Showing papers by "Richard M. Leahy published in 2013"
TL;DR: It is concluded that the sample PLV provides equivalent information to the cross-correlation of the two complex time series and the von Mises and Gaussian models are suitable for representing relative phase in application to LFP data from a visually-cued motor study in macaque.
252 citations
TL;DR: The experimental data analysis revealed that the denoising technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.
Abstract: Objective
Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM).
Theory
NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch.
Methods
To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches – Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches.
Results
The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.
121 citations
TL;DR: This article reviews research efforts over the past 20 years to develop model-based PET reconstruction methods based on the use of both Markov random field priors and joint information or entropy measures, and discusses approaches based onThe general framework for these methods is described, and their performance and longer-term potential and limitations are discussed.
95 citations
TL;DR: To develop an automatic registration‐based segmentation algorithm for measuring abdominal adipose tissue depot volumes and organ fat fraction content from three‐dimensional water–fat MRI data, and to evaluate its performance against manual segmentation.
Abstract: Purpose:
To develop an automatic registration-based segmentation algorithm for measuring abdominal adipose tissue depot volumes and organ fat fraction content from three-dimensional (3D) water–fat MRI data, and to evaluate its performance against manual segmentation.
Materials and Methods:
Data were obtained from 11 subjects at two time points with intermediate repositioning, and from four subjects before and after a meal with repositioning. Imaging was performed on a 3 Tesla MRI, using the IDEAL chemical-shift water–fat pulse sequence. Adipose tissue (subcutaneous—SAT, visceral—VAT) and organs (liver, pancreas) were manually segmented twice for each scan by a single trained observer. Automated segmentations of each subject's second scan were generated using a nonrigid volume registration algorithm for water–fat MRI images that used a b-spline basis for deformation and minimized image dissimilarity after the deformation. Manual and automated segmentations were compared using Dice coefficients and linear regression of SAT and VAT volumes, organ volumes, and hepatic and pancreatic fat fractions (HFF, PFF).
Results:
Manual segmentations from the 11 repositioned subjects exhibited strong repeatability and set performance benchmarks. The average Dice coefficients were 0.9747 (SAT), 0.9424 (VAT), 0.9404 (liver), and 0.8205 (pancreas); the linear correlation coefficients were 0.9994 (SAT volume), 0.9974 (VAT volume), 0.9885 (liver volume), 0.9782 (pancreas volume), 0.9996 (HFF), and 0.9660 (PFF). When comparing manual and automated segmentations, the average Dice coefficients were 0.9043 (SAT volume), 0.8235 (VAT), 0.8942 (liver), and 0.7168 (pancreas); the linear correlation coefficients were 0.9493 (SAT volume), 0.9982 (VAT volume), 0.9326 (liver volume), 0.8876 (pancreas volume), 0.9972 (HFF), and 0.8617 (PFF). In the four pre- and post-prandial subjects, the Dice coefficients were 0.9024 (SAT), 0.7781 (VAT), 0.8799 (liver), and 0.5179 (pancreas); the linear correlation coefficients were 0.9889, 0.9902 (SAT, and VAT volume), 0.9523 (liver volume), 0.8760 (pancreas volume), 0.9991 (HFF), and 0.6338 (PFF).
Conclusion:
Automated intra-subject registration-based segmentation is potentially suitable for the quantification of abdominal and organ fat and achieves comparable quantitative endpoints with respect to manual segmentation. J. Magn. Reson. Imaging 2013;37:423–430. © 2012 Wiley Periodicals, Inc.
47 citations
TL;DR: A novel family of linear transforms that can be applied to data collected from the surface of a 2-sphere in three-dimensional Fourier space that can outperform existing state-of-the-art orientation estimation methods with respect to measures such as angular resolution and robustness to noise and modeling errors.
30 citations
TL;DR: A novel measure of interaction between regions of interest rather than individual signals is presented and can be used to identify causal relationships between striate and prestriate cortexes in cases where standard Granger causality is unable to identify statistically significant interactions.
10 citations
07 Apr 2013
TL;DR: A method to quantify point-wise erosive changes of wrist bones in IA patients undergoing treatment is developed and has potential to provide new imaging biomarkers to be used in clinical trials evaluating the efficacy of new arthritis drugs.
Abstract: New aggressive therapeutic options have recently become available to treat inflammatory arthritis (IA) and rheumatoid arthritis in particular. These treatments not only control joint destruction, they may also aid in new bone formation at sites of eroded bone. Separation of non-responders from responders to these treatments, is critical, and is known to lead to reduced disease burden, toxicity, side-effects and overall cost. The bones of the wrist are early targets of IA and are known to show response to therapy early. In this paper, we develop a method to quantify point-wise erosive changes of wrist bones in IA patients undergoing treatment. The method employs 3D registration-based morphometric analysis. Our results indicate that the proposed method has potential to improve sensitivity to small, early changes in bone erosion status. This study has potential to provide new imaging biomarkers to be used in clinical trials evaluating the efficacy of new arthritis drugs.
3 citations
07 Apr 2013
TL;DR: This work provides a theoretical analysis of linear spherical deconvolution methods in diffusion MRI, building off of a theoretical framework that was previously developed for model-free linear transforms of the Fourier 2-sphere.
Abstract: This work provides a theoretical analysis of linear spherical deconvolution methods in diffusion MRI, building off of a theoretical framework that was previously developed for model-free linear transforms of the Fourier 2-sphere. It is demonstrated that linear spherical deconvolution methods have an equivalent representation as model-free linear transform methods. This perspective is used to study the characteristics of linear spherical deconvolution from the point of view of the diffusion propagator. Practical results are shown with experimental brain MRI data.
2 citations
07 Apr 2013
TL;DR: This work presents two parametric methods to assess the statistical significance of network partitions, and therefore control against spurious subnetworks that may arise in random graphs, rather than self-organized brain networks.
Abstract: Brain networks are often explored with graph theoretical approaches, and community structures identified using modularity-based partitions. Despite the popularity of these methods, the significance of the obtained subnetworks is largely unaddressed in the literature. We present two parametric methods to assess the statistical significance of network partitions, and therefore control against spurious subnetworks that may arise in random graphs, rather than self-organized brain networks. We evaluate these methods with simulated data and resting state fMRI data.
1 citations
01 Nov 2013
TL;DR: An efficient new algorithm is proposed for ARCH model estimation and it is demonstrated that the proposed approach provides promising results that are distinct from the causality estimates obtained from simpler and more conventional signal causality models.
Abstract: Measurements of electrophysiological activity can be used to infer interactions between different regions of the human brain. In this work, we consider the use of an autoregressive conditional heteroscedasticity (ARCH) model to estimate causality in variance between different brain regions in simulation and continuously measured EEG data. We propose an efficient new algorithm for ARCH model estimation and demonstrate that the proposed approach provides promising results that are distinct from the causality estimates obtained from simpler and more conventional signal causality models.
01 Nov 2013
TL;DR: A novel measure is proposed, partial lagged coherence (PLC), which is more robust to cross-talk in the presence of interfering signals and is evaluated on the relative performance of IC, PLI and LC.
Abstract: Coherence is a widely used measure to investigate interaction between electrophysiological signals obtained from EEG or MEG. However spurious coherence between locations can be introduced through cross-talk or linear mixing between signals that occurs as a result of diffuse lead-field sensitivity (in sensor data) or limited spatial resolution (in cortical current density maps). Measures including the imaginary part of the coherence (IC), phase lag index (PLI) and Lagged coherence (LC) have all been proposed to overcome this problem. Each of these metrics use the fact that cross-talk will produce instantaneous or zero phase-lag interactions between signals. By constructing measures such as these, all of which have zero value in the case of instantaneous mixing only, we can reduce sensitivity to cross-talk. However, none of these measures considers the effect of interference from external sources. In this paper we first investigate the relative performance of IC, PLI and LC. We then propose and evaluate a novel measure, partial lagged coherence (PLC), which is more robust to cross-talk in the presence of interfering signals.