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Biomedical subjects

Sandy Napel

Publications and source records attributed to Sandy Napel.

15 recordsLinked to original sources
PubMed2005

Alternative input devices for efficient navigation of large CT angiography data sets.

PURPOSE: To compare devices for the task of navigating through large computed tomographic (CT) data sets at a picture archiving and communication system workstation. MATERIALS AND METHODS: The institutional review board approved this study, and all subjects provided informed consent. Five radiologists were asked to find 25 different vascular targets in three CT angiography data sets (average number of sections, 1025) by using several devices (trackball, tablet, jog-shuttle wheel, and mouse). For each trial, the total time to acquire the targets (T1) was recorded. A secondary study in which 13 nonradiologists performed seven trials with an artificial target inserted at a random location in the same image data was also performed. For each trial, the following items were recorded: time until first target sighting (t2), time to manipulate the device after seeing the target, sections traversed during t2 (d1), time from first sight to target acquisition (t4), sections traversed during t4 (d2), and total trial time. Statistical analysis involved repeated-measures analysis of variance (ANOVA) and pairwise comparisons. RESULTS: Repeated-measures ANOVA revealed that the device used had a significant (P < .05) effect on T1. Pairwise comparisons revealed that the trackball was significantly slower than the tablet (P < .05) and marginally slower than the jog-shuttle wheel (P < .10). Further repeated-measures ANOVA for each secondary outcome measure revealed significant differences between devices for all outcome measures (P < .005). Pairwise comparisons revealed the trackball to be significantly slower than the other devices in all measures (P < .05). The trackball was significantly (P < .05) more accurate than the other devices for d1 and d2. CONCLUSION: The trackball may not be the optimal device for navigation of large CT angiography data sets; the use of other existing devices may improve the efficiency of interpretation of these sets.

Angiography↗
PubMed2004

Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection.

PURPOSE: To compare the performance of radiologists and of a computer-aided detection (CAD) algorithm for pulmonary nodule detection on thin-section thoracic computed tomographic (CT) scans. MATERIALS AND METHODS: The study was approved by the institutional review board. The requirement of informed consent was waived. Twenty outpatients (age range, 15-91 years; mean, 64 years) were examined with chest CT (multi-detector row scanner, four detector rows, 1.25-mm section thickness, and 0.6-mm interval) for pulmonary nodules. Three radiologists independently analyzed CT scans, recorded the locus of each nodule candidate, and assigned each a confidence score. A CAD algorithm with parameters chosen by using cross validation was applied to the 20 scans. The reference standard was established by two experienced thoracic radiologists in consensus, with blind review of all nodule candidates and free search for additional nodules at a dedicated workstation for three-dimensional image analysis. True-positive (TP) and false-positive (FP) results and confidence levels were used to generate free-response receiver operating characteristic (ROC) plots. Double-reading performance was determined on the basis of TP detections by either reader. RESULTS: The 20 scans showed 195 noncalcified nodules with a diameter of 3 mm or more (reference reading). Area under the alternative free-response ROC curve was 0.54, 0.48, 0.55, and 0.36 for CAD and readers 1-3, respectively. Differences between reader 3 and CAD and between readers 2 and 3 were significant (P < .05); those between CAD and readers 1 and 2 were not significant. Mean sensitivity for individual readings was 50% (range, 41%-60%); double reading resulted in increase to 63% (range, 56%-67%). With CAD used at a threshold allowing only three FP detections per CT scan, mean sensitivity was increased to 76% (range, 73%-78%). CAD complemented individual readers by detecting additional nodules more effectively than did a second reader; CAD-reader weighted kappa values were significantly lower than reader-reader weighted kappa values (Wilcoxon rank sum test, P < .05). CONCLUSION: With CAD used at a level allowing only three FP detections per CT scan, sensitivity was substantially higher than with conventional double reading.

Adolescent↗
PubMed2004

Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT.

We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.

Algorithms↗
PubMed2004

Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT.

The objective of this work was to develop and validate algorithms for detection and classification of hypodense hepatic lesions, specifically cysts, hemangiomas, and metastases from CT scans in the portal venous phase of enhancement. Fifty-six CT sections from 51 patients were used as representative of common hypodense liver lesions, including 22 simple cysts, 11 hemangiomas, 22 metastases, and 1 image containing both a cyst and a hemangioma. The detection algorithm uses intensity-based histogram methods to find central lesions, followed by liver contour refinement to identify peripheral lesions. The classification algorithm operates on the focal lesions identified during detection, and includes shape-based segmentation, edge pixel weighting, and lesion texture filtering. Support vector machines are then used to perform a pair-wise lesion classification. For the detection algorithm, 80% lesion sensitivity was achieved at approximately 0.3 false positives (FP) per slice for central lesions, and 0.5 FP per slice for peripheral lesions, giving a total of 0.8 FP per section. For 90% sensitivity, the total number of FP rises to about 2.2 per section. The pair-wise classification yielded good discrimination between cysts and metastases (at 95% sensitivity for detection of metastases, only about 5% of cysts are incorrectly classified as metastases), perfect discrimination between hemangiomas and cysts, and was least accurate in discriminating between hemangiomas and metastases (at 90% sensitivity for detection of hemangiomas, about 28% of metastases were incorrectly classified as hemangiomas). Initial implementations of our algorithms are promising for automating liver lesion detection and classification.

Algorithms↗
PubMed2004

Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: pilot study.

Computed tomography colonography (CTC) is a minimally invasive method that allows the evaluation of the colon wall from CT sections of the abdomen/pelvis. The primary goal of CTC is to detect colonic polyps, precursors to colorectal cancer. Because imperfect cleansing and distension can cause portions of the colon wall to be collapsed, covered with water, and/or covered with retained stool, patients are scanned in both prone and supine positions. We believe that both reading efficiency and computer aided detection (CAD) of CTC images can be improved by accurate registration of data from the supine and prone positions. We developed a two-stage approach that first registers the colonic central paths using a heuristic and automated algorithm and then matches polyps or polyp candidates (CAD hits) by a statistical approach. We evaluated the registration algorithm on 24 patient cases. After path registration, the mean misalignment distance between prone and supine identical anatomic landmarks was reduced from 47.08 to 12.66 mm, a 73% improvement. The polyp registration algorithm was specifically evaluated using eight patient cases for which radiologists identified polyps separately for both supine and prone data sets, and then manually registered corresponding pairs. The algorithm correctly matched 78% of these pairs without user input. The algorithm was also applied to the 30 highest-scoring CAD hits in the prone and supine scans and showed a success rate of 50% in automatically registering corresponding polyp pairs. Finally, we computed the average number of CAD hits that need to be manually compared in order to find the correct matches among the top 30 CAD hits. With polyp registration, the average number of comparisons was 1.78 per polyp, as opposed to 4.28 comparisons without polyp registration.

Algorithms↗
PubMed2003

Curved-slab maximum intensity projection: method and evaluation.

The authors developed and evaluated a method to produce curved-slab maximum intensity projections (MIPs) through blood vessels that semiautomatically excludes soft tissue and bone. Results obtained with the algorithm were compared with those obtained with rectangular-slab MIPs by using computed tomographic (CT) data from four patients with abdominal aortic aneurysms. Curved-slab MIPs exhibited increased mean vessel-to-perivascular tissue contrast of 55.1 HU (36%), allowed a 10% increase in contrast-to-noise ratio, and decreased apparent vessel narrowing by 0.12-1.09 mm, without increasing processing time. Curved-slab MIPs may also include multiple vessels in a single image, thereby improving interpretation efficiency by reducing the number of MIPs required in these patients from eight to three.

Aged↗
PubMed2003

CT colonography: does improved z resolution help computer-aided polyp detection?

Multislice helical CT offers several retrospective choices of longitudinal (z) resolution at a given detector collimation setting. We sought to determine the effect of z resolution on the performance of a computer-aided colonic polyp detector, since a human reader and a computer-aided polyp detector may have optimal performances at different z resolutions. We ran a computer-aided polyp detection algorithm on phantom data sets as well as data obtained from a single patient. All data were reconstructed at various slice thicknesses ranging from 1.25 to 10 mm. We studied the performance of the detector at various ranges of polyp sizes using free-response receiver-operating characteristic analyses. We also studied contrast-to-noise ratios (CNR) as a function of slice thickness and polyp size. For the phantom data, reducing the slice thickness from 5 to 1.25 mm improves sensitivity from 84.5% to 98.3% (all polyps), from 61.4% to 95.5% (polyps in the range [0, 5) mm) and from 97.7% to 100% (polyps in the range [5, 10) mm) at a false positive rate of 20 per data set. For polyps larger than 10 mm, there is no significant improvement in detection sensitivity when slice thickness is reduced. CNRs showed expected behavior with slice thickness and polyp size, but in all cases remained high (> 4). The results for the patient data followed similar patterns to that of the phantom case. Thus we conclude that for this detector, the optimal slice thickness is dependent upon the size of the smallest polyps to be detected. For detection of polyps 10 mm and larger, reconstruction of 5 mm sections may be sufficient. Further study is required to generalize these results to a broader population of patients scanned on different scanners.

Colon↗
PubMed2003

Semiautomated segmentation of blood vessels using ellipse-overlap criteria: method and comparison to manual editing.

Two-dimensional intensity-based methods for the segmentation of blood vessels from computed-tomography-angiography data often result in spurious segments that originate from other objects whose intensity distributions overlap with those of the vessels. When segmented images include spurious segments, additional methods are required to select segments that belong to the target vessels. We describe a method that allows experts to select vessel segments from sequences of segmented images with little effort. Our method uses ellipse-overlap criteria to differentiate between segments that belong to different objects and are separated in plane but are connected in the through-plane direction. To validate our method, we used it to extract vessel regions from volumes that were segmented via analysis of isolabel-contour maps, and showed that the difference between the results of our method and manually-edited results was within inter-expert variability. Although the total editing duration for our method, which included user-interaction and computer processing, exceeded that of manual editing, the extent of user interaction required for our method was about a fifth of that required for manual editing.

Aneurysm↗
PubMed2002

Edge displacement field-based classification for improved detection of polyps in CT colonography.

Colorectal cancer can easily be prevented provided that the precursors to tumors, small colonic polyps, are detected and removed. Currently, the only definitive examination of the colon is fiber-optic colonoscopy, which is invasive and expensive. Computed tomographic colonography (CTC) is potentially a less costly and less invasive alternative to FOC. It would be desirable to have computer-aided detection (CAD) algorithms to examine the large amount of data CTC provides. Most current CAD algorithms have high false positive rates at the required sensitivity levels. We developed and evaluated a postprocessing algorithm to decrease the false positive rate of such a CAD method without sacrificing sensitivity. Our method attempts to model the way a radiologist recognizes a polyp while scrolling a cross-sectional plane through three-dimensional computed tomography data by classification of the changes in the location of the edges in the two-dimensional plane. We performed a tenfold cross-validation study to assess its performance using sensitivity/specificity analysis on data from 48 patients. The mean specificity over all experiments increased from 0.19 (0.35) to 0.47 (0.56) for a sensitivity of 1.00 (0.95).

Adult↗
PubMed2002

Quantitative 3-d diagnostic ultrasound imaging using a modified transducer array and an automated image tracking technique.

An approach for acquiring dimensionally accurate three-dimensional (3-D) ultrasound data from multiple 2-D image planes is presented. This is based on the use of a modified linear-phased array comprising a central imaging array that acquires multiple, essentially parallel, 2-D slices as the transducer is translated over the tissue of interest. Small, perpendicularly oriented, tracking arrays are integrally mounted on each end of the imaging transducer. As the transducer is translated in an elevational direction with respect to the central imaging array, the images obtained by the tracking arrays remain largely coplanar. The motion between successive tracking images is determined using a minimum sum of absolute difference (MSAD) image matching technique with subpixel matching resolution. An initial phantom scanning-based test of a prototype 8 MHz array indicates that linear dimensional accuracy of 4.6% (2 sigma) is achievable. This result compares favorably with those obtained using an assumed average velocity [31.5% (2 sigma) accuracy] and using an approach based on measuring image-to-image decorrelation [8.4% (2 sigma) accuracy]. The prototype array and imaging system were also tested in a clinical environment, and early results suggest that the approach has the potential to enable a low cost, rapid, screening method for detecting carotid artery stenosis. The average time for performing a screening test for carotid stenosis was reduced from an average of 45 minutes using 2-D duplex Doppler to 12 minutes using the new 3-D scanning approach.

Carotid Arteries↗
PubMed2002

Carotid disease: automated analysis with cardiac-gated three-dimensional US technique and preliminary results.

Automatic analysis was performed of four-dimensional ultrasonographic (US) data in the carotid artery. The data, which were acquired in 31 subjects (eight healthy volunteers and 23 patients) by using a US scanner fitted with a special probe, were successfully processed. Acquisition time averaged 12 minutes. Data for all healthy volunteers (n = 8) and patients with complete occlusions (n = 3) were correctly classified. Data for two of the 12 patients with mild to severe (but not occlusive) disease were misclassified by one category.

Adult↗
PubMed2002

Quantification of distention in CT colonography: development and validation of three computer algorithms.

Three bowel distention-measuring algorithms for use at computed tomographic (CT) colonography were developed, validated in phantoms, and applied to a human CT colonographic data set. The three algorithms are the cross-sectional area method, the moving spheres method, and the segmental volume method. Each algorithm effectively quantified distention, but accuracy varied between methods. Clinical feasibility was demonstrated. Depending on the desired spatial resolution and accuracy, each algorithm can quantitatively depict colonic diameter in CT colonography.

Algorithms↗
PubMed2002

Automated generation of curved planar reformations from volume data: method and evaluation.

The authors developed and evaluated a method to automatically create interactive vascular curved planar reformations with computed tomographic (CT) angiographic data. The method decreased user interaction time by 86%, from 15 to 2 minutes. Expert reviewers were asked to indicate their confidence in differentiating automatically created images from clinical-quality manually produced images. The area under the receiver operating characteristic curve was 0.45 (95% CI: 0.39, 0.51), and a test of equivalency indicated that reviewers could not distinguish between images. They also graded image quality as equivalent to that with manual methods and found fewer artifacts on automatically created images. Automatic methods rapidly produce curved planar reformations of equivalent quality with reduced time and effort.

Humans↗
PubMedDate not supplied

Computed tomography colonography: feasibility of computer-aided polyp detection in a "first reader" paradigm.

OBJECTIVE: : To determine the feasibility of a computer-aided detection (CAD) algorithm as the "first reader" in computed tomography colonography (CTC). METHODS: : In phase 1 of a 2-part blind trial, we measured the performance of 3 radiologists reading 41 CTC studies without CAD. In phase 2, readers interpreted the same cases using a CAD list of 30 potential polyps. RESULTS: : Unassisted readers detected, on average, 63% of polyps > or =10 mm in diameter. Using CAD, the sensitivity was 74% (not statistically different). Per-patient analysis showed a trend toward increased sensitivity for polyps > or =10 mm in diameter, from 73% to 90% with CAD (not significant) without decreasing specificity. Computer-aided detection significantly decreased interobserver variability (P = 0.017). Average time to detection of the first polyp decreased significantly with CAD, whereas total reading case reading time was unchanged. CONCLUSION: : Computer-aided detection as a first reader in CTC was associated with similar per-polyp and per-patient detection sensitivity to unassisted reading. Computer-aided detection decreased interobserver variability and reduced the time required to detect the first polyp.

Adult↗