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Abraham H Dachman

Publications and source records attributed to Abraham H Dachman.

At least 19 recordsLinked to original sources
PubMed2006

Comparison of routine and unprepped CT colonography augmented by low fiber diet and stool tagging: a pilot study.

BACKGROUND: We performed a pilot study examining the feasibility of a new unprepped CT colonography (CTC) strategy: low fiber diet and tagging (unprepped) vs. low fiber diet, tagging and a magnesium citrate cleansing preparation (prepped). Prior reports of tagging were limited in that the residual stool was neither measured and stratified by size nor did prior reports subjectively evaluate the ease of interpretation by a reader experienced in interpreting CTC examinations. METHODS: Prospective randomized to unprepped n = 14 and prepped n = 14. Colonic segments were subjectively evaluated for residual stool that would potentially interfere with interpretation. Scores were given in the following categories: percentage of residual stool that was touching or nearly touching mucosa, the largest piece of retained stool, effectiveness of tagging, height of residual fluid, degree of distention, ease of interpretation, and reading time. RESULTS: Ease of the CT read (scale where 4 = optimal read) averaged 1.3 for the unprepped group and 2.3 for the prepped group. The mean read time averaged 17.5 min for unprepped and 17.9 min for prepped. The degree of distention (scale where 4 = well distended) averaged 3.7 for unprepped and 3.6 for prepped. Supine and prone images combined, the unprepped group had 160 segments with stool; prepped group had 58 segments. The amount of stool covering the mucosa in all segments averaged 1.6 (33%-66% coverage) in the unprepped group and 0.35 (<33% mucosal coverage) in the prepped group. The mean size of the largest piece of stool was 33.67 mm for unprepped and 4.01 mm for prepped. Percentage of tagged stool was not significantly different between the groups (range of 94-98%). The height of residual fluid averaged 8.37 mm for unprepped and 13.4 mm for prepped. Three polyps in three patients were found during optical colonoscopy (OC) in the unprepped group (5, 6, and 10 mm), none of which were prospectively detected at CTC. Three polyps in three patients were detected during OC in the prepped group (5, 10, and 15 mm), two of which were prospectively detected at CTC. Two false-positive lesions were observed at CTC in one patient in the prepped group. CONCLUSION: There was more stool in the unprepped group and while this factor did not slow down the reading time, it made the examination subjectively harder to interpret and likely caused the three polyps in this group to be missed. We conclude that a truly unprepped strategy that leaves significant residual stool, even if well tagged, is not desirable.

Adult↗
PubMed2006

Pericardial varices secondary to portal hypertension demonstrated by CT.

Pericardial varices are rarely seen on CT and can be misinterpreted as mediastinal masses or lymph nodes. We report a case of a 38-year-old man who presented for routine liver transplant evaluation. On high-resolution computed tomography (CT), a cluster of varices was seen in the left cardiophrenic angle.

Adult↗
PubMed2006

Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes.

One of the limitations of the current computer-aided detection (CAD) of polyps in CT colonography (CTC) is a relatively large number of false-positive (FP) detections. Rectal tubes (RTs) are one of the typical sources of FPs because a portion of a RT, especially a portion of a bulbous tip, often exhibits a cap-like shape that closely mimics the appearance of a small polyp. Radiologists can easily recognize and dismiss RT-induced FPs; thus, they may lose their confidence in CAD as an effective tool if the CAD scheme generates such "obvious" FPs due to RTs consistently. In addition, RT-induced FPs may distract radiologists from less common true positives in the rectum. Therefore, removal RT-induced FPs as well as other types of FPs is desirable while maintaining a high sensitivity in the detection of polyps. We developed a three-dimensional (3D) massive-training artificial neural network (MTANN) for distinction between polyps and RTs in 3D CTC volumetric data. The 3D MTANN is a supervised volume-processing technique which is trained with input CTC volumes and the corresponding "teaching" volumes. The teaching volume for a polyp contains a 3D Gaussian distribution, and that for a RT contains zeros for enhancement of polyps and suppression of RTs, respectively. For distinction between polyps and nonpolyps including RTs, a 3D scoring method based on a 3D Gaussian weighting function is applied to the output of the trained 3D MTANN. Our database consisted of CTC examinations of 73 patients, scanned in both supine and prone positions (146 CTC data sets in total), with optical colonoscopy as a reference standard for the presence of polyps. Fifteen patients had 28 polyps, 15 of which were 5-9 mm and 13 were 10-25 mm in size. These CTC cases were subjected to our previously reported CAD scheme that included centerline-based segmentation of the colon, shape-based detection of polyps, and reduction of FPs by use of a Bayesian neural network based on geometric and texture features. Application of this CAD scheme yielded 96.4% (27/28) by-polyp sensitivity with 3.1 (224/73) FPs per patient, among which 20 FPs were caused by RTs. To eliminate the FPs due to RTs and possibly other normal structures, we trained a 3D MTANN with ten representative polyps and ten RTs, and applied the trained 3D MTANN to the above CAD true- and false-positive detections. In the output volumes of the 3D MTANN, polyps were represented by distributions of bright voxels, whereas RTs and other normal structures partly similar to RTs appeared as darker voxels, indicating the ability of the 3D MTANN to suppress RTs as well as other normal structures effectively. Application of the 3D MTANN to the CAD detections showed that the 3D MTANN eliminated all RT-induced 20 FPs, as well as 53 FPs due to other causes, without removal of any true positives. Overall, the 3D MTANN was able to reduce the FP rate of the CAD scheme from 3.1 to 2.1 FPs per patient (33% reduction), while the original by-polyp sensitivity of 96.4% was maintained.

Algorithms↗
PubMed2006

Cecal mobility: a potential pitfall of CT colonography.

OBJECTIVE: On CT colonography, feces is differentiated from a polyp primarily by texture and mobility. When feces is soft-tissue in density and polypoid in shape, only its mobility is the clue to the correct diagnosis. There are reports of false-negative examinations caused by the mobility of bowel mimicking lesion movement. We studied the mobility of the cecum as seen on CT colonography to determine how often this potential pitfall exists. CONCLUSION: Rotation of the cecum is geometrically complex and occurs in several planes. It explains previous anecdotal reports of false-negative diagnoses. When solid feces is suspected in the cecum based on mobility, the reviewer should take the time to carefully analyze mobility of the cecum using multiplanar images.

Adult↗
PubMed2004

Computer-aided diagnosis for CT colonography.

CT colonography, or virtual colonoscopy, is a promising alternative screening tool for colon cancer. Computer-aided diagnosis (CAD) for CT colonography has the potential to increase radiologists' diagnostic performance in the detection of polyps and to reduce variability of the diagnostic accuracy among readers. Technical developments have advanced CAD for CT colonography substantially during the last several years. This paper describes the key techniques used for CAD for detection of polyps and masses in CT colonography, the current detection performance, and challenges and the future of CAD.

Colonic Polyps↗
PubMed2004

Computerized detection of colorectal masses in CT colonography based on fuzzy merging and wall-thickening analysis.

In recent years, several computer-aided detection (CAD) schemes have been developed for the detection of polyps in CT colonography (CTC). However, few studies have addressed the problem of computerized detection of colorectal masses in CTC. This is mostly because masses are considered to be well visualized by a radiologist because of their size and invasiveness. Nevertheless, the automated detection of masses would naturally complement the automated detection of polyps in CTC and would produce a more comprehensive computer aid to radiologists. Therefore, in this study, we identified some of the problems involved with the computerized detection of masses, and we developed a scheme for the computerized detection of masses that can be integrated into a CAD scheme for the detection of polyps. The performance of the mass detection scheme was evaluated by application to clinical CTC data sets. CTC was performed on 82 patients with helical CT scanners and reconstruction intervals of 1.0-5.0 mm in the supine and prone positions. Fourteen patients (17%) had a total of 14 masses of 30-50 mm, and sixteen patients (20%) had a total of 30 polyps 5-25 mm in diameter. Four patients had both polyps and masses. Fifty-six of the patients (68%) were normal. The CTC data were interpolated linearly to yield isotropic data sets, and the colon was extracted by use of a knowledge-guided segmentation technique. Two methods, fuzzy merging and wall-thickening analysis, were developed for the detection of masses. The fuzzy merging method detected masses with a significant intraluminal component by separating the initial CAD detections of locally cap-like shapes within the colonic wall into mass candidates and polyp candidates. The wall-thickening analysis detected nonintraluminal masses by searching the colonic wall for abnormal thickening. The final regions of the mass candidates were extracted by use of a level set method based on a fast marching algorithm. False-positive (FP) detections were reduced by a quadratic discriminant classifier. The performance of the scheme was evaluated by use of a leave-one-out (round-robin) method with by-patient elimination. All but one of the 14 masses, which was partially cut off from the CTC data set in both supine and prone positions, were detected. The fuzzy merging method detected 11 of the masses, and the wall-thickening analysis detected 3 of the masses including all nonintraluminal masses. In combination, the two methods detected 13 of the 14 masses with 0.21 FPs per patient on average based on the leave-one-out evaluation. Most FPs were generated by extrinsic compression of the colonic wall that would be recognized easily and quickly by a radiologist. The mass detection methods did not affect the result of the polyp detection. The results indicate that the scheme is potentially useful in providing a high-performance CAD scheme for the detection of colorectal neoplasms in CTC.

Algorithms↗
PubMed2004

The effect of reconstruction algorithm on conspicuity of polyps in CT colonography.

OBJECTIVE: CT colonography studies to date have used a standard CT algorithm. To determine whether nonstandard algorithms may result in better performance of CT colonography, we conducted a prospective, blinded-observer study of the effect of the reconstruction algorithm on the conspicuity of colonic polyps and folds. SUBJECTS AND METHODS: CT colonography of patients with proven polyps, masses, or polypoid folds was performed on an MDCT scanner, and the images were reconstructed using the standard, soft, lung, and detail algorithms. Two experiments were performed. The first used four patient data sets of a short segment of colon (30-60 images), each reconstructed using all four algorithms and then viewed on a workstation in a four-on-one format that allowed all four reconstructions to be viewed simultaneously. The second used six sets of cut-film images (four or eight images each); images within each set differed only in the reconstruction algorithm used to generate them (eight-image sets were prepared with two different level settings). Twenty-one reviewers with varying levels of experience who were unaware of the purpose of the study were asked to rank the images within each set according to their value in the detection of either polyps or masses. RESULTS: Reviewers showed statistically significant differences in preference for the four algorithms (p = 0.037 in the computer-based experiment; for the cut-film experiment, p = 0.029 for the four-image sets and p = 0.041 for the eight-image sets). In the computer-based experiment, reviewers preferred the detail algorithm to the standard algorithm with an estimated probability of 0.67 (95% confidence interval [CI], 0.57-0.75) and the soft algorithm over the standard algorithm with an estimated probability of 0.59 (95% CI, 0.51-0.66). However, reviewers with the most experience (having interpreted at least 250 cases) preferred the soft algorithm over the standard algorithm by the same two-to-one margin as observed for the detail algorithm. In contrast, the standard and detail algorithms were ranked similarly in the cut-film experiment, with the soft and lung algorithms ranked worst. CONCLUSION: To our knowledge, ours is the first observer study on the effect of the reconstruction algorithm on conspicuity of folds and polyps in CT colonography. Our results indicate significant differences in the reconstruction algorithms, with the soft and detail algorithms being preferred over the standard algorithm by experienced reviewers when interpreting images on a workstation. These results indicate the need for further research into the effect of reconstruction algorithms on CT colonography.

Algorithms↗
PubMed2003

Computerized tomographic colonography: performance evaluation in a retrospective multicenter setting.

BACKGROUND & AIMS: No multicenter study has been reported evaluating the performance and interobserver variability of computerized tomographic colonography. The aim of this study was to assess the accuracy of computerized tomographic colonography for detecting clinically important colorectal neoplasia (polyps >or=10 mm in diameter) in a multi-institutional study. METHODS: A retrospective study was developed from 341 patients who had computerized tomographic colonography and colonoscopy among 8 medical centers. Colonoscopy and pathology reports provided the standard. A random sample of 117 patients, stratified by criterion standard, was requested. Ninety-three patients were included (47% with polyps >or=10 mm; mean age, 62 years; 56% men; 84% white; 40% reported colorectal symptoms; 74% at increased risk for colorectal cancer). Eighteen radiologists blinded to the criterion standard interpreted computerized tomography colonography examinations, each using 2 of 3 different software display platforms. RESULTS: The average area under the receiver operating characteristic curve for identifying patients with at least 1 lesion >or=10 mm was 0.80 (95% lower confidence bound, 0.74). The average sensitivity and specificity were 75% (95% lower confidence bound, 68%) and 73% (95% lower confidence bound, 66%), respectively. Per-polyp sensitivity was 75%. A trend was observed for better performance with more observer experience. There was no difference in performance across software display platforms. CONCLUSIONS: Computerized tomographic colonography performance compared favorably with reported performance of fecal occult blood testing, flexible sigmoidoscopy, and barium enema. A prospective study evaluating the performance of computerized tomography colonography in a screening population is indicated.

Colonic Neoplasms↗
PubMed2003

Virtual colonoscopy: past, present, and future.

Virtual colonoscopy is developing into a practical clinical technique. The issues of the steep learning curve and accuracy of the technique are undergoing advances related to patient preparation, scanning technique, reading methods, and CAD. It is probably the best test for patients with an incomplete colonoscopy or for those patients who cannot undergo colonoscopy. Its precise role in screening average-risk patients for colon cancer remains to be defined by ongoing research and clinical trials.

Colonography, Computed Tomographic↗
PubMed2002

Prospective evaluation of ureteral stent durability in patients with chronic ureteral obstruction.

OBJECTIVES: To prospectively evaluate the durability of ureteral stents in patients with chronic ureteral obstruction. METHODS: Twenty-nine renal units from 22 patients with known chronic ureteral obstruction treated with indwelling ureteral stents were studied at 3, 6, 9, and 12 months after initial stent placement (7 to 8F Percuflex) by abdominal x-ray, serum creatinine, and renal and bladder ultrasonography. Renal ultrasound scans were performed by a single senior radiologist with specific attention paid to any increase in hydronephrosis and to the presence of ureteral jets. The stent was changed when calcifications were seen along the stent on abdominal x-ray, if an increase in serum creatinine occurred, or if any increase in hydronephrosis or absence of ureteral jets was seen on ultrasonography. In addition, the stents were changed if new-onset flank pain and/or pyelonephritis developed. If none of the above occurred, the stents were changed after 12 months. RESULTS: Two patients died of metastatic cancer, leaving 25 renal units available for follow-up. The mean and median time to stent change were 6 months. At 3, 6, 9, and 12 months, the number of renal units that did not meet the criteria warranting stent change was 18 (72%), 12 (48%), 6 (24%), and 2 (8%), respectively. Only 2 (8%) of 25 renal units did not meet any exclusion criteria at 12 months. The most common reasons for requiring the stent to be changed were new-onset flank pain in 8 (35%), an increase in serum creatinine in 7 (30%), and new-onset or increased hydronephrosis in 7 (30%). No criteria were useful in predicting how long stents could be maintained in an individual patient. CONCLUSIONS: The stents were maintained for more than 6 months in 12 (48%) of 25 renal units. However, very few patients were able to maintain chronic ureteral stents for 12 months without requiring stent change based on our criteria.

Chronic Disease↗
PubMed2002

Virtual colonoscopy. Potential clinical applications of a new technique.

The potential role of virtual colonoscopy is not limited to colorectal cancer screening, although that is its greatest potential. Same-day VC should be offered for patients undergoing an incomplete colonoscopy. Conversely, faster, more accurate interpretation by the radiologist on the same day as a VC should lead to the consideration by the gastroenterologist of same-day colonoscopy for patients with a suspicious, significantly sized polyp.

Cohort Studies↗
PubMed2002

Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study.

PURPOSE: To develop a computer-aided diagnosis (CAD) scheme for automated detection of colonic polyps on the basis of volumetric features and to assess its accuracy on the basis of colonoscopy, the standard. MATERIALS AND METHODS: Computed tomographic (CT) colonography was performed in patients with use of standard bowel cleansing, air insufflation, and helical scanning in supine and prone positions. The colon was extracted from volumetric data sets generated from transverse CT sections. Volumetric features characterizing polyps were computed at each point in the extracted colon. Polyps were detected by means of hysteresis thresholding and fuzzy clustering followed by a rule-based test on the basis of feature values. Locations of the detected polyps were compared with those detected at conventional colonoscopy. RESULTS: Forty-one cases were analyzed: nine cases with polyps and 32 without polyps. Each case with polyps had one polyp of clinically important size (six were 5-9 mm; three, 10 mm). Thus, there were 82 volumetric data sets, 18 included polyps. Eighty-nine percent (16 of 18) of the polyps were detected. Each of the two false-negative findings was detected in the other position; thus, 100% of polyp cases were detected, with 2.5 false-positive findings per patient. The false-positive findings were similar to those due to common perceptual errors. Most of the false-positive findings were easily distinguishable from true polyps by experienced radiologists. CONCLUSION: The CAD scheme has the potential to depict polyps with high sensitivity and an acceptable false-positive rate.

Adult↗
PubMedDate not supplied

Automated knowledge-guided segmentation of colonic walls for computerized detection of polyps in CT colonography.

PURPOSE: We have developed a novel automated technique for segmenting colonic walls for the application of computer-aided polyp detection in CT colonography. In particular, the technique was designed to minimize the presence of extracolonic components, such as small bowel, in the segmented colon. METHODS: The segmentation technique combines an improved version of our previously reported anatomy-oriented colon segmentation technique with a colon-based analysis step that performs self-adjusting volume-growing within the colonic lumen. Extracolonic components are eliminated by intersecting of the resulting two segmentations, so that the colonic walls remain in the intersection. The technique was evaluated on 88 CT colonography datasets. The colon segmentations were evaluated subjectively by four radiologists, as well as objectively by performance of an automated polyp detection on the segmentation. For comparison, the tests were also performed for the anatomy-oriented colon segmentation technique. RESULTS: On average, the technique covered 98% of the visible colonic walls. Approximately 50% of the extracolonic components remaining in the anatomy-oriented segmentation were removed, but 10-15% of the segmentation still contained extracolonic components. The dataset-based false-positive rate of the automated polyp detection was improved by 10% without compromising the 100% case-based sensitivity, and the case-based false-positive rate was improved by 15% over the previous false-positive rate. CONCLUSIONS: The technique segments practically all of the colonic walls in the region of diagnostic quality with a large reduction in the amount of extracolonic components over our previously used technique. The new segmentation improves the specificity of our computer-aided polyp detection scheme significantly without any degradation in detection sensitivity.

Artificial Intelligence↗