Limited FOV Research Articles

Literature review and case study: unveiling the complexities of mandibular second molar with three distal canals

The occurrence of morphological deviations in root canal anatomy is challenging and can influence treatment outcomes. While mandibular second molars typically have 2 roots and 3 canals, a unique case emerged in a 23-year-old Indian male with a symptomatic permanent left mandibular second molar showing three distal, two mesial canals, and two roots. The tooth presented as non-mobile but tender on percussion. The patient was diagnosed with symptomatic irreversible pulpitis with symptomatic apical periodontitis. The presence of mid distal canal was radiographically and visually confirmed with the aid of limited FOV cone-beam computed tomography and the dental operating microscope. Successful nonsurgical root canal treatment with 6-month follow-up has been documented in this case report.

Rebar-tying Robot based on machine vision and coverage path planning

Automation technology can replace manual work in the traditional construction industry, improve quality and efficiency, and reduce costs. This paper developed a rebar-tying robot for tying the intersection of rebars. It proposed a Hough transform multi-segment fitting method to detect the intersections of rebars in real-time acquired RGB-D images. To cover the surface of the rebar net as much as possible under the condition of limited camera FOV, this paper designed a coverage path planning method to plan the path of the photo positions and the detected intersections of rebars efficiently and orderly. The experimental results show that the robot achieved an accuracy rate of 99.4 % in intersection detection, the detection error is within 2.8 mm, the single tying time is 1.85 s, and the average tying time is 5.5 s, which is faster than most robots. The robot realizes the task of automatically tying the intersection of rebars, which is robust and efficient, without duplication or omission.

Uloga CBCT-a u području dentalne implantologije

As radiology rapidly and continuously develops, dental radiological devices are becoming integral to dental practice. CBCT enables a precise three-dimensional image of the orofacial region. The wider use of CBCT devices is further encouraged by the flaws of two-dimensional radiological methods, especially concerning dental implantology. This paper aims to describe the advantages and disadvantages of using CBCT devices in planning and placing dental implants. It offers an insight into artifacts and radiation doses and their influence on the quality of the resulting image. The generation of multiplanar slices of the area of interest, the possibility of 3D reconstruction, the reduction of the magnification error, and the radiation dose are some of the important advantages of the CBCT device. A Limited FOV and volume of the screening, a low range of contrasts, measuring of bone density and shades of grayscale, as well as possible artifacts of movement are some of its disadvantages. Not only did the CBCT device find a purpose in the processes of planning and placement of dental implants, but also in postoperative assessments of healing and possible complications. CBCT balances the price and radiation dose and the quality of clinical information. It is used in oral surgery, orthodontics, periodontology, and endodontics. Due to its precision and quality, the CBCT device is the foundation of dental implantology.

Effect of extended field-of-view approaches on the accuracy of stopping power ratio estimation for single-energy computed tomography simulators.

Extended field-of-view (eFOV) methods have been proposed to generate larger demonstration FOVs for computed tomography (CT) simulators with a limited scanning FOV (sFOV) size in order to ensure accurate dose calculation and patient collision avoidance. Although the efficacy of these strategies has been evaluated for photon applications, the effect of stopping power ratio (SPR) estimation on proton therapy has not been studied. This study investigated the effect of an eFOV approach on the accuracy of SPR to water estimation in homogeneous and heterogeneous phantoms. To simulate patient geometries, tissue-equivalent material (TEM) and customized extension phantoms were used. The TEM phantom supported various rod arrangements through predefined holes. Images were reconstructed to three FOV sizes using a commercial eFOV technique. A single-energy CT stoichiometric method was used to generate Hounsfield unit (HU) to SPR (HU-to-SPR) conversion curves for each FOV. To investigate the effect of rod location in the sFOV and eFOV regions, eight TEM rods were placed at off-center distances in the homogeneous phantom and scanned individually. Similarly, 16 TEM rods were placed in the heterogeneous TEM phantom and scanned simultaneously. The conversion curves derived from the sFOV and eFOV data were identical. The average SPR differences of soft-tissue, bone, and lung materials for rods placed at various off-center locations were 3.3%, 4.8%, and 39.6%, respectively. In the heterogeneous phantom, the difference was within 1.0% in the absence of extension. However, in the presence of extension, the difference increased to 2.8% for all rods, except for lung materials, whose difference was 4.8%. When an eFOV method is used, the SPR variation in phantoms considerably increases for all TEM rods, especially for lung TEM rods. This phenomenon may substantially increase the uncertainty of HU-to-SPR conversion. Therefore, image reconstruction with a standard FOV size is recommended.

Evaluation of generalization ability for deep learning-based auto-segmentation accuracy in limited field of view CBCT of male pelvic region.

The aim of this study was to evaluate generalization ability of segmentation accuracy for limited FOV CBCT in the male pelvic region using a full-image CNN. Auto-segmentation accuracy was evaluated using various datasets with different intensity distributions and FOV sizes. A total of 171 CBCT datasets from patients with prostate cancer were enrolled. There were 151, 10, and 10 CBCT datasets acquired from Vero4DRT, TrueBeam STx, and Clinac-iX, respectively. The FOV for Vero4DRT, TrueBeam STx, and Clinac-iX was 20, 26, and 25cm, respectively. The ROIs, including the bladder, prostate, rectum, and seminal vesicles, were manually delineated. The U2 -Net CNN network architecture was used to train the segmentation model. A total of 131 limited FOV CBCT datasets from Vero4DRT were used for training (104 datasets) and validation (27 datasets); thereafter the rest were for testing. The training routine was set to save the best weight values when the DSC in the validation set was maximized. Segmentation accuracy was qualitatively and quantitatively evaluated between the ground truth and predicted ROIs in the different testing datasets. The mean scores±standard deviation of visual evaluation for bladder, prostate, rectum, and seminal vesicle in all treatment machines were 1.0±0.7, 1.5±0.6, 1.4±0.6, and 2.1±0.8 points, respectively. The median DSC values for all imaging devices were ≥0.94 for the bladder, 0.84-0.87 for the prostate and rectum, and 0.48-0.69 for the seminal vesicles. Although the DSC values for the bladder and seminal vesicles were significantly different among the three imaging devices, the DSC value of the bladder changed by less than 1% point. The median MSD values for all imaging devices were ≤1.2mm for the bladder and 1.4-2.2mm for the prostate, rectum, and seminal vesicles. The MSD values for the seminal vesicles were significantly different between the three imaging devices. The proposed method is effective for testing datasets with different intensity distributions and FOV from training datasets.

CR-LDSO: Direct Sparse LiDAR-Assisted Visual Odometry With Cloud Reusing

LiDAR-assisted visual odometry (VO) is a widely-used solution for pose estimation and mapping. However, most existing LiDAR-assisted VO systems could suffer from the problems of 1) lacking distinctive and evenly distributed pixels for tracking due to the sparsity of LiDAR points and limited FOV overlap between a camera and LiDAR, and 2) nontrivial errors when processing LiDAR point clouds. To address above problems, we present CR-LDSO, a direct sparse LiDAR-assisted VO with the core parts being: 1) a novel cloud reusing method with point extraction/re-extraction to increase both the camera-LiDAR FOV overlap and the number of high-quality tracking pixels and 2) an occlusion removal method to exclude mismatching pixels due to occluded 3D object from sliding-window optimization and a point extraction strategy without depth interpolation. Extensive experimental results on public datasets demonstrates the superiority of our method to the existing state-of-the-art methods.

Design and performance evaluation of a 4[formula omitted]-view gamma camera with mosaic-patterned 3D position-sensitive scintillators

Gamma hotspots imaging is of dramatic demand in nuclear-related industrial and homeland security activities such as nuclear facilities monitoring, nuclear emergency response and border inspection. Gamma cameras with mechanical collimation (i.e., coded-aperture) have reduced sensitivity, limited FOV and are typically heavy-weighted. Compton cameras with electrical collimation can achieve higher sensitivity as well as being capable of performing portable 4π-view imaging. However, imageable isotopes of Compton cameras are limited to middle- or high-energy ones. We have previously proposed the concept to image gamma hotspots with a 3-D position-sensitive scintillation detector. This approach achieves high sensitivity, 4π-view FOV and is effective in a wide energy range. In this work, we present a novel gamma camera design aiming for high-resolution gamma imaging. The proposed detector consists of interspaced GAGG(Ce) scintillators that form a mosaic pattern, which is coupled to SiPM arrays on both ends. 3-D photon interaction position information is measured with a dual-end-readout technique. The measured interaction positions for detected photon events are rebinned into the projection data, and the gamma image is reconstructed using a maximum likelihood expectation maximization (MLEM) algorithm. We perform Monte Carlo simulations and experiments to evaluate the performance of the proposed gamma camera design for imaging 99mTc and 137Cs sources. Results show that the proposed gamma camera achieves a positioning accuracy of less than 1° for a 99mTc source and 3° for a 137Cs source. It can clearly resolve two 99mTc sources with 10° separation and two 137Cs point sources with 23° separation, as well as a 2 × 6 99mTc point-source-array with 20° separation clearly. We conclude that the proposed gamma camera design is profitable in portability, FOV, sensitivity, image resolution and energy range. It has promising application potential in fast and accurate radiation monitoring tasks in nuclear security applications.

Comparative analysis of the accuracy of periapical radiography and cone-beam computed tomography for diagnosing complex endodontic pathoses using a gold standard reference - A prospective clinical study.

To compare the diagnostic accuracy of periapical radiography (PR) and cone-beam computed tomography (CBCT) for detecting pathoses of endodontic origin using the gold standard of direct surgical visualization. Seventy-four patients (112 teeth) underwent radiographic examination by periapical radiography and CBCT. The presence of periapical lesions, root perforations and their locations, root fractures, different types of external root resorptive defects, apicomarginal bone defects, through and through bone defects, buccal bone status and proximity of root apices and lesions to vital anatomical structures were assessed. These same parameters were assessed intraoperatively by direct surgical visualization which served as the gold standard reference for the radiographic assessments. Sensitivity, specificity, positive predictive values, negative predictive values were determined for comparison of diagnostic accuracy between two modalities. There were no significant differences between the two imaging modalities for root resorptive defects and root fractures. However, CBCT accurately detected periapical lesions, root perforations, apicomarginal bone defects and through and through bone defects. The overall accuracy of CBCT varied from 91% to 96% in detecting dehiscence and fenestration of buccal cortical plates. Overall, CBCT had a higher diagnostic accuracy in complex endodontic pathoses compared to PR. Nevertheless, CBCT failed to diagnose apicomarginal bone defects in 33% teeth. In evaluating the status of buccal cortical plate from CBCT images, observers could detect absence of bone better than its presence. Thus, limited FOV CBCT should be considered for selective cases where periapical radiography has diagnostic ambiguity.

Efficient Reactive Obstacle Avoidance Using Spirals for Escape

The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies.

LoLa-SLAM: Low-Latency LiDAR SLAM Using Continuous Scan Slicing

Real-time 6D pose estimation is a key component for autonomous indoor navigation of Unmanned Aerial Vehicles (UAVs). This letter presents a low-latency LiDAR SLAM framework based on LiDAR scan slicing and concurrent matching, called LoLa-SLAM. Our framework uses sliced point cloud data from a rotating LiDAR in a concurrent multi-threaded matching pipeline for 6D pose estimation with high update rate and low latency. The LiDAR is actuated using a 2D Lissajous spinning pattern to overcome the sensor's limited FoV. We propose a two-dimensional roughness model to extract the feature points for fine matching and registration of the point cloud. In addition, the pose estimator engages a temporal motion predictor that assists in finding the feature correspondences in the map for the fast convergence of the non-linear optimizer. Subsequently, an Extended Kalman Filter (EKF) is adopted for final pose fusion. The framework is evaluated in multiple experiments by comparing the accuracy, latency, and the update rate of the pose estimation for the trajectories flown in an indoor environment. We quantify the superior quality of the generated volumetric map in comparison to the state-of-the-art frameworks. We further examine the localization precision using ground truth pose information recorded by a total station unit.

Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration.

Multimodal image registration has many applications in diagnostic medical imaging and image-guided interventions, such as Transcatheter Arterial Chemoembolization (TACE) of liver cancer guided by intraprocedural CBCT and pre-operative MR. The ability to register peri-procedurally acquired diagnostic images into the intraprocedural environment can potentially improve the intra-procedural tumor targeting, which will significantly improve therapeutic outcomes. However, the intra-procedural CBCT often suffers from suboptimal image quality due to lack of signal calibration for Hounsfield unit, limited FOV, and motion/metal artifacts. These non-ideal conditions make standard intensity-based multimodal registration methods infeasible to generate correct transformation across modalities. While registration based on anatomic structures, such as segmentation or landmarks, provides an efficient alternative, such anatomic structure information is not always available. One can train a deep learning-based anatomy extractor, but it requires large-scale manual annotations on specific modalities, which are often extremely time-consuming to obtain and require expert radiological readers. To tackle these issues, we leverage annotated datasets already existing in a source modality and propose an anatomy-preserving domain adaptation to segmentation network (APA2Seg-Net) for learning segmentation without target modality ground truth. The segmenters are then integrated into our anatomy-guided multimodal registration based on the robust point matching machine. Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.

Enhancing Solid State LiDAR Mapping with a 2D Spinning LiDAR in Urban Scenario SLAM on Ground Vehicles.

Solid-State LiDAR (SSL) takes an increasing share of the LiDAR market. Compared with traditional spinning LiDAR, SSLs are more compact, energy-efficient and cost-effective. Generally, the current study of SSL mapping is limited to adapting existing SLAM algorithms to an SSL sensor. However, compared with spinning LiDARs, SSLs are different in terms of their irregular scan patterns and limited FOV. Directly applying existing SLAM approaches on them often increase the instability of a mapping process. This study proposes a systematic design, which consists of a dual-LiDAR mapping system and a three DOF interpolated six DOF odometry. For dual-LiDAR mapping, this work uses a 2D LiDAR to enhance a 3D SSL performance on a ground vehicle platform. The proposed system takes a 2D LiDAR to preprocess the scanning field into a number of feature sections according to the curvatures on the 2D fraction. Subsequently, this section information is passed to 3D SSL for direction feature selection. Additionally, this work proposes an odometry interpolation method which uses both LiDARs to generate two separated odometries. The proposed odometry interpolation method selectively determines the appropriate odometry information to update the system state under challenging conditions. Experiments are conducted in different scenarios. The results proves that the proposed approach is able to utilise 12 times more corner features from the environment than the comparied method, thus results in a demonstrable improvement in its absolute position error.

Robot Floor‐Tiling Control Method Based on Finite‐State Machine and Visual Measurement in Limited FOV

In recent years, accelerative aging society is meeting the short supply of young and middle‐aged labor. Particularly, most young people are reluctant to work in the construction industry, which has caused the labor cost of floor tiling to rise year by year. In addition, floor tiling requires workers to continuously bend over or lean over to work, which greatly jeopardizes the physical health of them. Therefore, advanced technology applied in floor tiling is highly demanded to replace the traditional manual method. On the context, the automatic method of floor tiling may promote the transformation and upgrading of the industry. Although a few robots for floor tiling have been developed, the automation of existing systems is still at a low level. This paper proposes a robot floor‐tiling control strategy based on visual measurement feedback and finite‐state machine, in which the calculation of tile position information in limited field of vision is obtained by an improved Canny edge detection and a Hough linear transformation. Moreover, an algorithm for complementing tile position information based on visual measurement is proposed, and the quality of tile laying is evaluated online. To evaluate the effect of the proposed control strategy, the experimental verifications are given. The experimental results indicate that the proposed method can complete the automatic floor tiling with high accuracy.

Distributed Multi-Object Tracking Under Limited Field of View Sensors

We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a novel distributed multi-object tracking algorithm. To accomplish this, we first formalise the concept of label consistency, determine a sufficient condition to achieve it and develop a novel \textit{label consensus approach} that reduces label inconsistency caused by objects' movements from one node's limited FoV to another. Second, we develop a distributed multi-object fusion algorithm that fuses local multi-object state estimates instead of local multi-object densities. This algorithm: i) requires significantly less processing time than multi-object density fusion methods; ii) achieves better tracking accuracy by considering Optimal Sub-Pattern Assignment (OSPA) tracking errors over several scans rather than a single scan; iii) is agnostic to local multi-object tracking techniques, and only requires each node to provide a set of estimated tracks. Thus, it is not necessary to assume that the nodes maintain multi-object densities, and hence the fusion outcomes do not modify local multi-object densities. Numerical experiments demonstrate our proposed solution's real-time computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios. We also release source code at https://github.com/AdelaideAuto-IDLab/Distributed-limitedFoV-MOT for our fusion method to foster developments in DMOT algorithms.

Path planning and guidance for underactuated vehicles with limited field-of-view

When GPS is not available, autonomous vehicles can obtain navigation information by observing a source with a camera or an acoustic system mounted on the frame of the vehicle. This information can be used in the navigation and to overcome the lack of other sources of positioning. However, these systems often have a limited range and angular FOV. Due to this restriction, motion along some paths will make it impossible to obtain the necessary navigation information as the source is no longer in the vehicle's FOV. This paper proposes both a path planning approach and a guidance control law that allows the vehicle to preserve landmarks that are critical for the navigation inside the FOV while at the same time converging to the proposed path. The suggested approach is experimentally validated by implementation in an autonomous underwater vehicle.

A multi-wavelength study of the evolution of early-type galaxies in groups: the ultraviolet view

ABRIDGED- The UV-optical color magnitude diagram (CMD) of rich galaxy groups is characterised by a well developed Red Sequence (RS), a Blue Cloud (BC) and the so-called Green Valley (GV). Loose, less evolved groups of galaxies likely not virialized yet may lack a well defined RS. This is actually explained in the framework of galaxy evolution. We are focussing on understanding galaxy migration towards the RS, checking for signatures of such a transition in their photometric and morphological properties. We report on the UV properties of a sample of ETGs galaxies inhabiting the RS. The analysis of their structures, as derived by fitting a Sersic law to their UV luminosity profiles, suggests the presence of an underlying disk. This is the hallmark of dissipation processes that still must have a role in the evolution of this class of galaxies. SPH simulations with chemo-photometric implementations able to match the global properties of our targets are used to derive their evolutionary paths through UV-optical CDM, providing some fundamental information such as the crossing time through the GV, which depends on their luminosity. The transition from the BC to the RS takes several Gyrs, being about 3-5 Gyr for the the brightest galaxies and more long for fainter ones, if it occurs. The photometric study of nearby galaxy structures in UV is seriously hampered by either the limited FoV of the cameras (e.g in HST) or by the low spatial resolution of the images (e.g in the GALEX). Current missions equipped with telescopes and cameras sensitive to UV wavelengths, such as Swift-UVOT and Astrosat-UVIT, provide a relatively large FoV and better resolution than the GALEX. More powerful UV instruments (size, resolution and FoV) are obviously bound to yield fundamental advances in the accuracy and depth of the surface photometry and in the characterisation of the galaxy environment.

SU‐E‐J‐92: On‐Line Cone Beam CT Based Planning for Emergency and Palliative Radiation Therapy

Purpose:To evaluate and develop the feasibility of on‐line cone beam CT based planning for emergency and palliative radiotherapy treatments.Methods:Subsequent to phantom studies, a case library of 28 clinical megavoltage cone beam CT (MVCBCT) was built to assess dose‐planning accuracies on MVCBCT for all anatomical sites. A simple emergency treatment plan was created on the MVCBCT and copied to its reference CT. The agreement between the dose distributions of each image pair was evaluated by the mean dose difference of the dose volume and the gamma index of the central 2D axial plane. An array of popular urgent and palliative cases was also evaluated for imaging component clearance and field‐of‐view.Results:The treatment cases were categorized into four groups (head and neck, thorax/spine, pelvis and extremities). Dose distributions for head and neck treatments were predicted accurately in all cases with a gamma index of >95% for 2% and 2 mm criteria. Thoracic spine treatments had a gamma index as low as 60% indicating a need for better uniformity correction and tissue density calibration. Small anatomy changes between CT and MVCBCT could contribute to local errors. Pelvis and sacral spine treatment cases had a gamma index between 90% and 98% for 3%/3 mm criteria. The limited FOV became an issue for large pelvis patients. Imaging clearance was difficult for cases where the tumor was positioned far off midline.Conclusion:The MVCBCT based dose planning and delivery approach is feasible in many treatment cases. Dose distributions for head and neck patients are unrestrictedly predictable. Some FOV restrictions apply to other treatment sites. Lung tissue is most challenging for accurate dose calculations given the current imaging filters and corrections. Additional clinical cases for extremities need to be included in the study to assess the full range of site‐specific planning accuracies.This work is supported by Siemens.

Geometric calibration of a multi-layer LiDAR system and image sensors using plane-based implicit laser parameters for textured 3-D depth reconstruction

The paper proposes the calibration of a LiDAR-camera system that consists of a multi-layer laser rangefinder device and a pair of video cameras. The method calibrates the intrinsic laser parameters and the extrinsic parameters of the integrated LiDAR-camera system. Using a linear form, the dimensionality of the calibration parameter space is reduced in the plane-based least square model. The optimal laser intrinsic parameters can be determined during the optimization of the extrinsic parameters, without being explicitly modeled. However, due to limited FOV of the cameras, the reduced model may lead to a solution that cannot be generalized to the working space. Hence, we use additional scene planes to improve the determination of intrinsic laser parameters. Overall performance is improved if calibration targets can be accurately estimated from the cameras. Results indicate a reduction of 50% in the flatness error is achievable and running time of the process is also decreased.

3D-3D Registration of partial capitate bones using spin-images.

It is often necessary to register partial objects in medical imaging. Due to limited FOV, the entirety of an object cannot always be imaged. This study presents a novel application of an existing registration algorithm to this problem. The spin-image algorithm [1] creates pose-invariant representations of global shape with respect to individual mesh vertices. These 'spin-images,' are then compared for two different poses of the same object to establish correspondences and subsequently determine relative orientation of the poses. In this study, the spin-image algorithm is applied to 4DCT-derived capitate bone surfaces to assess the relative accuracy of registration with various amounts of geometry excluded. The limited longitudinal coverage under the 4DCT technique (38.4mm, [2]), results in partial views of the capitate when imaging wrist motions. This study assesses the ability of the spin-image algorithm to register partial bone surfaces by artificially restricting the capitate geometry available for registration. Under IRB approval, standard static CT and 4DCT scans were obtained on a patient. The capitate was segmented from the static CT and one phase of 4DCT in which the whole bone was available. Spin-image registration was performed between the static and 4DCT. Distal portions of the 4DCT capitate (10-70%) were then removed and registration was repeated. Registration accuracy was evaluated by angular errors and the percentage of sub-resolution fitting. It was determined that 60% of the distal capitate could be omitted without appreciable effect on registration accuracy using the spin-image algorithm (angular error < 1.5 degree, sub-resolution fitting > 98.4%).

Dual Energy Virtual CT Colonoscopy with Dual Source Computed Tomography: Initial Experience

To describe the technique of DE MDCT colonoscopy and to assess its feasibility. 8 patients were scanned with DSCT with a DE scan protocol and dose modulation software. Analysis was performed using dedicated DE software. Prone non-contrast images and DE supine images after contrast injection were obtained. DE colonoscopic images were successfully obtained in 7 patients, but the FOV did not cover all colonic segments in 1 patient, thus resulting in a technical success rate was 87.5 %. Streak artifacts were present in the pelvic region in 2 patients. Virtual unenhanced images and iodine map images were obtained for all patients. In 1 patient a polypoid non-enhancing structure was noted on the iodine map, and conventional colonoscopy revealed impacted stool. Enhancing rectal cancer in 1 patient was correctly shown on the iodine map. Iodine maps helped to differentiate stool fragments/retained fluid by the absence of enhancement when compared to prone CT images. The major advantage of DE colonoscopy was the lack of misregistration. DE MDCT colonoscopy is technically feasible and may obviate the need for unenhanced prone images. It may be possible to perform noncathartic DECT colonoscopy. The major limitation is the limited FOV of tube B. The dose should be optimized to reduce streak artifacts in the pelvic region.