Accurate identification of polyps is the ultimate goal of Computed Tomography Colonography (CTC). While oral contrast agents were originally used to tag stool and fluid for the ultimate goal of CTC, recently their effect on coating the surface of polyps has been observed. This study aims to evaluate (1) the frequency at which the oral contrast adhered to polyp surfaces and (2) if there was a difference in contrast adherence with respect to diverse polyp types. To eliminate gravity as a factor in this study, the polyps in contact with tagged fluid pools, particularly on the bottom of the colon wall were excluded. A total of 150 polyps were selected under the above condition from a CTC database and screened for any adherent contrast on the luminal edge. Among the total, 53% of the screened polyps had adherent contrast. Serrated adenomas and hyperplastic polyps had a higher tagging percentage, 77.80% and 62.50% respectively, than tubular adenomas and tubulovillous adenomas, 44.40% and 43% respectively. Other factors that were analyzed for the effect on coating include size and location of the polyps. The higher tagging percentage of serrated adenomas and hyperplastic polyps may be due to their similar cellular features. The average size of the polyps was 8.9 mm. When the polyps were separated by size into small (5-9mm) and large (10-26mm) groups, the large group had a higher tagging percentage. The polyp types were also classified by location with the major findings being: 1) Tubular adenomas were present in all segments of the colon and 2) that serrated adenomas were present at a higher percentage in the proximal colon. These findings shall facilitate characterizing tagging agents and improve computer aided detection and classification of polyps via CTC.
Human colon has complex structures mostly because of the haustral folds. Haustral folds are thin flat protrusions on the
colon wall, which inherently attached on the colon wall. These structures may complicate the shape analysis for
computer-aided detection of colonic polyps (CADpolyp); however, they can serve as solid reference during image
interpretation in computed tomographic colonography (CTC). Therefore, in this study, based on a clear model of the
haustral fold boundaries, we employ level set method to automatically segment the fold surfaces. We believe the
segmented folds have the potential to significantly benefit various post-procedures in CTC, e.g., supine-prone
registration, synchronized image interpretation, automatic polyp matching, CADpolyp, teniae coli extraction, etc. For
the first time, with assistance from physician experts, we established the ground truth of haustral fold boundaries of 15
real patient data from two medical centers, based on which we evaluated our algorithm. The results demonstrated that
about 92.7% of the folds are successfully detected. Furthermore, we explored the segmented area ratio (SAR), i.e., the
ratio between the areas of the intersection and the union of the expert-drawn and the automatically-segmented folds, to
measure the accuracy of the segmentation algorithm. The averaged result of SAR=86.2% shows a good match between
the ground truth and our segmentation results.
Orally administered tagging agents are usually used in CT colonography (CTC) to differentiate residual bowel content
from native colonic structure. However, the high-density contrast agents tend to introduce the scatter effect on
neighboring soft tissues and elevate their observed CT attenuation values toward that of the tagged materials (TMs),
which may result in an excessive electronic colon cleansing (ECC) where pseudo-enhanced soft tissues are incorrectly
identified as TMs. To address this issue, we integrated a scale-based scatter correction as a preprocessing procedure into
our previous ECC pipeline based on the maximum a posteriori expectation-maximization (MAP-EM) partial volume
segmentation. The newly proposed ECC scheme takes into account both scatter effect and partial volume effect that
commonly appear in CTC images. We evaluated the new method with 10 patient CTC studies and found improved
performance. Our results suggest that the proposed strategy is effective with potentially significant benefits for both
clinical CTC examinations and automatic computer-aided detection (CAD) of colon polyps.
Computed tomography (CT)-based virtual colonoscopy or CT colonography (CTC) currently utilizes oral contrast
solutions to tag the colonic fluid and possibly residual stool for differentiation from the colon wall and polyps. The
enhanced image density of the tagged colonic materials causes a significant partial volume (PV) effect into the colon
wall as well as the lumen space (filled with air or CO2). The PV effect on the colon wall can "bury" polyps of size as
large as 5mm by increasing their image densities to a noticeable level, resulting in false negatives. It can also create
false positives when PV effect goes into the lumen space. We have been modeling the PV effect for mixture-based
image segmentation and developing text-based computer-aided detection of polyp (CADpolyp) by utilizing the PV
mixture-based image segmentation. This work presents some preliminary results of developing and applying texture-based
CADpolyp technique to low-dose CTC studies. A total of 114 studies of asymptomatic patients older than 50,
who underwent CTC and then optical colonoscopy (OC) on the same day, were selected from a database, which was
accumulated in the past decade and contains various bowel preparations and CT scanning protocols. The participating
radiologists found ten polyps of greater than 5 mm from a total of 16 OC proved polyps, i.e., a detection sensitivity of
63%. They scored 23 false positives from the database, i.e., a 20% false positive rate. Approximately 70% of the
datasets were marked as imperfect bowel cleansing and/or presence of image artifacts. The impact of imperfect bowel
cleansing and image artifacts on VC performance is significant. The texture-based CADpolyp detected all the polyps
with an average of 2.68 false positives per patient. This indicates that texture-based CADpolyp can improve the CTC
performance in the cases of imperfect cleansed bowels and presence of image artifacts.
Objective: To investigate a less stressful bowel preparation for polyp screening by virtual colonoscopy (VC) with follow-up biopsy on the positive findings by optical colonoscopy (OC). Materials and Methods: Fifty-eight volunteers of age older than 40 -- receiving low-residue diet and laxatives of magnesium citrate, bisacodyl tablets and suppository -- were divided into three groups. In Group I, 16 volunteers took three 40cc oral doses of MD-Gastroview with the three meals respectively, the day prior to VC procedure. In Group II, 18 volunteers ingested barium sulfate suspension (2% w/v, 250 cc/dose) at bedtime and in the next day morning of VC. In Group III, 24 volunteers received 60 cc of MD-Gastroview at bedtime and in the next day morning of VC. Following colon inflation with CO2, computer tomography (CT) abdominal images were acquired by a standard single-slice detector-band VC protocol, i.e., 5 mm collimation, 1 mm reconstruction, 1.5-2.0:1.0 pitch, 120 kVp and 100-150 mA. The CT density of the tagged residual fluid was measured. An image segmentation algorithm was applied to remove electronically the residue fluid. Results: The average fluid density was 97 HU for Group I, 221 HU for Group II2, and 599 HU for Group III. These three groups’ density means are significantly different (p < 0.001 one-way ANOVA). After the electronic cleansing, the % of cleansed fluid regions was 5.5%, 16.5% and 93.1% (p<0.0001 Chi square) for these groups respectively. Conclusion: A less-stressful bowel preparation with low residue diet and MD-Gastroview oral contrast is feasible for VC screening with follow-up biopsy on the positive findings by OC.
Objective: To investigate the feasibility of laxative-free bowel preparation to relieve the patient stress in colon cleansing for virtual colonoscopy. Materials and Methods: Three different bowel-preparation protocols were investigated by 60 study cases from 35 healthy male volunteers. All the protocols utilize low-residue diet for two days and differ in diet for the third day - the day just prior to image acquisition in the fourth day morning. Protocol Diet-1 utilizes fluid or liquid diet in the third day, Diet-2 utilizes a food kit, and Diet-3 remains the low-residue diet. Oral contrast of barium sulfate (2.1%, 250 ml) was added respectively to the dinner in the second day and the three meals in the third day. Two doses of MD-Gastroview (60 ml) were ingested each in the evening of the third day and in the morning before image acquisition. Images were acquired by a single-slice detector spiral CT (computed tomography) scanner with 5 mm collimation, 1 mm reconstruction, 1.5-2.0:1.0 pitch, 100-150 mA, and 120 kVp after the colons were inflated by CO2. The contrasted colonic residue materials were electronically removed from the CT images by specialized computer-segmentation algorithms. Results: By assumptions that the healthy young volunteers have no polyp and the image resolution is approximately 4 mm, a successful electronic cleansing is defined as “no more than five false positives and no removal of a colon fold part greater than 4 mm” for each study case. The successful rate is 100% for protocol Diet-1, 77% for Diet-2 and 57% for Diet-3. Conclusion: A laxative-free bowel preparation is feasible for virtual colonoscopy.
In this paper, we propose a new technique to utilize both the morphological and the texture information of the colon
wall for detection of colonic polyps. Firstly this method can quickly identify suspicious patches of the colon wall by
employing special local and global geometrical information, different from other methods of utilizing local geometry
only. By our edge-detection technology, the growing region of suspected polyps is identified and its internal textures
are quantitatively analyzed based on an assumed ellipsoid polyp model. Both the extracted texture and morphological
information are then applied to eliminate the false positives from the identified suspicious patches. With all the
extracted geometrical, morphological and texture features, this presented computer-aided detection method have
demonstrated significant improvement in detection of the colonic polyps for virtual colonoscopy.
We propose a new partial volume (PV) segmentation scheme to extract bladder wall for computer aided detection (CAD) of bladder lesions using multispectral MR images. Compared with CT images, MR images provide not only a better tissue contrast between bladder wall and bladder lumen, but also the multispectral information. As multispectral images are spatially registered over three-dimensional space, information extracted from them is more valuable than that extracted from each image individually. Furthermore, the intrinsic T1 and T2 contrast of the urine against the bladder wall eliminates the invasive air insufflation procedure. Because the earliest stages of bladder lesion growth tend to develop gradually and migrate slowly from the mucosa into the bladder wall, our proposed PV algorithm quantifies images as percentages of tissues inside each voxel. It preserves both morphology and texture information and provides tissue growth tendency in addition to the anatomical structure. Our CAD system utilizes a multi-scan protocol on dual (full and empty of urine) states of the bladder to extract both geometrical and texture information. Moreover, multi-scan of transverse and coronal MR images eliminates motion artifacts. Experimental results indicate that the presented scheme is feasible towards mass screening and lesion detection for virtual cystoscopy (VC).
In this paper, we propose a new computer aided detection (CAD) technique to utilize both global and local shape information of the colon wall for detection of colonic polyps. Firstly, the whole colon wall is extracted by our mixture-based image segmentation method. This method uses partial volume percentages to represent the distribution of different materials in each voxel, so it provides the most accurate information on the colon wall, especially the mucosa layer. Local geometrical measure of the colon mucosa layer is defined by the curvature and gradient information extracted from the segmented colon-wall mixture data. Global shape information is provided by applying an improved linear integral convolution operation to the mixture data. The CAD technique was tested on twenty patient datasets. The local geometrical measure extracted from the mixture segmentation represents more accurately the polyp variation than that extracted from conventional label classification, leading to improved detection. The added global shape information further improves the polyp detection.
Powerful communication methods based upon multi-media and network technology are becoming available at reasonable cost. At the same time there is an increased awareness of the need to control costs and improve use of scarce resources by effective use of technology. Combining powerful communication methods with teleradiology will enhance its effectiveness and may aid in reduction of costs.
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