences in cognitive, social, and neural measures prior to music or sports training in children

Abstract: Several studies comparing adult musicians and non-musicians have provided compelling evidence for functional and anatomical differences in the brain systems engaged by musical training. It is not known, however, whether those differences result from long-term musical training or from pre-existing traits favoring musicality. In an attempt to begin addressing this question, we have launched a longitudinal investigation of the effects of childhood music training on cognitive, social and neural development. We compared a group of 6- to 7-year old children at the start of intense after-school musical training, with two groups of children: one involved in high intensity sports training but not musical training, another not involved in any systematic training. All children were tested with a comprehensive battery of cognitive, motor, musical, emotional, and social assessments and underwent magnetic resonance imaging and electroencephalography. Our first objective was to determine whether children who participate in musical training were different, prior to training, from children in the control groups in terms of cognitive, motor, musical, emotional, and social behavior measures as well as in structural and functional brain measures. Our second objective was to determine whether musical skills, as measured by a music perception assessment prior to training, correlates with emotional and social outcome measures that have been shown to be associated with musical training. We found no neural, cognitive, motor, emotional, or social differences among the three groups. In addition, there was no correlation between music perception skills and any of the social or emotional measures. These results provide a baseline for an ongoing longitudinal investigation of the effects of music training.

Global point signature for shape analysis of carpal bones.

Abstract: We present a method based on spectral theory for the shape analysis of carpal bones of the human wrist. We represent the cortical surface of the carpal bone in a coordinate system based on the eigensystem of the two-dimensional Helmholtz equation. We employ a metric—global point signature (GPS)—that exploits the scale and isometric invariance of eigenfunctions to quantify overall bone shape. We use a fast finite-element-method to compute the GPS metric. We capitalize upon the properties of GPS representation—such as stability, a standard Euclidean (l2) metric definition, and invariance to scaling, translation and rotation—to perform shape analysis of the carpal bones of ten women and ten men from a publicly-available database. We demonstrate the utility of the proposed GPS representation to provide a means for comparing shapes of the carpal bones across populations.

Improved B0-distortion correction in diffusion MRI using interlaced q-space sampling and constrained reconstruction

Abstract: Purpose To enable high-quality correction of susceptibility-induced geometric distortion artifacts in diffusion magnetic resonance imaging (MRI) images without increasing scan time. Theory and Methods A new method for distortion correction is proposed based on subsampling a generalized version of the state-of-the-art reversed-gradient distortion correction method. Rather than acquire each q-space sample multiple times with different distortions (as in the conventional reversed-gradient method), we sample each q-space point once with an interlaced sampling scheme that measures different distortions at different q-space locations. Distortion correction is achieved using a novel constrained reconstruction formulation that leverages the smoothness of diffusion data in q-space. Results The effectiveness of the proposed method is demonstrated with simulated and in vivo diffusion MRI data. The proposed method is substantially faster than the reversed-gradient method, and can also provide smaller intensity errors in the corrected images and smaller errors in derived quantitative diffusion parameters. Conclusion The proposed method enables state-of-the-art distortion correction performance without increasing data acquisition time. Magn Reson Med 72:1218–1232, 2014. © 2013 Wiley Periodicals, Inc.

Sparsity Constrained Mixture Modeling for the Estimation of Kinetic Parameters in Dynamic PET

Abstract: The estimation and analysis of kinetic parameters in dynamic positron emission tomography (PET) is frequently confounded by tissue heterogeneity and partial volume effects. We propose a new constrained model of dynamic PET to address these limitations. The proposed formulation incorporates an explicit mixture model in which each image voxel is represented as a mixture of different pure tissue types with distinct temporal dynamics. We use Cramer-Rao lower bounds to demonstrate that the use of prior information is important to stabilize parameter estimation with this model. As a result, we propose a constrained formulation of the estimation problem that we solve using a two-stage algorithm. In the first stage, a sparse signal processing method is applied to estimate the rate parameters for the different tissue compartments from the noisy PET time series. In the second stage, tissue fractions and the linear parameters of different time activity curves are estimated using a combination of spatial-regularity and fractional mixture constraints. A block coordinate descent algorithm is combined with a manifold search to robustly estimate these parameters. The method is evaluated with both simulated and experimental dynamic PET data.

MAP reconstruction for Fourier rebinned TOF-PET data

Abstract: Time-of-flight (TOF) information improves the signal-to-noise ratio in positron emission tomography (PET). The computation cost in processing TOF-PET sinograms is substantially higher than for nonTOF data because the data in each line of response is divided among multiple TOF bins. This additional cost has motivated research into methods for rebinning TOF data into lower dimensional representations that exploit redundancies inherent in TOF data. We have previously developed approximate Fourier methods that rebin TOF data into either three-dimensional (3D) nonTOF or 2D nonTOF formats. We refer to these methods respectively as FORET-3D and FORET-2D. Here we describe maximum a posteriori (MAP) estimators for use with FORET rebinned data. We first derive approximate expressions for the variance of the rebinned data. We then use these results to rescale the data so that the variance and mean are approximately equal allowing us to use the Poisson likelihood model for MAP reconstruction. MAP reconstruction from these rebinned data uses a system matrix in which the detector response model accounts for the effects of rebinning. Using these methods we compare the performance of FORET-2D and 3D with TOF and nonTOF reconstructions using phantom and clinical data. Our phantom results show a small loss in contrast recovery at matched noise levels using FORET compared to reconstruction from the original TOF data. Clinical examples show FORET images that are qualitatively similar to those obtained from the original TOF-PET data but with a small increase in variance at matched resolution. Reconstruction time is reduced by a factor of 5 and 30 using FORET3D+MAP and FORET2D+MAP respectively compared to 3D TOF MAP, which makes these methods attractive for clinical applications.

Linear transform for diffusion mri

Abstract: Samples of a Fourier transform of a signal may be received that are distributed in a generally spherically-shaped pattern on a surface of at least one sphere. The samples may be assembled to form a profile function having a domain that is a surface of at least one sphere. Information indicative of at least one property of the signal may be determined by applying a spherical linear transform to the profile function. The spherical linear transform may use more than just an equator of the profile function for each of multiple orientations.

Estimation of cortical thickness from T1-weighted MRI images using tissue fractions

Abstract: • Thickness of the human cerebral cortex is an important phenotypical feature. Cortical thickness variations can be helpful in characterizing differences in cognitive performance, cortical changes associated with aging, and neurological disorders such as Alzheimer’s and Parkinson’s disease. • Methods based on computing distances between crisp inner and outer cortical boundaries are sensitive to partial volume effects and selection of the threshold used to define transition between white matter, gray matter and CSF regions. • We propose a novel method based on the anisotropic heat equation which exploits partial fraction tissue classification maps for accurate estimation. • We compare our approach with other methods and histological findings reported in the literature. We also use our method to study left-right hemispherical thickness differences in a large population. • Our results show a larger effect size than the other methods we tested, which is consistent with improved accuracy in detecting subtle differences in cortical thickness.

A registration-based segmentation method with application to adiposity analysis of mice microCT images

Abstract: Obesity is a global health problem, particularly in the U.S. where one third of adults are obese. A reliable and accurate method of quantifying obesity is necessary. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) are two measures of obesity that reflect different associated health risks, but accurate measurements in humans or rodent models are difficult. In this paper we present an automatic, registration-based segmentation method for mouse adiposity studies using microCT images. We co-register the subject CT image and a mouse CT atlas. Our method is based on surface matching of the microCT image and an atlas. Surface-based elastic volume warping is used to match the internal anatomy. We acquired a whole body scan of a C57BL6/J mouse injected with contrast agent using microCT and created a whole body mouse atlas by manually delineate the boundaries of the mouse and major organs. For method verification we scanned a C57BL6/J mouse from the base of the skull to the distal tibia. We registered the obtained mouse CT image to our atlas. Preliminary results show that we can warp the atlas image to match the posture and shape of the subject CT image, which has significant differences from the atlas. We plan to use this software tool in longitudinal obesity studies using mouse models.

4856 INVERSION: A robust method for co-registration of MPRAGE and Diffusion MRI images

Abstract: 2image to look like an MPRAGE image. This transformation means that inter-modal image registration methods can be replaced by simpler and more robust methods designed for registering images from the same-modality. In this work, we invert the intensity histogram of the b=0s/mm 2 image, and then register this image to the MPRAGE image using the SSD cost function. In practice, we also apply histogram matching to the inverted b=0s/mm 2 image and MPRAGE image to further refine the intensity match. In order to study the robustness of the proposed method we acquired an MPRAGE image, a diffusion dataset (single-shot EPI, TE=115ms, TR=10s, 65 diffusion-weighted image with b=2500s/mm 2 , one b=0s/mm 2 , 2x2x2mm) and a B0 inhomogeneity map for a single subject. The diffusion dataset was first corrected for susceptibility induced distortion using the acquired inhomogeneity map 4 . Then the MPRAGE image was aligned to b=0s/mm 2 image using a manually guided rigid registration procedure 5 . This manual result was used as a gold-standard to compare the accuracy of different automatic registration methods. In order to understand the properties of different cost functions, we studied how they changed as a function of mis-registration (translation along the x-axis). Consistency of the solutions obtained by different cost functions was evaluated by applying 36 known rigid transformations to the MPRAGE image and assessing the registration quality achieved with each cost function using our implementation of CR and NMI. The registration accuracy was quantified using RMS error 3 . All cost functions were optimized using simple gradient decent.