KEYWORDS: Magnetic resonance imaging, Statistical analysis, Photovoltaics, Signal to noise ratio, Monte Carlo methods, Data modeling, Image segmentation, Statistical modeling, Interference (communication), Tissues
We are developing small animal imaging techniques to characterize the kinetics of lipid accumulation/reduction of fat
depots in response to genetic/dietary factors associated with obesity and metabolic syndromes. Recently, we developed
an MR ratio imaging technique that approximately yields lipid/{lipid + water}. In this work, we develop a statistical
model for the ratio distribution that explicitly includes a partial volume (PV) fraction of fat and a mixture of a Rician and
multiple Gaussians. Monte Carlo hypothesis testing showed that our model was valid over a wide range of coefficient of
variation of the denominator distribution (c.v.: 0-0:20) and correlation coefficient among the numerator and
denominator (&rgr; 0-0.95), which cover the typical values that we found in MRI data sets (c.v.: 0:027-0:063, &rgr;: 0:50-0:75). Then a maximum a posteriori (MAP) estimate for the fat percentage per voxel is proposed. Using a
digital phantom with many PV voxels, we found that ratio values were not linearly related to PV fat content and that our
method accurately described the histogram. In addition, the new method estimated the ground truth within +1.6% vs.
+43% for an approach using an uncorrected ratio image, when we simply threshold the ratio image. On the six
genetically obese rat data sets, the MAP estimate gave total fat volumes of 279 ± 45mL, values ≈ 21% smaller than
those from the uncorrected ratio images, principally due to the non-linear PV effect. We conclude that our algorithm can
increase the accuracy of fat volume quantification even in regions having many PV voxels, e.g. ectopic fat depots.
With the ever-increasing complexity of science and engineering, many important research problems are being
addressed by collaborative, multidisciplinary teams. We present a web-based collaborative environment for small
animal imaging research, called the Mouse Imaging Collaboration Environment (MICE). MICE provides an effective
and user-friendly tool for managing and sharing of the terabytes of high-resolution and high-dimension
image data generated at small animal imaging core facilities. We describe the design of MICE and our experience
in the implementation and deployment of a beta-version baseline-MICE. The baseline-MICE provides an
integrated solution from image data acquisition to end-user access and long-term data storage at our UH/Case
Small Animal Imaging Resource Center. As image data is acquired from scanners, it is pushed to the MICE
server which automatically stores it in a directory structure according to its DICOM metadata. The directory
structure reflects imaging modality, principle investigators, animal models, scanning dates and study details.
Registered end-users access this imaging data through an authenticated web-interface. Thumbnail images are
created by custom scripts running on the MICE server while data down-loading is achieved through standard
web-browser ftp. MICE provides a security infrastructure that manages user roles, their access privileges such
as read/write, and the right to modify the access privileges. Additional data security measures include a two
server paradigm with the Web access server residing outside a network firewall to provide access through the Internet,
and the imaging data server - a large RAID storage system supporting flexible backup policies - residing
behind the protected firewall with a dedicated link to the Web access server. Direct network link to the RAID
storage system outside the firewall other than this dedicated link is not permitted. Establishing the initial image
directory structure and letting the project leader manage data access through a web-interface represent Phase I
implementation. In Phase II, features for uploading image analysis scripts and results back to the MICE server
will be implemented, as well as mechanisms facilitating asynchronous and synchronous discussion, annotation,
and analysis. Most of MICE features are being implemented in the Plone5 object-oriented database environment
which greatly shortens developmental time and effort by the reuse of a variety of Plone's open-source modules
for Content Management Systems.7, 8 The open-source modules are well suited as an implementation basis of
MICE and provide data integration as a built-in primitive.
KEYWORDS: Magnetic resonance imaging, Tissues, Genetics, Image segmentation, Image processing, In vivo imaging, Blood, Image analysis, Medical imaging, Scanners
Obesity is a global epidemic and a comorbidity for many diseases. We are using MRI to characterize obesity in rodents, especially with regard to visceral fat. Rats were scanned on a 1.5T clinical scanner, and a T1W, water-spoiled image (fat only) was divided by a matched T1W image (fat + water) to yield a ratio image related to the lipid content in each voxel. The ratio eliminated coil sensitivity inhomogeneity and gave flat values across a fat pad, except for outlier voxels (> 1.0) due to motion. Following sacrifice, fat pad volumes were dissected and measured by displacement in canola oil. In our study of 6 lean (SHR), 6 dietary obese (SHR-DO), and 9 genetically obese rats (SHROB), significant differences in visceral fat volume was observed with an average of 29±16 ml increase due to diet and 84±44 ml increase due to genetics relative to lean control with a volume of 11±4 ml. Subcutaneous fat increased 14±8 ml due to diet and 198±105 ml due to genetics relative to the lean control with 7±3 ml. Visceral fat strongly correlated between MRI and dissection (R2 = 0.94), but MRI detected over five times the subcutaneous fat found with error-prone dissection. Using a semi-automated images segmentation method on the ratio images, intra-subject variation was very low. Fat pad composition as estimated from ratio images consistently differentiated the strains with SHROB having a greater lipid concentration in adipose tissues. Future work will include in vivo studies of diet versus genetics, identification of new phenotypes, and corrective measures for obesity; technical efforts will focus on correction for motion and automation in quantification.
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