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SPIE:
Lihong Li, Dongqing Chen,
Sarang Lakare, Kevin Kreeger, Ingmar Bitter, Arie Kaufman,
Mark Wax, Petar Djuric, and Zhengrong Liang
Submit to:
MI03
Chairs: Dr. Anne
V. Clough and Dr. Chin-Tu Chen
Title of Conference: Physiology
and Function from Multidimensional Images
Summary:
Purpose: Virtual
colonoscopy provides a safe, minimal-invasive approach
to detect colonic polyps using medical imaging and computer
graphics technologies. Residual stool and fluid are
problematic for optimal viewing of the colonic mucosa.
Electronic cleansing techniques combining both bowel
preparation and image segmentation were developed to
extract colon lumen from the abdominal computed tomographic
(CT) images. In this paper, we present a new electronic
colon cleansing technology, which employs a hidden Markov
random filed (MRF) model to integrate the neighborhood
information for overcoming the non-uniformity problems
in the tagged stool/fluid region.
Methods:
Prior to obtaining CT images for virtual colonoscopy,
the patient undergoes bowel preparation of mild laxatives
and a low residue diet. This also includes four 250cc
doses of 2.1% w/v barium sulfate suspension taken with
meals and two doses of 60 cc of gastroview (diatrizoate
meglumine and diatrizoate sodium solution) taken the
night prior to and the morning of the procedure. After
the patient's colon was inflated with CO2 (1 -2 L) given
through a small bore rectal tube, CT images are obtained
in a less than 40 second breath-hold sequence using
standard virtual colonoscopy parameters of 120kVp, 100
- 120 mA and 34 -40 field of view. The acquired data
was reconstructed at 1 mm intervals with a 512 x 512
array size resulting in 300 - 450 images which were
reconstructed into a 3D data set.
A statistical method for maximum a posterior
probability (MAP) was developed to identify the enhanced
regions of residual stool/fluid from the CT images.
Unlike other non-MRF approaches, which have an intrinsic
limitation on taking into account the spatial information,
our method utilizes a hidden MRF Gibbs model to integrate
the spatial information into the well-established Expectation
Maximization (EM) model-fitting algorithm. Given the
image data set, our method estimates the model parameters
and segments the voxels iteratively in an interleaved
manner, converging to a solution where the model parameters
and voxel labels are stabilized within a specified criterion.
Results:
The new electronic cleansing method has been
evaluated by volunteer and patient datasets. Figure
1 demonstrates the segmentation results on the tagged
region with the standard EM algorithm (left) and the
integration of hidden MRF-EM algorithm (right). Clearly,
the results of the hidden MRF-EM algorithm are much
better than those of the standard EM algorithm. Figure
2 demonstrates an overview of the entire extracted colon
lumen. Figure 3 is the internal colon view rendered
by our virtual colonoscopy system. In addition, the
computational performance of the algorithm was also
tested. It took approximately 5 minutes for a 400 image
of 512X512 array size to classify the tagged region
on an AMD 900 MHz-based PC system
New
or breakthrough work: In this new approach, the
algorithm models the tagged materials and colon object
by an isotropic Markov random field. Unlike the non-MRF
approaches, our MRF-based spatial information has been
integrated to the EM algorithm to estimate model parameters
and segmenting voxels simultaneously. The use of a hidden
MRF model overcomes the non-uniformity problems in the
tagged fluid/stool regions, which are the major obstacle
for virtual colonoscopy without the conventional physical
colon cleansing technique. By eliminating or minimizing
this bowel preparation with stool/fluid tagging and
image segmentation, virtual colonoscopy will be more
accepted by patients.
Conclusion:
We present an electronic colon cleansing technology
based on a hidden MRF model and MAP-EM algorithm for
extracting colon lumen from the abdominal CT images.
We have tested this algorithm on a user-friendly virtual
colonoscopy system which utilizes navigator through
the colon. The results indicate that the algorithm is
feasible for virtual colonoscopy to detect colonic polyps.
Keywords:
virtual colonoscopy, virtual endoscopy, volumetric
segmentation, Markov random field, electronic colon
cleansing, massive polyp screening, early cancer detection,
interactive visualization.
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