| Diffusion: Biophysical Foundations, Accuracy & Reproducibility |
|
Wednesday 22 April 2009 |
| Room 316BC |
10:30-12:30 |
Moderators: |
Yaniv Assaf and Sharon S. Peled |
|
|
| |
|
10:30 |
449. |
Experimentally Measured
Intracellular Water at Very Short Diffusion Times |
|
|
|
Kevin D. Harkins1,
Jean-Phillipe Galons2, Joe L. Divijak1,
Theodore P. Trouard1
1Biomedical Engineering, University of
Arizona, Tucson, AZ, USA; 2Radiology,
University of Arizona, Tucson, AZ, USA |
|
|
|
The apparent diffusion
coefficient is a measure of water diffusion in
tissue, and is sensitive to cellular properties and
tissue integrity. In this work, we present estimates
of the intrinsic intracellular diffusivity derived
from hollow-fiber bioreactor cell culture
measurements using oscillating gradient diffusion
measurements at very short diffusion times. The ADC
of intracellular approaches 2.0 to 2.4 mm2 /ms as
the diffusion time approaches 0 ms. |
|
|
|
|
|
10:42 |
450. |
Permeability and Surface Area
of Cell Membranes from the DWI Signal |
|
|
|
Dmitry S. Novikov1,
Jens H. Jensen1, Joseph A. Helpern1
1Radiology, NYU Medical Center, New York, NY,
USA |
|
|
|
Diffusion of water in
tissues is strongly restricted on the cellular
length scale. The consequence of such a restriction
is a ubiquitous non-Gaussian shape of the
diffusion-weighted imaging (DWI) signal. We
demonstrate how a non-Gaussian shape of the DWI
signal can originate solely due to the presence of
cell membranes, with no diffusivity difference
between the intra- and extra-cellular compartments.
We find that the effective diffusivity acquires
frequency dependence. We relate the cell membrane
permeability and surface-to-volume ratio to
experimentally relevant parameters, such as
dispersive diffusivity and time-dependent kurtosis. |
|
|
|
|
|
10:54 |
451. |
Fast Monte Carlo Simulations
Replace Analytical Tissue Models in Diffusion MRI |
|
|
|
Markus Nilsson1,
Erik Alerstam2, Sara Brockstedt3,
Ronnie Wirestam1, Freddy Ståhlberg1,4,
Jimmy Lätt3
1Department of Medical Radiation Physics, Lund
University, Lund, Sweden; 2Department of
Physics, Lund University, Lund, Sweden; 3MR
Department, Center for Medical Imaging and
Physiology, Lund University Hospital, Lund, Sweden;
4Department of Diagnostic Radiology, Lund
University, Lund, Sweden |
|
|
|
Evaluation of diffusion
MRI data obtained with different diffusion times
currently involves non-linear fitting of analytical
models. These models rely on assumptions about the
tissue as well as on mathematical approximations in
the derivation of the signal expression. Replacing
the non-linear fitting by a lookup database,
constructed from fast Monte Carlo simulations,
improves and speeds up the evaluations. This novel
approach was demonstrated by investigating the
estimated posterior distributions for four tissue
parameters, i.e. the intracellular volume fraction,
the cell diameter, the intracellular exchange time
and the apparent diffusion coefficient, given
simulated signal-versus-b curves with added noise. |
|
|
|
|
|
11:06 |
452. |
MR Characterization of
Compartment Shape Anisotropy (CSA) |
| |
|
Evren Ozarslan1
1STBB / LIMB / NICHD, National Institutes of
Health, Bethesda, MD, USA |
|
|
|
Anisotropy observed in
diffusion-weighted acquisitions is influenced by the
shape of the cells (compartment shape anisotropy,
CSA) and any coherence in the alignment of the
population of cells (ensemble anisotropy, EA). We
show that CSA and EA can be probed simultaneously
and differentiated if double pulsed field gradient
(double-PFG) sequences are employed. To this end,
expressions for the MR signal intensity from capped
cylinders with completely arbitrary parameters of
the double-PFG sequence are derived. Our findings
suggest that simultaneous noninvasive measurements
of cell size, eccentricity and orientation
distributions may be possible using relatively small
gradient strengths. |
|
|
|
|
|
11:18 |
453. |
Feasibility of in Vivo
Metabolites Diffusion Tensor Assessment |
|
|
|
Nicolas Kunz1,2,
Stéphane Sizonenko2, Rolf Gruetter1,3
1Laboratory of functional and metabolic
imaging (LIFMET), Ecole Polytechnique Fédérale de
Lausanne, Lausanne, Switzerland; 2Division
of Child Growth & Development, Dept. of Pediatrics,
University of Geneva, Geneva, Switzerland; 3Department
of Radiology, University of Lausanne and Geneva,
Switzerland |
|
|
|
DTI is based on water
molecules’ diffusion motion, which is present in
both intra- and extra-cellular spaces. On the other
hand, DW-MRS uses metabolites as diffusion markers
by combining MRS with diffusion weighting technique
and provides information limited to the
intra-cellular compartment. In this study, DW-MRS
shows that the principal diffusion directions of the
metabolites were aligned to the water along the
corpus callosum fibers, and is likely to shed light
on the nature of the diffusion signal. |
|
|
|
|
|
11:30 |
454. |
On the Effects of Dephasing
Due to Local Gradients in DTI Experiments: Relevance
for DTI Fiber Phantoms |
|
|
|
Frederik Bernd Laun1,
Sandra Huff1, Bram Stieltjes
1Medical Physics in Radiology, German
Cancer Research Center, Heidelberg,
Baden-Württemberg, Germany |
|
|
|
Former versions of DTI
phantoms could not be oriented arbitrarily towards
B0 due to susceptibility induced local gradients. We
countered this limitation by matching
susceptibilities of restricting structure and fluid.
This is achieved by solving sodium chloride. Thus,
T2 becomes independent of orientation and diffusion
measurements become reliable. |
| |
|
|
|
11:42 |
455. |
Comparison of DTI Fiber Tracks
with Light Microscopy of Myelinated Fibers |
| |
|
Ann Sunah Choe1,2,
Xin Hong1,2, Daniel Christopher Colvin1,2,
Iwona Stepniewska3, Zhaohua Ding1,2,
Adam W. Anderson1,2
1Vanderbilt University Institute of Imaging
Science, Vanderbilt University, Nashville, TN, USA;
2Department of Biomedical Engineering,
Vanderbilt University, Nashville, TN, USA; 3Department
of Psychology, Vanderbilt University, Nashville, TN,
USA |
| |
|
Fibers tracked using
Diffusion Tensor Imaging (DTI) are directly compared
with myelin stained fibers on a microscopic level.
Gold standard measurements of fiber orientation and
spread from micrographs enabled us to investigate
the challenges of DTI fiber tracking in brain
tissue. Limitations due to partial volume averaging
and image noise are readily observed. |
| |
|
|
|
11:54 |
456. |
Correlation Between R2* and FA
in Human Brain White Matter |
| |
|
Tie-Qiang Li1,
Masaki Fukunaga2, Peter van Gelderen2,
Jeff H. Duyn2
1Medical Physics, Karolinska Huddinge,
Stockholm, Sweden; 2NINDS, NIH, Bethesda,
MD, USA |
| |
|
High resolution MRI at
7T suggests that T2* contrast may be sensitive to
white matter composition and microstructure. In this
study, the quantitative correlation between T2*
relaxation rate (R2*=1/ T2*) and diffusion
fractional anisotropy (FA) in white matter was
investigated in a dozen of normal volunteers at 3T
and 7T. |
| |
|
|
|
12:06 |
457. |
Evaluation of Within-Site and
Cross-Site Accuracy and Precision of DTI
Measurements Through a Multi-Center Human Volunteer
Study |
| |
|
Tong Zhu1,
Michelle Gaugh2, Xiaoxu Liu3,
Michael Taylor4, Yuen Tso5,
Giovanni Schifitto2, Constantin
Yiannoutsos6, Bradford Navia7,
Jianhui Zhong8
1Biomedical Engineering, University of
Rochester, Rochester, NY, USA; 2Neurology,
University of Rochester, Rochester, NY, USA; 3Electrical
Engineeering, University of Rochester, Rochester,
NY, USA; 44University of California San
Diego, San Diego, CA, USA; 5Stanford
University, Stanford, CA, USA; 6Indiana
University, Indianapolis, IN, USA; 7Tufts
University, Medford, MA, USA; 8Imaging
Sciences, University of Rochester, Rochester, NY,
USA |
| |
|
In a typical
neuroimaging multicenter DTI study, biases and
variations in data due to differences in scanners
among sites prevent pooling of data for conventional
statistical inferences. This is one of unsolved
critical issues we are facing for multi-center DTI
studies. In this study, multiple DTI data of a
healthy volunteer were acquired at three imaging
centers. Precision of DTI measurement of each center
was quantified by the bootstrap analysis of
measurement uncertainty while the accuracy (bias) of
measurement was evaluated by comparing DTI
parameters from each site to those from a “super”
data set with all data combined. Our study suggests
that, while precision level of DTI data from
different sites is not significantly affected by the
short-term variations of scanners at a site, the
bias of DTI data from each site will vary and reduce
the statistical power when data from multiple sites
are combined together. In order to facilitate the
multiple-center DTI study, a routine calibration
process to regularly measure the bias level is
necessary. |
| |
|
|
|
12:18 |
458. |
Statistical Assessment of the Effects of
Physiological Noise and Artifacts in a Population
Analysis of Diffusion Tensor MRI Data |
| |
|
Lindsay Walker1,
Lin-Ching Chang1,2, Efstathios Kanterakis3,
Luke Bloy3, Kristina Simonyan4,
Ragini Verma3, Carlo Pierpaoli1
1NICHD, NIH, Bethesda, MD, USA; 2Dept
of EE & CS, Catholic University of America,
Washington, DC, USA; 3Dept of Radiology,
University of Pennsylvania, Philadelphia, PA, USA;
4NINDS, NIH, Bethesda, MD, USA |
| |
|
Corrupted DWI data has
an effect on DTI derived quantities. An analysis of
the effect of physiological noise on statistical
analysis of a population is presented. Comparison of
non-robust and RESTORE robust tensor fitting shows
significant differences in the mean and variance of
anisotropy and mean diffusivity. The effects are
regionally varying across the brain, and not the
same for different tensor derived metrics. When
considering a statistical analysis of a population,
the effect of outliers may not be the same for both
patient and control groups. Statistically
significant results may originate from the presence
of outliers instead of pathology. |
|
|
|
|
|