Image Analysis: Applications |
Thursday 23 April 2009 |
Room 313BC |
10:30-12:30 |
Moderators: |
Joseph V. Hajnal and Min-Ying Lydia Su |
|
|
|
10:30 |
649. |
Left Ventricular Wall Motion
Abnormalities: Using Center Point Trajectory (CPT)
Mapping to Quantify Focal Lesions |
|
|
Ting Song1,
Jeffrey A. Stainsby2, Maureen N. Hood3,
Vincent B. Ho4
1Applied Science Laboratory, GE Healthcare,
Bethesda, MD, USA; 2Applied Science
Laboratory, GE Healthcare, Toronto, ON, Canada;
3Radiology, Uniformed Services University of
the Health Sciences and National Naval Medical
Center, Bethesda, MD, USA; 4Radiology,
Uniformed Services University of the Health Sciences
and National Naval Medical Center, Bethesda,
Bethesda, MD |
|
|
Visual inspection of
echocardiography or cardiac MR images remains the
current clinical gold standard. We present a novel
image-processing algorithm called Center Point
Trajectory (CPT) mapping for quantification of focal
left ventricular wall motion abnormalities. CPT
analysis yields amplitude and direction values to
define focal wall motion abnormalities. The
technique uses standard cine SSFP images and does
not require specialized MR pulse. The method can be
potentially used quantification of systolic and
diastolic wall motion changes and monitoring
pharmacologic testing. |
|
|
|
10:42 |
650. |
A Novel Method for Cine-CMR
Automated Assessment of Left Ventricular Diastolic
Dysfunction |
|
|
Keigo Kawaji1,2,
Noel Christopher Codella2, Christopher W.
Chu3, Richard B. Devereux3,
Martin R. Prince2, Yi Wang1,2,
Jonathan W. Weinsaft2,3
1Biomedical Engineering, Cornell University,
Ithaca, NY, USA; 2Radiology, Weill
Cornell Medical College, New York, USA; 3Medicine/Division
of Cardiology, Weill Cornell Medical College, New
York, USA |
|
|
Cardiac magnetic
resonance (CMR) is a well-established standard for
assessment of LV systolic function, but assessment
of diastolic function is limited and currently
requires additional imaging, which can be
time-consuming. We present a novel automated
approach based upon an LV segmentation algorithm (LV-METRIC)
that assesses diastolic function from SSFP cine-CMR
by generating ventricular filling profiles. Our
results demonstrate that automated segmentation
using LV-METRIC can generate multiple diastolic
parameters that are rapidly derivable, require no
additional imaging beyond standard cine-CMR, and
agree with echocardiographic measures of diastolic
function. |
|
|
|
10:54 |
651. |
Exploring Effective
Connectivity During Unilateral Movement in Stroke
Using Structural Equation Modeling |
|
|
Wan-wa Wong1,
Kai-yu Tong1, Fei Meng1,2,
Kwok-wing Tang3, Xiaorong Gao2,
Shangkai Gao2, Suk-tak Chan1
1The Hong Kong Polytechnic University, Hong
Kong, China; 2Tsinghua University,
Beijing, China; 3Queen Elizabeth
Hospital, Hong Kong, China |
|
|
The interactions between
brain areas after stroke is thought to be critical
information for the exploration of abnormal patterns
observed after stroke. Investigation of the
interactions will enable us to explore the network
in the damaged brain and explain how the adapted
network contributes to the functional task.
Structural equation modeling (SEM) is an approach to
quantify interactions among brain regions based on
connectivity models. An automated elaborative SEM
analysis together with bootstrapping validation were
applied to fMRI data of stroke subjects acquired
during unilateral movement using unaffected wrist.
Our findings showed that there are a total of 30
significant paths survived after validation test. |
|
|
|
11:06 |
652. |
Automated Computational
Analysis of Neonatal Hypoxic Injury and Implanted
Therapeutic Neuronal Stem Cells |
|
|
Nirmalya Ghosh1,
Stephen Ashwal1, Andre Obenaus2
1Department of Pediatrics, Loma Linda
University, Loma Linda, CA, USA; 2Department
of Radiology and Radiation Medicine, Loma Linda
University, Loma Linda, CA, USA |
|
|
Manual quantification
methods have hindered objective and rapid MRI
analysis of neonatal hypoxic brain injury and its
complex interactions with implanted therapeutic
neuronal stem cells (NSCs). To extract such
information to understand stem cell therapeutic
activity, we need an automated MRI analysis method.
Hierarchical Region Splitting (HRS) detects and
quantifies lesion and NSCs and their internal
compositions over time and space to quantify
individual characteristics and interactions more
objectively. |
|
|
|
11:18 |
653. |
Robust Myelin Water
Quantification Using Spatially Regularized
Nonnegative Least Square Algorithm |
|
|
Dosik Hwang1,
Yiping P. Du2
1Electrical and Electronic Engineering, Yonsei
University, Seoul, Korea; 2Psychiatry,
University of Colorado Denver, Denver, CO, USA |
|
|
A spatially regularized
nonnegative least square algorithm was developed for
robust myelin water quantification in the brain. The
regularization of the conventional nonnegative least
square (NNLS) algorithm has been expanded into the
spatial domain in addition to the spectral domain. A
substantial decrease in the myelin water fraction (MWF)
variability was observed in both simulation results
and the analysis of experimental data. In contrast
to other filtering approaches that reduced the noise
with a penalty of reduced spatial resolution, this
new algorithm effectively preserved details of
myelin distribution with substantial noise
reduction. The visibility of small focal lesions was
greatly improved. |
|
|
|
11:30 |
654. |
Three-Dimensional Segmentation
and Visualization of Cerebral Arteries and Veins
from Simultaneously Acquired MRA and MRV Based on
Graph-Cuts and Vessel Enhancement Filter |
|
|
Hackjoon Shim1,2,
Sung-Hong Park1,3, Kyongtae Ty Bae1,3
1Department of Radiology, University of
Pittsburgh, Pittsburgh, PA, USA; 2School
of Electrical Engineering, Seoul National
University, Seoul, Korea; 3Department of
Bioengineering, University of Pittsburgh,
Pittsburgh, PA, USA |
|
|
In this study, we
proposed a new 3D segmentation method to segment the
arteries and veins from MRA and MRV in the brain,
respectively, using a graph-cuts technique and a
vessel enhancement filter and then displayed the
arteries and veins together in 3D. The MRA and MRV
were acquired simultaneously using newly-introduced
compatible dual-echo arteriovenography (CODEA). Our
proposed method is promising when there is a need to
study the morphology of both arteries and veins
together in the brain, for example, the
characterization and quantification of arteriovenous
malformation and brain tumor vascularity. |
|
|
|
11:42 |
655. |
Fuzzy Clustering-Based Segmentation of
Manganese-Enhanced Neuronal Network Areas on MR
Images |
|
|
Mark J.R.J. Bouts1,
Jet P. van der Zijden1, Wim M. Otte1,
Rick M. Dijkhuizen1
1Image
Sciences Institute, University Medical Center
Utrecht, Utrecht, Netherlands |
|
|
Regional contrast
enhancement on manganese-enhanced MR images is
commonly measured using ROI analysis. However such
methods are subject to user bias. We evaluated four
fuzzy clustering methods to unbiasedly depict
prominent areas of manganese enhancement in a
neuronal tracing study in rat brain. A conventional
Fuzzy C-Means approach was tested against three
spatial contiguity constrained approaches. Spatial
contiguity was defined either by a Markov random
field (MRF) or neighborhood homogeneity weighting.
The third method combined the latter approaches. Our
study demonstrated highest accuracy and best overlap
with manual outlines for a combination of MRF with
neighborhood homogeneity weighting. |
|
|
|
11:54 |
656. |
Reduction of Motion Artefacts
in Renal Perfusion DCE-MRI Data |
|
|
Gernot Reishofer1,
Robert Merwa2, Manuela Aschauer3,
Sabine Zitta4, Rudolf Stollberger2,
Franz Ebner5
1Department of Radiology / MR-Physics, Medical
University of Graz, Graz, Austria; 2Institute
of Medical Engineering, Graz University of
Technology , Graz, Austria; 3Department
of Radiology, Medical University of Graz, Graz,
Austria; 4Department of Internal Medicine
/ Division of Nephrology, Medical University of
Graz, Graz, Austria; 5Department of
Radiology / Division of Neuroradiology, Medical
University of Graz, Graz, Austria |
|
|
An exact analysis of
renal perfusion parameter in different tissues
requires a sufficient image registration of the
dynamic scan. Most registration algorithms, based on
the conservation of pixel intensity values fail due
to fast intensity changes caused by contrast media
uptake. We circumvent this pitfall by evaluating a
second dynamic time series using a filter operation
in the time domain. The images of this time series
are used as templates for a non rigid registration
algorithm. We demonstrate that our algorithm
significantly reduces kidney movement and allows a
more differentiated analysis of several kidney
tissue types. |
|
|
|
12:06 |
657. |
Selection of Diagnostic
Features to Differentiate Between Malignant and
Benign Lesions That Presented as Mass Lesions and
Non-Mass Type Enhancement on Breast MRI |
|
|
Ke Nie1,
Dustin Newell1, Jeon-Hor Chen1,2,
Chieh-Chih Hsu2, Hon J. Yu1,
Orhan Nalcioglu1, Min-Ying Lydia Su1
1Tu & Yuen Center for Functional Onco-Imaging,
University of California, Irvine, CA, USA; 2Department
of Radiology, China Medical University, Taiwan |
|
|
Diagnostic features to
differentiate between malignant and benign lesions
presenting as mass and non-mass types were
investigated using 116 lesions. The morphology of
lesion (shape/margin and enhancement texture) and
the enhancement kinetic parameters were obtained,
and then a best feature set was selected by
artificial neural network for making differential
diagnosis. Morphology parameters can diagnose mass
type benign and malignant lesions with a high
accuracy (AUC=0.87), and adding Ktrans will further
improve to 0.90. On the other hand, neither the
morphology nor the kinetic parameters analyzed from
outlined lesion ROI for non-mass lesions could
differentiate between malignant and benign lesions. |
|
|
|
12:18 |
658. |
Quantifying the Vascular
Profile of a Tumor by 3D Euclidean Distance Maps |
|
|
Meiyappan Solaiyappan1,
Deepak Dinakaran2, Yoshinori Kato1,
Dmitri Artemov1
1Department of Radiology, Johns Hopkins
Medical Institutions, Baltimore, MD, USA; 2Department
of Biological Sciences, University of Toronto,
Toronto, Ontario, Canada |
|
|
A practical
quantification method for the analysis of the
vascular data will benefit MRA studies that focus on
tumor vasculature for the purpose of understanding
the effects of treatment. We show that the
distribution of 3D Euclidean distance from each
voxel within the vascularized regions of the tumor
to their nearest vessel boundary can provide a
useful quantification measure to monitor the changes
in the tumor vasculature following treatment. Such
an approach represents an objective way of
monitoring the changes in the vasculature based on
the changes in the distribution of the vascularized
region with respect to the vessel branches. |
|
|
|
|