|Image Simulation & Analysis|
Inverse Consistent Geometric Flow
Based Nonlinear Registration Driven by Mutual Information
Guozhi Tao1, Renjie He1, Sushmita Datta1, Ponnada A. Narayana1
1University of Texas Medical School at Houston, Houston, Texas, USA
A diffeomorphic registration technique that explicitly includes inverse consistent constraint is developed and applied on normal human brain. The maximum inverse consistency error with our method is less than 0.07 voxels. This is ten times smaller than the recently reported values.
|10:42||485.||iMRI Data Repository for
Validation of Brain Non-Rigid Registration Algorithms
Neculai Archip1, Ion-Florin Talos1
1Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
This paper presents the development of a freely accessible, on-line repository of pre-and intra-operative MR-images derived from patients with hemispheric brain tumors, to serve as a validation platform for non-rigid registration algorithms.
Computational Method for the Measurement of Amyloid Plaque Load in the
APP Transgenic Mouse Brain
George Iordanescu1, 2, Palamadai Venkatasubramanian1, 2, Alice M. Wyrwicz1, 2
1ENH Research Institute, Evanston, Illinois, USA; 2Northwestern University Feinberg School of Medicine, Evanston, Illinois, USA
We present a novel automatic method based on simulated flooding and data Laplacian to segment the AD plaques in mouse MR brain images, and show how the method can be used for a detailed analysis of plaque characteristics. The segmentation map can be used to asses individual plaques (average profile, volume, intensity variability grade), to compute the plaques load or the plaques distribution for specific brain structures. Since no assumptions are made on the plaques shape or size, the proposed method can be used to analyze data for different AD stages or mouse strains, where plaques variability should be expected.
Improved Accuracy and Smoothed
Lipid Content by Maximum a Posteriori Estimation in CHESS Ratio Images
Wilbur C. K. Wong1, David Johnson2, Chris Flask3, Paul Ernsberger2, David Wilson2, 3
1The Hong Kong University of Science & Technology, Kowloon, Hong Kong; 2Case Western Reserve University, Cleveland, Ohio, USA; 3University Hospitals of Cleveland, Cleveland, Ohio, USA
We are using MR to quantify fat depots (e.g., visceral, subcutaneous, hepatic, muscular) so as to determine the role of genetic, environmental, and therapeutic factors on lipid accumulation, metabolism, and disease states. In this report, we studied lean spontaneously hypertensive rats (SHRs), a genetic variant prone to obesity (SHROBs), and animals given a high fat, high sucrose diet creating dietary obese animals (SHR-DOs). Animals were imaged with and without CHESS water-suppression. Ratio images exactly compensate for receive coil sensitivity inhomogeneity and enable the creation of gray-scale based automated analysis. A ratio image model was created for measuring lipid content in subcutaneous and visceral depots. We analyzed the statistical property of the ratio of two noisy signals and developed a maximum a posteriori (MAP) estimate of the lipid content in each voxel. We simulated the partial volume effect in a digital phantom with known fat content in each voxel. Even if relaxivities were corrected, ratio images overestimated the true volume of fat by 23%. In a cohort of rats, the MAP correction reduced visceral adipose tissue volume by 20%. We identified obesity phenotypes and characterized this model of metabolic syndrome.
Quantitative Contrast Enhancement
Maps of the Carotid Atherosclerotic Plaque In-Vivo: Methodology and
Niranjan Balu1, Vasily Yarnykh1, William Kerwin1, Jianming Cai2, Chun Yuan1
1University of Washington, Seattle, USA; 2Chinese PLA General Hospital, Beijing, People's Republic of China
Contrast enhancement (CE) patterns of the carotid atherosclerotic plaque are indicative of plaque neovasculature and inflammation. Therefore quantitative CE information from black-blood CE-MRI may provide additional markers of plaque vulnerability. We demonstrate a fast, semi-automated method to obtain quantitative CE maps (QCEM) of the carotid plaque in-vivo. Luminal surface, adventitial surface, plaque shoulder and core subregions were automatically segmented for their respective QCEM. Group histograms and histogram statistics from subregion QCEM of 24 patients were compared between plaques of high and low plaque burden. Larger plaque locations showed significantly higher core and shoulder enhancement indicating possible inflammation/neovasculature in these regions.
Efficient MRI Simulation Via
Integration of the Signal Equation Over Triangulated Surfaces
Luca Antiga1, David Andrew Steinman2
1Mario Negri Institute for Pharmacological Research, Bergamo, Italy; 2University of Toronto, Toronto, Canada
MRI simulation of anatomically realistic objects can be inefficient owing to the need to discretize these objects volumetrically. Here we present an efficient simulation technique based on integrating the MRI signal equation over a surface rather than volume discretization.
Magnetization Transfer Imaging Using Balanced SSFP
Monika Gloor1, Klaus Scheffler1, Oliver Bieri1
1University Hospital / University of Basel, Basel, Switzerland
In tissues, the signal of balanced steady-state free precession (bSSFP) is considerably reduced from magnetization transfer (MT). An extended bSSFP signal equation is derived, based on a binary spin-bath model including MT effects. Using this new bSSFP signal model, quantitative MT model parameters such as the fractional pool size, corresponding magnetization exchange rates, and relaxation times are estimated in human brain. The results show high correlation with the ones from standard methods, but benefit from bSSFP’s short acquisition times and high signal-to-noise ratios. This allows the acquisition of isotropic high resolution quantitative MT maps within clinically feasible acquisition times.
Optimal Sample Parameter Estimates
from Phased Array Coil Data Utilizing Joint Bayesian Analysis
James D. Quirk1, Alexander L. Sukstanskii1, G Larry Bretthorst1, Dmitriy A. Yablonskiy1, 2
1Washington University School of Medicine, St. Louis, Missouri, USA; 2Washington University, St. Louis, Missouri, USA
We demonstrate that joint Bayesian analysis of array coil data offers a "worry-free" method for obtaining optimal estimates of sample parameters. A theory predicting the parameter estimate uncertainty was developed and validated on simulated data. Comparisons between joint analysis and more traditional coil combination methods (e.g. sensitivity weighted, SOS) indicate that estimates from joint analysis have equivalent or superior precision and accuracy on phased and magnitude data, without the use of reference scans. Combining channels without the optimal weighting factor (signal amplitude over noise power) can corrupt the parameter estimation and produce inferior results to a single channel analysis.
Phantom for Evaluation of MR Estimation Techniques
Christian Graff1, Eric Clarkson1, Eric K. Outwater1, Maria I. Altbach1
1University of Arizona, Tucson, Arizona , USA
Computer-generated phantoms provide a convenient way to evaluate MR parameter estimation algorithms. However, if the phantom data is significantly different from in vivo data, results from phantom studies may not be predictive of performance in vivo. We have constructed a computer-generated abdomen phantom which contains realistic models for parameter variability, coil sensitivities and other practical effects.
A Flexible Software Phantom for
Generating Realistic Dynamic Contrast-Enhanced MR Images of Abdominal
Anita Banerji1, Angela Caunce1, Yvon Watson1, Chris Rose1, Giovanni Buonaccorsi1, Geoff Parker1
1The University of Manchester, Manchester, UK
The software phantom presented in this work generates synthetic images from known ground truth for use in the validation of post-acquisition image processing algorithms, for example registration techniques for DCE-MR time series. The design of the phantom is flexible allowing images for various different imaging scenarios and modalities to be produced. A range of synthetic data sets can be generated that are relevant to the image processing algorithm in order to test robustness, accuracy and precision. Example DCE-MR images with realistic anatomy based on in-vivo data sets are shown.