Wavelet Image Restoration Using Multifractal Priors

Bayesian image restoration has had a long history of successful application but one of the limitations that has prevented more widespread use is that the methods are generally computationally intensive. The authors recently addressed this issue by developing a method that performs the image enhancement in an orthogonal space (Fourier space in that case) which effectively transforms the problem from a large multivariate optimization problem to a set of smaller independent univariate optimization problems. The current paper extends these methods to analysis in another orthogonal basis, wavelets. While still providing the computational efficiency obtained with the original method in Fourier space, this extension allows more flexibility in adapting to local properties of the images, as well as capitalizing on the long history of developments for wavelet shrinkage methods. In addition, wavelet methods, including empirical Bayes specific methods, have recently been developed to effectively capture multifractal properties of images. An extension of these methods is utilized to enhance the recovery of textural characteristics of the underlying image. These enhancements should be beneficial in characterizing textural differences such as those occurring in medical images of diseased and healthy tissues. The Bayesian framework defined in the space of wavelets provides a flexible model that is easily extended to a variety of imaging contexts.Comment: 19 pages, 4 figure

Spatially Extended fMRI Signal Response to Stimulus in Non-Functionally Relevant Regions of the Human Brain: Preliminary Results

The blood-oxygenation level dependent (BOLD) haemodynamic response function (HDR) in functional magnetic resonance imaging (fMRI) is a delayed and indirect marker of brain activity. In this single case study a small BOLD response synchronised with the stimulus paradigm is found globally, i.e. in all areas outside those of expected activation in a single subject study. The nature of the global response has similar shape properties to the archetypal BOLD HDR, with an early positive signal and a late negative response typical of the negative overshoot. Fitting Poisson curves to these responses showed that voxels were potentially split into two sets: one with dominantly positive signal and the other predominantly negative. A description, quantification and mapping of the global BOLD response is provided along with a 2 × 2 classification table test to demonstrate existence with very high statistical confidence. Potential explanations of the global response are proposed in terms of 1) global HDR balancing; 2) resting state network modulation; and 3) biological systems synchronised with the stimulus cycle. Whilst these widespread and low-level patterns seem unlikely to provide additional information for determining activation in functional neuroimaging studies as conceived in the last 15 years, knowledge of their properties may assist more comprehensive accounts of brain connectivity in the future

A statistical method (cross-validation) for bone loss region detection after spaceflight.

Astronauts experience bone loss after the long spaceflight missions. Identifying specific regions that undergo the greatest losses (e.g. the proximal femur) could reveal information about the processes of bone loss in disuse and disease. Methods for detecting such regions, however, remains an open problem. This paper focuses on statistical methods to detect such regions. We perform statistical parametric mapping to get t-maps of changes in images, and propose a new cross-validation method to select an optimum suprathreshold for forming clusters of pixels. Once these candidate clusters are formed, we use permutation testing of longitudinal labels to derive significant changes

K-Bayes Reconstruction for Perfusion MRI I: Concepts and Application

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities, such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. In this study, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach (described in detail in Part II: Modeling and Technical Development) combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT

Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113725/1/jmri24883.pd

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival

A method for determining venous contribution to BOLD contrast sensory activation

While BOLD contrast reflects haemodynamic changes within capillaries serving neural tissue, it also has a venous component. Studies that have determined the relation of large blood vessels to the activation map indicate that veins are the source of the largest response, and the most delayed in time. It would be informative if the location of these large veins could be extracted from the properties of the functional responses, since vessels are not visible in BOLD contrast images. The present study describes a method for investigating whether measures taken from the functional response can reliably predict vein location, or at least be useful in down-weighting the venous contribution to the activation response, and illustrates this method using data from one subject. We combined fMRI at 3 Tesla with high-resolution anatomical imaging and MR venography to test whether the intrinsic properties of activation time courses corresponded to tissue type. Measures were taken from a gamma fit to the functional response. Mean magnitude showed a significant effect of tissue type (P veins ≈ grey matter > white matter. Mean delays displayed the same ranking across tissue types (P grey matter. However, measures for all tissue types were distributed across an overlapping range. A logistic regression model correctly discriminated 72% of the veins from grey matter in the absence of independent information of macroscopic vessels (ROC=0.72). Whilst tissue classification was not perfect for this subject, weighting the T contrast by the predicted probabilities materially reduced the venous component to the activation map

K-Bayes Reconstruction for Perfusion MRI II: Modeling and Technical Development

Despite the continued spread of magnetic resonance imaging (MRI) methods in scientific studies and clinical diagnosis, MRI applications are mostly restricted to high-resolution modalities such as structural MRI. While perfusion MRI gives complementary information on blood flow in the brain, its reduced resolution limits its power for detecting specific disease effects on perfusion patterns. This reduced resolution is compounded by artifacts such as partial volume effects, Gibbs ringing, and aliasing, which are caused by necessarily limited k-space sampling and the subsequent use of discrete Fourier transform (DFT) reconstruction. Here, a Bayesian modeling procedure (K-Bayes) is developed for the reconstruction of perfusion MRI. The K-Bayes approach combines a process model for the MRI signal in k-space with a Markov random field prior distribution that incorporates high-resolution segmented structural MRI information. A simulation study, described in Part I (Concepts and Applications), was performed to determine qualitative and quantitative improvements in K-Bayes reconstructed images compared with those obtained via DFT. The improvements were validated using in vivo perfusion MRI data of the human brain. The K-Bayes reconstructed images were demonstrated to provide reduced bias, increased precision, greater effect sizes, and higher resolution than those obtained using DFT