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Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that involves the central and peripheral nervous systems. Accurate detection and segmentation of neurofibromas are essential for assessing tumor burden and longitudinal tumor size changes. Automatic convolutional neural networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical location and heterogeneous appearance on MRI. In this study, we propose deep interactive networks (DINs) to address the above limitations. User interactions guide the model to recognize complicated tumors and quickly adapt to heterogeneous tumors. We introduce a simple but effective Exponential Distance Transform (ExpDT) that converts user interactions into guide maps regarded as the spatial and appearance prior. Comparing with popular Euclidean and geodesic distances, ExpDT is more robust to various image sizes, which reserves the distribution of interactive inputs. Furthermore, to enhance the tumor-related features, we design a deep interactive module to propagate the guides into deeper layers. We train and evaluate DINs on three MRI data sets from NF1 patients. The experiment results yield significant improvements of 44% and 14% in DSC comparing with automated and other interactive methods, respectively. We also experimentally demonstrate the efficiency of DINs in reducing user burden when comparing with conventional interactive methods. The source code of our method is available at \url{https://github.com/Jarvis73/DINs}., Accepted by IEEE Journal of Biomedical and Health Informatics (JBHI)

Locating the neurofibromatosis boundaries is one of the most challenging tasks due to the properties of low contrast and irregular blurred boundaries. To handle this problem, we propose Deep Parametric Active Contours (DPAC), a novel end-to-end framework that integrates the precise boundary localization abilities of the Active Contour Models (ACMs) with the robust nonlinear priori feature extraction abilities of the Convolutional Neural Networks (CNN). DPAC employs CNN to predict the parameter maps of the ACM per instance and incorporates them into the energy functional of the ACM to locate the neurofibromatosis boundaries, which makes DPAC end-to-end trainable. We evaluate the proposed method on three neurofibromatosis datasets that we created, and experimental results show that our DPAC method can improve the boundary similarity and precision of the classic ACMs. Even in the case with a small amount of data, our method can achieve good performance outperforming similar methods.

Medial ulnar collateral ligament (UCL) repair utilization is increasing in recent years, bolstered by shorter rehabilitation and satisfactory clinical outcomes. Although previous literature has illustrated the importance of tunnel position on restoring graft isometry in reconstruction, there remains a paucity of literature guiding anchor placement in UCL repair. The purpose of this study is to design a 3-dimensional (3D) elbow model to understand the effect of anchor location on UCL repair isometry.A 3D computer model of an elbow joint was created using computed tomographic and magnetic resonance imaging MRI scans from a single patient. The humeral and ulnar attachments of the UCL were plotted using 3 methodologies: (1) geometric cloud mapping and (2) quantitative measurements from the anatomic studies by Camp et al and (3) Frangiamore et al. A 3.5-mm-diameter clockface was placed on each attachment site, which allowed for simulation of 12 distinct 1.75-mm deviations in anchor position. The 3 models were ranged through 0°-120° at 10° increments, and the 3D distances were measured between the ligament centroids. The humeral and ulnar anchors were sequentially repositioned around the clockfaces, and construct lengths were again measured to evaluate changes in isometry. A paired Student t test was performed to determine if there was a significant difference in isometry between the humeral and ulnar anchor deviations.Using method 1, the UCL repair length at 90° of elbow flexion was 26.8 mm. This construct underwent 13.6 mm of total excursion for a 46.4% change in length throughout its arc of motion. Method 2 produced a 19.3-mm construct that underwent 0.8 mm of excursion for a 3.9% length change throughout the arc. Method 3 produced a 24.5-mm construct that underwent 2.3 mm of excursion for a 9.4% length change. Identifying ligament footprints using the quantitative anatomic measurements from Camp et al and Frangiamore et al improved construct isometry through 120° of flexion (length changes of 3.9% and 9.4%, respectively) when compared to using the geometric cloud technique alone (46.4% length change). Humeral anchor deviations produced a significant increase in repair construct excursion compared with ulnar anchor deviations (P .001).When performing UCL repair, small deviations in humeral anchor position may significantly influence ligament repair isometry. Using quantitative anatomic data may help identify anchor positions with improved repair isometry. Particularly when addressing detachments of the humeral footprint, surgeons should be critical of the humeral anchor position in order to restore native anatomy and optimal biomechanics.

This paper is concerned with large time behavior of solutions to a semi-discrete model involving nonlinear competition that describes the evolution of a trait-structured population. Under some threshold assumptions, the steady solution is shown unique and strictly positive, and also globally stable. The exponential convergence rate to the steady state is also established. These results are consistent with the results in [P.-E. Jabin, H. L. Liu. Nonlinearity 30 (2017) 4220–4238] for the continuous model.

Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semi-automated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases.

This paper is concerned with the discrete dynamics of an integro-differential model that describes the evolution of a population structured with respect to a continuous trait. Various time-asymptotic convergence rates towards the discrete evolutionary stable distribution (ESD) are established. For some special ESD satisfying a strict sign condition, the exponential convergence rates are obtained for both semi-discrete and fully discrete schemes. Towards the general ESD, the algebraic convergence rate that we find is consistent with the known result for the continuous model.

In this paper, we present entropy satisfying schemes for solving an integro-differential equation that describes the evolution of a population structured with respect to a continuous trait. In [P.-E. Jabin and G. Raoul, J. Math. Biol., 63 (2011), pp. 493--517] solutions are shown to converge toward the so-called evolutionary stable distribution (ESD) as time becomes large, using the relative entropy. At the discrete level, the ESD is shown to be the solution to a quadratic programming problem and can be computed by any well-established nonlinear programing algorithm. The schemes are then shown to satisfy the entropy dissipation inequality on the set where initial data are positive and the numerical solutions tend toward the discrete ESD in time. An alternative algorithm is presented to capture the global ESD for nonnegative initial data, which is made possible due to the mutation mechanism built into the modified scheme. A series of numerical tests are given to confirm both accuracy and the entropy satisf...

To evaluate the feasibility and sensitivity of the 3D-reading of fecal-tagging CT colonography (CTC) with a novel structure-analysis electronic cleansing (SAEC) in detecting colorectal flat lesions in comparison with a cleansed 3D reading with Viatronix V3D Colon system (V3D) and primary uncleansed 2D reading (2D).Forty CTC cases with flat lesions were retrospectively observed. The Subjects from a multicenter clinical trial underwent cathartic bowel preparation with orally administrated barium-based fecal-tagging. Sixty-nine flat lesions were confirmed using colonoscopy and histopathology as a reference standard. The results from SAEC reading were compared with those of prospective V3D and 2D readings.Overall detection sensitivity with SAEC was 52% (36/69), which was statistically higher than that of 32% (22/69) and 29% (20/69) with V3D and 2D readings, respectively (p0.05). The sensitivities in detecting not-on-fold flat lesions were 63% (24/38), 45% (17/38), and 42% (16/38) with SAEC, V3D, and 2D readings, respectively; whereas those of on-fold flat lesions were 39% (12/31), 16% (5/31), and 13% (4/31), respectively. None of the eight flat lesions (2-9mm) at cecum was detected by any of the three reading methods. Excluding the flat lesions at cecum, the sensitivity with SAEC for detecting flat lesion ≥4mm increased to 84% (31/37).The fecal-tagging CTC with structure-analysis electronic cleansing could yield a high sensitivity for detecting flat lesions ≥4mm. The not-on-fold flat lesions were detected with higher sensitivity than on-fold flat lesions.

It is commonly believed that the size of a pneumothorax is an important determinant of treatment decision, in particular regarding whether chest tube drainage (CTD) is required. However, the volumetric quantification of pneumothoraces has not routinely been performed in clinics. In this paper, we introduced an automated computer-aided volumetry (CAV) scheme for quantification of volume of pneumothoraces in chest multi-detect CT (MDCT) images. Moreover, we investigated the impact of accurate volume of pneumothoraces in the improvement of the performance in decision-making regarding CTD in the management of traumatic pneumothoraces. For this purpose, an occurrence frequency map was calculated for quantitative analysis of the importance of each clinical parameter in the decision-making regarding CTD by a computer simulation of decision-making using a genetic algorithm (GA) and a support vector machine (SVM). A total of 14 clinical parameters, including volume of pneumothorax calculated by our CAV scheme, was collected as parameters available for decision-making. The results showed that volume was the dominant parameter in decision-making regarding CTD, with an occurrence frequency value of 1.00. The results also indicated that the inclusion of volume provided the best performance that was statistically significant compared to the other tests in which volume was excluded from the clinical parameters. This study provides the scientific evidence for the application of CAV scheme in MDCT volumetric quantification of pneumothoraces in the management of clinically stable chest trauma patients with traumatic pneumothorax.

Electronic cleansing (EC) is a method that segments fecal material tagged by an X-ray-opaque oral contrast agent in computed tomographic colonography (CTC) images, and effectively removes the material for digitally cleansing the colon. In this study, we developed a novel EC method, called mosaic decomposition (MD), for reduction of the artifacts due to incomplete cleansing of inhomogeneously tagged fecal material in CTC images, especially in noncathartic CTC images. In our approach, the entire colonic region, including the residual fecal regions, was first decomposed into a set of local homogeneous regions, called tiles, after application of a 3-D watershed transform to the CTC images. Each tile was then subjected to a single-class support vector machine (SVM) classifier for soft-tissue discrimination. The feature set of the soft-tissue SVM classifier was selected by a genetic algorithm (GA). A scalar index, called a soft-tissue likelihood, is formulated for differentiation of the soft-tissue tiles from those of other materials. Then, EC based on MD, called MD-cleansing, is performed by first initializing of the level-set front with the classified tagged regions; the front is then evolved by use of a speed function that was designed, based on the soft-tissue index, to reserve the submerged soft-tissue structures while suppressing the residual fecal regions. The performance of the MD-cleansing method was evaluated by use of a phantom and of clinical cases. In the phantom evaluation, our MD-cleansing was trained with the supine (prone) scan and tested on the prone (supine) scan, respectively. In both cases, the sensitivity and specificity of classification were 100%. The average cleansing ratio was 90.6%, and the soft-tissue preservation ratio was 97.6%. In the clinical evaluation, 10 noncathartic CTC cases (20 scans) were collected, and the ground truth of a total of 2095 tiles was established by manual assignment of a material class to each tile. Five cases were randomly selected for training GA/SVM, and the remaining five cases were used for testing. The overall sensitivity and specificity of the proposed classification scheme were 97.1% and 85.3%, respectively, and the accuracy was 94.6%. The area under the ROC curve (Az) was 0.96. Our results indicated that the use of MD-cleansing substantially improved the effectiveness of our EC method in the reduction of incomplete cleansing artifacts.

Rationale and Objectives Our purpose in this study was to develop an automated computer-aided volumetry (CAV) scheme for quantifying pneumothorax in multidetector computed tomography (MDCT) images for pediatric patients and to investigate the imaging parameters that may affect its accuracy. Materials and Methods Fifty-eight consecutive pediatric patients (mean age 12 ± 6 years) with pneumothorax who underwent MDCT for evaluation were collected retrospectively for this study. All cases were imaged by a 16- or 64-MDCT scanner with weight-based kilovoltage, low-dose tube current, 1.0−1.5 pitch, 0.6−5.0 mm slice thickness, and a B70f (sharp) or B31f (soft) reconstruction kernel. Sixty-three pneumothoraces ≥1 mL were visually identified in the left (n = 30) and right (n = 33) lungs. Each identified pneumothorax was contoured manually on an Amira workstation V4.1.1 (Mercury Computer Systems, Chelmsford, MA) by two radiologists in consensus. The computerized volumes of the pneumothoraces were determined by application of our CAV scheme. The accuracy of our automated CAV scheme was evaluated by comparison between computerized volumetry and manual volumetry, for the total volume of pneumothoraces in the left and right lungs. Results The mean difference between the computerized volumetry and the manual volumetry for all 63 pneumothoraces ≥1 mL was 8.2%. For pneumothoraces ≥10 mL, ≥50 mL, and ≥200 mL, the mean differences were 7.7% (n = 57), 7.3% (n = 33), and 6.4% (n = 13), respectively. The correlation coefficient was 0.99 between the computerized volume and the manual volume of pneumothoraces. Bland-Altman analysis showed that computerized volumetry has a mean difference of -5.1% compared to manual volumetry. For all pneumothoraces ≥10 mL, the mean differences for slice thickness ≤1.25 mm, = 1.5 mm, and = 5.0 mm were 6.1% (n = 28), 3.5% (n = 10), and 12.2% (n = 19), respectively. For the two reconstruction kernels, B70f and B31f, the mean differences were 6.3% (n = 42, B70f) and 11.7% (n = 15, B31f), respectively. Conclusion Our automated CAV scheme provides an accurate measurement of pneumothorax volume in MDCT images of pediatric patients. For accurate volumetric quantification of pneumothorax in children in MDCT images by use of the automated CAV scheme, we recommended reconstruction parameters based on a slice thickness ≤1.5 mm and the reconstruction kernel B70f.

Objective: Given the frequency of transition from anorexia nervosa to bulimia nervosa, this study investigated whether a history of activity-based anorexia (ABA) during adolescence would promote binge eating during adulthood in female rats. Method: Adolescent rats were given 1-h unlimited access to chow and ad libitum access to a running wheel until body weight reached

Electronic cleansing (EC) is an emerging method for segmentation of fecal material in CT colonography (CTC) that is used for reducing or eliminating the requirement for cathartic bowel preparation and hence for improving patients’ adherence to recommendations for colon cancer screening. In EC, feces tagged by an x-ray-opaque oral contrast agent are removed from the CTC images, effectively cleansing the colon after image acquisition. Existing EC approaches tend to suffer from the following cleansing artifacts: degradation of soft-tissue structures because of pseudo-enhancement caused by the surrounding tagged fecal materials, and pseudo soft-tissue structures and false fistulas caused by partial volume effects at the boundary between the air lumen and the tagged regions, called the air-tagging boundary (AT boundary). In this study, we developed a novel EC method, called structure-analysis cleansing, which effectively avoids these cleansing artifacts. In our method, submerged soft-tissue structures are recognized by their local morphologic signatures that are characterized based on the eigenvalues of a three-dimensional Hessian matrix. A structure-enhancement function is formulated for enhancing of the soft-tissue structures. In addition, thin folds sandwiched between the air lumen and tagged regions are enhanced by analysis of the local roughness based on multi-scale volumetric curvedness. Both values of the structure-enhancement function and the local roughness are integrated into the speed function of a level set method for delineating the tagged fecal materials. Thus, submerged soft-tissue structures as well as soft-tissue structures adhering to the tagged regions are preserved, whereas the tagged regions are removed along with the associated AT boundaries from CTC images. Evaluation of the quality of the cleansing based on polyps and folds in a colon phantom, as well as on polyps in clinical cathartic and noncathartic CTC cases with fluid and stool tagging, showed that our structure-analysis cleansing method is significantly superior to that of our previous thresholding-based EC method. It provides a cleansed colon with substantially reduced subtraction artifacts.

Objectives Neurofibromatosis 1 (NF1), NF2, and schwannomatosis are characterized by a predisposition to develop multiple neurofibromas and schwannomas. Currently, there is no blood test to estimate tumor burden in patients with these disorders. We explored whether S100β would act as a biomarker of tumor burden in NF since S100β is a classic immunohistochemical marker of astrocytes, oligodendrocytes and Schwann cells and a small study showed S100β concentrations correlate with the volume of vestibular schwannomas. Design and methods We calculated whole-body tumor burden in subjects with NF1, NF2, and schwannomatosis using whole-body MRI (WBMRI) and measured the concentration of S100β in plasma using ELISA. We used chi-square tests and Spearman rank correlations to test the relationship between S100β levels and whole-body tumor burden. Results 127 consecutive patients were enrolled in the study (69 NF1 patients, 28 NF2 patients, and 30 schwannomatosis patients). The median age was 40 years, 43% were male, and median whole-body tumor volume was 26.9 mL. There was no relationship between the presence of internal tumors and the presence of detectable S100β in blood for the overall group or for individual diagnoses (p > 0.05 by chi-square for all comparisons). Similarly, there was no correlation between whole-body tumor volume and S100β concentration for the overall group or for individual diagnoses (p > 0.05 by Spearman for all comparisons). Conclusions Plasma S100β is not a useful biomarker for tumor burden in the neurofibromatoses. Further work is needed to identify a reliable biomarker of tumor burden in NF patients.

Analyses of sea surface height anomalies (SSHA) from Jan. 1993 to Dec. 2001 measured by TOPEX/ Poseidon (T/P) satellite altimetry indicate that there are two energy highs of SSHA fluctuations on both sides of the Luzon Strait. The highs centered at (19.5° N, 119.5° E) and (22.0° N, 124.0° E) respectively, are separated by a low energy band in the Strait. Empirical Orthogonal Function (EOF) analysis on the “Far Field” suggests that fluctuations of these two areas are mutually independent. EOF analysis on the “Near Field” shows that SSHA oscillation in the northeastern South China Sea (SCS) is dominated by seasonal variations accompanied with significant intra-seasonal and inter-annual fluctuations. Among which, EOF1 and EOF2 reflect 66.7% of the total variance. They are both seasonal but showing three months difference in phase. The basic pattern of EOF1 appears to be the seasonal SSHA oscillation centered at (18.0° N, 119.0° E) northwest off Luzon Island, which shows maximums (minimums) in August/September (January/February) and mainly reflects the seasonal variation of steric heights of the deep SCS basin and its corresponding circulation adjustments. The basic pattern of EOF2 is featured by seasonal seesaw of sea surface elevation in the SE-NW direction, which imaged the Ekman adjustment of sea surface height (SSH) in the northeastern South China Sea (NESCS) driven by the monsoons. Our analyses also reveal that intra-seasonal variation is an important component of SSHA fluctuation of the NESCS, which reflects the frequent occurrence of mesoscale processes west to the Luzon Strait. Moreover, inter-annual variations appear in all major EOF modes.

The simulation of Radiotherapy Treatment Planning (RTP) is a normal procedure in oncology clinics carried out on a Simulator machine. The Virtual Simulation of RTP replaces the real Simulator machine with a virtual one by using the CT data sets of a patient instead of the real patient. In this paper, we present a collaborative virtual simulation environment of RTP, named EU-VIRTUOSO, which is based on volume rendering and telecommunication techniques. The RTP procedure is visualised on a virtual patient, which is created by using the CT data of the patient. Different volume rendering and volume interaction techniques, such as DRR, MIP, gradient surface, and iso-surface, supply physicians with high quality rendering images to simulate the real working environment of the Simulator machine. In the collaborative environment, physicians distributed at different locations can work together via network to plan the treatment or to validate the treatment plan on-line by a collaborative application sharing approach. Both concepts virtualised planning and collaborative planning improve the efficiency and accuracy of a radiotherapy treatment while reducing the effort for an individual patient.

The main difference between multi-volume rendering and mono-volume rendering is data intermixing. In this paper, we present three levels of data intermixing and their rendering pipelines in direct multi-volume rendering, which discriminate image level intensity intermixing, accumulation level opacity intermixing, and illumination model level parameter intermixing. In the context of radiotherapy treatment planning, different data intermixing methods are applied to three volumes, including CT volume, Dose volume, and Segmentation volume, to compare the features of different data intermixing methods.

In this paper we present a new Maximum Intensity Projection (MIP) algorithm which was implemented employing splatting in a shear-warp context. This algorithm renders a MIP image by first splatting each voxel on two intermediate spaces called "worksheet" and "shear image". Then, the maximum value is evaluated between worksheet and shear image. Finally, shear image is warped on the screen to generate the result image. Different footprints implementing different quality modes are discussed. In addition, we introduced a line encoded indexing speed-up method to obtain interactive speed. This algorithm allows for a quantitative, predictable trade-off between interactivity and image quality.

This paper focuses on entirety interpretation, representation and reconstruction of three-dimensional volume data sets based on the physical model of the data. The data model is represented by three-dimensional geometric model. The surfaces inside the datafield are extracted and matched to the model surfaces in order to reconstruct the new datafield based on the model. A conclusion is drawn that physical modeling provides a good basis and approach to interpret and represent the data sets. This paper also presents a subdivision algorithm to fast trace B-spline curve and the contrary algorithm is adopted to extract the geometry feature of the curve.

Under the volume segmentation in voxel space, a lot of details, some fine and thin objects, are ignored. In order to accurately display these details, this paper has developed a methodology for volume segmentation in subvoxel space. In the subvoxel space, most of the “bridges” between adjacent layers are broken down. Based on the subvoxel space, an automatic segmentation algorithm reserving details is discussed. After segmentation, volume data in subvoxel space are reduced to original voxel space. Thus, the details with width of only one or several voxels are extracted and displayed.

Based on the equation of transfer in transport theory of optical physics, a new volume rendering model, called composed scattering model (CSM), is presented. In calculating the scattering term of the equation, it is decomposed into volume scattering intensity and surface scattering intensity, and they are composed with the boundary detection operator as the weight function. This proposed model differs from the most current volume rendering models in the aspect that in CSM segmentation and illumination intensity calculation are taken as two coherent parts while in existing models they are regarded as two separate ones. This model has been applied to the direct volume rendering of 3D data sets obtained by CT and MRI. The resultant images show not only rich details but also clear boundary surfaces. CSM is demonstrated to be an accurate volume rendering model suitable for CT and MRI data sets.

The purpose of this evaluation was to compare the tagging quality of a barium-based regimen with that of iodine-based regimens for computed tomographic (CT) colonography.Tagging quality was assessed retrospectively in three different types of fecal-tagging CT colonographic cases: 24 barium-based cases, 22 nonionic iodine-based cases, and 24 ionic iodine-based cases. For the purpose of evaluation, the large intestine was divided into six segments, and the tagging homogeneity of a total of 420 segments (70 patients) was graded by three blinded readers from 0 (heterogeneous) to 4 (homogeneous).For barium-based cases, the average score for the three readers was 2.4, whereas it was 3.4 for nonionic iodine and 3.6 for ionic iodine. The percentages of segments that were assigned scores of 4 (excellent tagging [100%]) were 11.6%, 61.9%, and 72.9% for the barium-based, nonionic iodine-based, and ionic iodine-based regimens, respectively. The homogeneity scores of iodine-based fecal-tagging regimens were significantly higher than those of the barium-based fecal-tagging regimen (P.001). The CT attenuation values of tagging in the cases were also assessed: the minimum and maximum values were significantly higher for the iodine-based regimens than for the barium-based regimen (P.001).The iodine-based fecal-tagging regimens provide significantly greater homogeneity in oral-tagging fecal material than the barium-based fecal-tagging regimen. Iodine-based fecal-tagging regimens can provide an appropriate method for use in nonlaxative or minimum-laxative CT colonography.

Background Colon screening by optical colonoscopy (OC) or computed tomographic colonography (CTC) requires a laxative bowel preparation, which inhibits screening participation. Objective To assess the performance of detecting adenomas 6 mm or larger and patient experience of laxative-free, computer-aided CTC. Design Prospective test comparison of laxative-free CTC and OC. The CTC included electronic cleansing and computer-aided detection. Optical colonoscopy examinations were initially blinded to CTC results, which were subsequently revealed during colonoscope withdrawal; this method permitted reexamination to resolve discrepant findings. Unblinded OC served as a reference standard. (ClinicalTrials.gov registration number: NCT01200303) Setting Multicenter ambulatory imaging and endoscopy centers. Participants 605 adults aged 50 to 85 years at average to moderate risk for colon cancer. Measurements Per-patient sensitivity and specificity of CTC and first-pass OC for detecting adenomas at thresholds of 10 mm or greater, 8 mm or greater, and 6 mm or greater; per-lesion sensitivity and survey data describing patient experience with preparations and examinations. Results For adenomas 10 mm or larger, per-patient sensitivity of CTC was 0.91 (95% CI, 0.71 to 0.99) and specificity was 0.85 (CI, 0.82 to 0.88); sensitivity of OC was 0.95 (CI, 0.77 to 1.00) and specificity was 0.89 (CI, 0.86 to 0.91). Sensitivity of CTC was 0.70 (CI, 0.53 to 0.83) for adenomas 8 mm or larger and 0.59 (CI, 0.47 to 0.70) for those 6 mm or larger; sensitivity of OC for adenomas 8 mm or larger was 0.88 (CI, 0.73 to 0.96) and 0.76 (CI, 0.64 to 0.85) for those 6 mm or larger. The specificity of OC at the threshold of 8 mm or larger was 0.91 and at 6 mm or larger was 0.94. Specificity for OC was greater than that for CTC, which was 0.86 at the threshold of 8 mm or larger and 0.88 at 6 mm or larger (P= 0.02). Reported participant experience for comfort and difficulty of examination preparation was better with CTC than OC. Limitations There were 3 CTC readers. The survey instrument was not independently validated. Conclusion Computed tomographic colonography was accurate in detecting adenomas 10 mm or larger but less so for smaller lesions. Patient experience was better with laxative-free CTC. These results suggest a possible role for laxative-free CTC as an alternate screening method.