A Semi-automatic Algorithm for Preliminary Assessment of Labial Gingiva and Alveolar Bone Thickness of Maxillary Anterior Teeth.

PURPOSE: Soft and hard tissue volumes are critical for implant placement and long-term stability. Although the literature has adequately addressed tissue biotypes of Western populations, pertinent information about Asian populations is limited. This study aimed to evaluate the soft and hard tissue profiles of the maxillary anterior teeth of the Taiwanese population using a semi-automatic algorithm. MATERIALS AND METHODS: Cone beam computed tomography images of 11 adults with well-aligned maxillary anterior teeth were overlaid with those of cast models, based on the tooth crowns manually outlined by two independent observers. Each tooth was digitally trisected mesiodistally and apicocoronally. The thicknesses of the labial gingiva and alveolar bone were measured using a customized software program. RESULTS: No obvious difference between the observers was noted regarding the dimension of tooth crowns. The average thicknesses of the labial gingiva, the labial alveolar bone, and the palatal alveolar bone were 1.76 ± 0.11 mm, 1.02 ± 0.12 mm, and 1.80 ± 0.31 mm, respectively, with no significant differences between teeth. All parameters were thicker in the apical region than in the cervical region, and the alveolar bone was thinner in the midlabial region of incisors than in the interproximal regions. The thinnest areas were the midcervical compartment of the right central incisor (0.53 ± 0.33 mm) for the labial gingiva, the midcervical compartment of the right lateral incisor (0.23 ± 0.10 mm) for the labial alveolar bone, and the mesiocervical compartment of the left central incisor (0.33 ± 0.09 mm) for the palatal alveolar bone. CONCLUSION: This study presents an objective and comprehensive methodology for evaluating the soft and hard tissue profiles of maxillary anterior teeth and may be of value for presurgical planning for immediate implant placement. The results suggest that profiles of the Taiwanese subjects are similar to profiles of Western populations.

Determinants of pupil diameters and pupil dynamics in an adult Chinese population.

BACKGROUND: To investigate the determinants of pupil diameter (PD), amplitude of pupil diameter change (PD-change) and speed of pupil constriction (SPC) using video anterior segment optical coherence tomography (AS-OCT) in a population-based sample of Chinese adults. METHODS: Chinese adults aged 40 to 80 years who were free from glaucoma were consecutively recruited from the population-based Singapore Chinese Eye Study. The SPC was measured by AS-OCT videography. Univariate and multivariate analyses were performed to examine the effects of demographic and ocular biometric factors (e.g., axial length [AL], anterior chamber depth [ACD], baseline PD, iris thickness at the area of the dilator muscle [ITDMR], iris area [IA], and iris bowing [IB]) on SPC, PD, and PD-change. RESULTS: A total of 266/302 (89.5 %) AS-OCT videos of eligible eyes were available for analysis. Among these subjects, 64.3 % were women, and the mean age (± standard deviation [SD]) was 56 ± 8.3 years. SPC was not associated with sex. In multiple regression analyses, SPC was independently associated with baseline PD (ß = 0.116, p = 0.006). Baseline PD was independently associated with ACD (ß = 0.341, p < 0.001), TISA 500 (ß = -4.513, p < 0.001), IA (ß = -2.796, p < 0.001), and ITDMR (ß = 6.573, p < 0.001). PD-change was independently associated with ACD (ß = 0.256, p < 0.001), IA (ß = -1.507, p < 0.001), IB (ß = 0.630, p = 0.011), and ITDMR (ß = 3.124, p < 0.001). CONCLUSIONS: Among normal eyes in an adult Chinese population, SPC was associated with larger baseline PD. Larger baseline PD and greater PD change form dark to light were associated with greater ACD, with smaller IA and thicker ITDMR.

Automatic segmentation of the nasal cavity and paranasal sinuses from cone-beam CT images.

PURPOSE: A patient-specific upper airway model is important for clinical, education, and research applications. Cone-beam computed tomography (CBCT) is used for imaging the upper airway but automatic segmentation is limited by noise and the complex anatomy. A multi-step level set segmentation scheme was developed for CBCT volumetric head scans to create a 3D model of the nasal cavity and paranasal sinuses. METHODS: Gaussian mixture model thresholding and morphological operators are first employed to automatically locate the region of interest and to initialize the active contour. Second, the active contour driven by the Kullback-Leibler (K-L) divergence energy in a level set framework to segment the upper airway. The K-L divergence asymmetry is used to directly minimize the K-L divergence energy on the probability density function of the image intensity. Finally, to refine the segmentation result, an anisotropic localized active contour is employed which defines the local area based on shape prior information. The method was tested on ten CBCT data sets. The results were evaluated by the Dice coefficient, the volumetric overlap error (VOE), precision, recall, and F-score and compared with expert manual segmentation and existing methods. RESULTS: The nasal cavity and paranasal sinuses were segmented in CBCT images with a median accuracy of 95.72 % [93.82-96.72 interquartile range] by Dice, 8.73 % [6.79-12.20] by VOE, 94.69 % [93.80-94.97] by precision, 97.73 % [92.70-98.79] by recall, and 95.72 % [93.82-96.69] by F-score. CONCLUSION: Automated CBCT segmentation of the airway and paranasal sinuses was highly accurate in a test sample of clinical scans. The method may be useful in a variety of clinical, education, and research applications.

Adaptation of motor imagery EEG classification model based on tensor decomposition.

OBJECTIVE: Session-to-session nonstationarity is inherent in brain-computer interfaces based on electroencephalography. The objective of this paper is to quantify the mismatch between the training model and test data caused by nonstationarity and to adapt the model towards minimizing the mismatch. APPROACH: We employ a tensor model to estimate the mismatch in a semi-supervised manner, and the estimate is regularized in the discriminative objective function. MAIN RESULTS: The performance of the proposed adaptation method was evaluated on a dataset recorded from 16 subjects performing motor imagery tasks on different days. The classification results validated the advantage of the proposed method in comparison with other regularization-based or spatial filter adaptation approaches. Experimental results also showed that there is a significant correlation between the quantified mismatch and the classification accuracy. SIGNIFICANCE: The proposed method approached the nonstationarity issue from the perspective of data-model mismatch, which is more direct than data variation measurement. The results also demonstrated that the proposed method is effective in enhancing the performance of the feature extraction model.

A pre-operative CT and non-contrast-enhanced C-arm CT registration framework for trans-catheter aortic valve implantation.

Contrast-enhanced C-arm CT is routinely used for intra-operative guidance during the trans-catheter aortic valve implantation (TAVI); however, the requirement for contrast agent injection is not preferable, especially for patients with renal insufficiencies. To address this problem, we present a novel framework for fully automatic registration of pre-operative CT and non-contrast-enhanced C-arm CT. The proposed framework provides an improved workflow and minimizes the usage of contrast agent in the TAVI procedure. Our framework consists of three steps: coarse rigid-body alignment, anatomical knowledge-based prior deformation field generation, and fine deformable registration. We validated the proposed framework on 20 real patient data sets. Based on the 20 data sets, the mesh-to-mesh errors at the aortic root from different methods are measured. Our proposed method significantly outperforms the other state-of-the-art methods. Specifically, we achieve the registration accuracy at 1.76±0.43 mm which is clinically plausible. Quantitative evaluation on real non-contrast enhanced C-arm CT data sets confirms the applicability in the clinical usage. The proposed heart registration method is generic and hence can be easily applied to other cardiac applications.

A level-set based approach for anterior teeth segmentation in cone beam computed tomography images.

Cone beam CT (CBCT) has gained popularity in dentistry for 3D imaging of the jaw bones and teeth due to its high resolution and relatively lower radiation exposure compared to multi-slice CT (MSCT). However, image segmentation of the tooth from CBCT is more complex than from MSCT due to lower bone signal-to-noise. This paper describes a level-set method to extract tooth shape from CBCT images of the head. We improve the variational level set framework with three novel energy terms: (1) dual intensity distribution models to represent the two regions inside and outside the tooth; (2) a robust shape prior to impose a shape constraint on the contour evolution; and (3) using the thickness of the tooth dentine wall as a constraint to avoid leakage and shrinkage problems in the segmentation process. The proposed method was compared with several existing methods and was shown to give improved segmentation accuracy.

Three-dimensional analysis of the change in the curvature of the smiling line following orthodontic treatment in incisor class II division 1 malocclusion.

OBJECTIVES: To investigate the different effects of changes in the occlusal plane, incisors inclination, and maxillary intercanine width on the curvature of the smiling line. MATERIALS: Records of 46 subjects (28 females and 18 males, mean age 16.6 ± 4.2 years) with incisor class II division 1 malocclusions were selected. All subjects had four premolar extractions and were treated with preadjusted edgewise appliances. METHODS: Pre- and post-treatment maxillary dental digital models were virtually aligned via corresponding landmarks to the respective lateral cephalograms. Subsequent two-dimensional superimposition of the aligned cephalograms facilitated the three-dimensional superimposition of the pre- and post-treatment models. This process allowed the quantification of the curvature from a frontal perspective of the models. The change in curvature was then correlated with changes in the cephalometric inclination of the anterior occlusal plane (AOP), functional occlusal plane (FOP), maxillary central incisor (U1), and the intercanine width. RESULTS: Orthodontic correction in this sample resulted in the clockwise rotation of the anterior occlusal plane (5.84 degrees), reduction in proclination of the incisors (-14.39 degrees), increase in intercanine width (2.48mm), and a corresponding increase in the curvature of the smiling line (6.83 degrees). CONCLUSIONS: The change in curvature of the smiling line in these subjects was found to be related more significantly to the magnitude of difference in the inclination between the pre-treatment AOP and FOP than to the change in the inclination of the maxillary incisors. With orthodontic treatment, the smiling line can be correlated with cephalometric data to improve or maintain the curvature.

Automatic transfer function design for medical visualization using visibility distributions and projective color mapping.

Transfer functions play a key role in volume rendering of medical data, but transfer function manipulation is unintuitive and can be time-consuming; achieving an optimal visualization of patient anatomy or pathology is difficult. To overcome this problem, we present a system for automatic transfer function design based on visibility distribution and projective color mapping. Instead of assigning opacity directly based on voxel intensity and gradient magnitude, the opacity transfer function is automatically derived by matching the observed visibility distribution to a target visibility distribution. An automatic color assignment scheme based on projective mapping is proposed to assign colors that allow for the visual discrimination of different structures, while also reflecting the degree of similarity between them. When our method was tested on several medical volumetric datasets, the key structures within the volume were clearly visualized with minimal user intervention.

Discriminative learning of propagation and spatial pattern for motor imagery EEG analysis.

Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance.

Projection-based visual guidance for robot-aided RF needle insertion.

PURPOSE: The use of projector-based augmented reality (AR) in surgery may enable surgeons to directly view anatomical models and surgical data from the patient's surface (skin). It has the advantages of a consistent viewing focus on the patient, an extended field of view and augmented interaction. This paper presents an AR guidance mechanism with a projector-camera system to provide the surgeon with direct visual feedback for supervision of robotic needle insertion in radiofrequency (RF) ablation treatment. METHODS: The registration of target organ models to specific positions on the patient body is performed using a surface-matching algorithm and point-based registration. An algorithm based on the extended Kalman filter and spatial transformation is used to intraoperatively compute the virtual needle's depth in the patient's body for AR display. RESULTS: Experiments of this AR system on a mannequin were conducted to evaluate AR visualization and accuracy of virtual RF needle insertion. The average accuracy of 1.86 mm for virtual needle insertion met the clinical requirement of 2 mm or better. The feasibility of augmented interaction with a surgical robot using the proposed open AR interface with active visual feedback was demonstrated. CONCLUSIONS: The experimental results demonstrate that this guidance system is effective in assisting a surgeon to perform a robot-assisted radiofrequency ablation procedure. The novelty of the work lies in establishing a navigational procedure for percutaneous surgical augmented intervention integrating a projection-based AR guidance and robotic implementation for surgical needle insertion.

Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long- and short-axis information.

Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR) images is difficult due to the intensity heterogeneity arising from accumulation of contrast agent in infarcted myocardium. In this paper, we present a comprehensive framework for automatic 3D segmentation of the LV in LGE CMR images. Given myocardial contours in cine images as a priori knowledge, the framework initially propagates the a priori segmentation from cine to LGE images via 2D translational registration. Two meshes representing respectively endocardial and epicardial surfaces are then constructed with the propagated contours. After construction, the two meshes are deformed towards the myocardial edge points detected in both short-axis and long-axis LGE images in a unified 3D coordinate system. Taking into account the intensity characteristics of the LV in LGE images, we propose a novel parametric model of the LV for consistent myocardial edge points detection regardless of pathological status of the myocardium (infarcted or healthy) and of the type of the LGE images (short-axis or long-axis). We have evaluated the proposed framework with 21 sets of real patient and four sets of simulated phantom data. Both distance- and region-based performance metrics confirm the observation that the framework can generate accurate and reliable results for myocardial segmentation of LGE images. We have also tested the robustness of the framework with respect to varied a priori segmentation in both practical and simulated settings. Experimental results show that the proposed framework can greatly compensate variations in the given a priori knowledge and consistently produce accurate segmentations.

A two-stage rule-constrained seedless region growing approach for mandibular body segmentation in MRI.

PURPOSE: Extraction of the mandible from 3D volumetric images is frequently required for surgical planning and evaluation. Image segmentation from MRI is more complex than CT due to lower bony signal-to-noise. An automated method to extract the human mandible body shape from magnetic resonance (MR) images of the head was developed and tested. METHODS: Anonymous MR images data sets of the head from 12 subjects were subjected to a two-stage rule-constrained region growing approach to derive the shape of the body of the human mandible. An initial thresholding technique was applied followed by a 3D seedless region growing algorithm to detect a large portion of the trabecular bone (TB) regions of the mandible. This stage is followed with a rule-constrained 2D segmentation of each MR axial slice to merge the remaining portions of the TB regions with lower intensity levels. The two-stage approach was replicated to detect the cortical bone (CB) regions of the mandibular body. The TB and CB regions detected from the preceding steps were merged and subjected to a series of morphological processes for completion of the mandibular body region definition. Comparisons of the accuracy of segmentation between the two-stage approach, conventional region growing method, 3D level set method, and manual segmentation were made with Jaccard index, Dice index, and mean surface distance (MSD). RESULTS: The mean accuracy of the proposed method is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The mean accuracy of CRG is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The mean accuracy of the 3D level set method is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The proposed method shows improvement in accuracy over CRG and 3D level set. CONCLUSION: Accurate segmentation of the body of the human mandible from MR images is achieved with the proposed two-stage rule-constrained seedless region growing approach. The accuracy achieved with the two-stage approach is higher than CRG and 3D level set.

Analysis of anterior segment dynamics using anterior segment optical coherence tomography before and after laser peripheral iridotomy.

OBJECTIVE: To evaluate changes in the speed of pupil constriction and in anterior segment parameters after laser peripheral iridotomy (LPI) in patients with angle closure using anterior segment optical coherence tomography. METHODS: In this prospective observational study, videos of pupil and anterior segment changes in response to illumination were captured with real-time video recording using anterior segment optical coherence tomography and were analyzed frame by frame before and after LPI. Customized software was used to measure the speed of pupil constriction and changes in anterior chamber depth and anterior chamber area, as well as iris thickness at 750 µm from the scleral spur, at the sphincter muscle region (0.75 mm from the pupillary margin), and at the mid-iris location (half the distance between the scleral spur and the pupillary margin). Pupil diameter, angle opening distance, and trabecular-iris space area at 500 µm from the scleral spur were determined. The speed of pupil constriction was defined as the rate of pupil diameter change in response to illumination. RESULTS: Twenty-nine patients were included. Most were Chinese (26 of 29 [90%]) and female (18 of 29 [62%]). The anterior chamber area, angle opening distance at 500 µm from the scleral spur, and trabecular-iris space area at 500 µm from the scleral spur were significantly higher after LPI (P < .001). A significant increase was observed in the speed of pupil constriction after LPI (P < .005). In response to illumination, the rate of change in iris thickness at the sphincter muscle region and at 750 µm from the scleral spur was faster after LPI (P < .05). Similarly, an increase was observed in the speed of change of angle-opening distance at 500 µm from the scleral spur in response to illumination after LPI (P < .05). CONCLUSIONS: In patients with angle closure, changes in dynamic iridopupillary behavior are observed after LPI. The speed of pupillary constriction is faster after LPI.

A comprehensive 3-D framework for automatic quantification of late gadolinium enhanced cardiac magnetic resonance images.

Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) can directly visualize nonviable myocardium with hyperenhanced intensities with respect to normal myocardium. For heart attack patients, it is crucial to facilitate the decision of appropriate therapy by analyzing and quantifying their LGE CMR images. To achieve accurate quantification, LGE CMR images need to be processed in two steps: segmentation of the myocardium followed by classification of infarcts within the segmented myocardium. However, automatic segmentation is difficult usually due to the intensity heterogeneity of the myocardium and intensity similarity between the infarcts and blood pool. Besides, the slices of an LGE CMR dataset often suffer from spatial and intensity distortions, causing further difficulties in segmentation and classification. In this paper, we present a comprehensive 3-D framework for automatic quantification of LGE CMR images. In this framework, myocardium is segmented with a novel method that deforms coupled endocardial and epicardial meshes and combines information in both short- and long-axis slices, while infarcts are classified with a graph-cut algorithm incorporating intensity and spatial information. Moreover, both spatial and intensity distortions are effectively corrected with specially designed countermeasures. Experiments with 20 sets of real patient data show visually good segmentation and classification results that are quantitatively in strong agreement with those manually obtained by experts.

Osteoporosis screening using areal bone mineral density estimation from diagnostic CT images.

RATIONALE AND OBJECTIVES: A reliable and cost-effective method for osteoporosis screening is important in addressing the increase in osteoporotic fractures due to aging populations. Diagnostic computed tomographic (dCT) images may contain densitometric information useful for osteoporosis screening. The aim of this study was to investigate the relationship between areal bone mineral density (aBMD) and volumetric information on dCT imaging and its suitability for building an osteopenia screening system. The goal of this system is to estimate aBMD and predict bone disease condition on the basis of dCT images of the lumbar spine. MATERIALS AND METHODS: Dual-energy x-ray absorptiometry (DXA) aBMD and computed tomographic (CT) images were obtained from 44 male patients (mean age, 60 years). An aBMD from CT images (aBMD(CT)) was computed from the CT volume using established relationships of Hounsfield units to bone density and used to estimate DXA-derived aBMD (aBMD(DxA)). Estimated aBMD(CT) was then applied to diagnose osteopenia of the lumbar spine using statistical methods. RESULTS: For the estimation of aBMD(DxA) from aBMD(CT), the proposed approach yielded a high correlation factor of r = 0.852, with a root mean square error of 0.0884 g/cm(2). The correlation was strongest when every slice in the dCT volume and both trabecular and cortical bone components were used. The classifier achieved an overall classification accuracy of 80.1% and an area under the receiver-operating characteristic curve of 0.894. CONCLUSIONS: This clinical study demonstrates that aBMD(DxA) can be determined from routine CT data. Estimated aBMD(DxA) can be extended to form a dCT imaging-based opportunistic screening system for the detection and management of osteopenia.

In vivo analysis of vectors involved in pupil constriction in Chinese subjects with angle closure.

PURPOSE: To evaluate the acceleration of pupil constriction (APC) in response to illumination using video anterior segment optical coherence tomography (AS-OCT) in angle closure and normal eyes. METHODS: This was an observational study of 342 Chinese subjects. Iris and angle changes in response to illumination were captured with real-time video recordings of AS-OCT and analyzed frame by frame. APC was calculated using a quadratic function, fitting pupil diameter to a time series. APC was divided into two vector components: acceleration of pupil block (APB) acting perpendicular to the lens surface and acceleration of iris stretch (AIS) acting toward the iris root. RESULTS: Of 342 eligible patients, 306 (89.5%) were available for analysis; of whom 136 (41.7%) had angle closure. After adjusting for age, sex, baseline pupil diameter, and iris thickness, APC was significantly lower in angle closure eyes (0.61 mm/s(2)) than in open-angle eyes (0.90 mm/s(2)) (P < 0.0001) as was AIS (0.58 mm/s(2) vs. 0.89 mm/s(2)) (P < 0.001). APB was significantly higher in angle closure eyes compared to open-angle eyes (0.14 mm/s(2) vs. 0.09 mm/s(2)) (P < 0.001). After adjusting for age and sex in logistic regression, the magnitude and direction of all vector parameters were significantly associated with presence of angle closure. CONCLUSIONS: Angle closure eyes have smaller AIS and larger APB in response to illumination as measured using AS-OCT videography. This shows that, comparatively, the iris of angle closure eyes stretches less and develops a more convex configuration in response to illumination.

A general strategy for anisotropic diffusion in MR image denoising and enhancement.

Anisotropic diffusion (AD) has proven to be very effective in the denoising of magnetic resonance (MR) images. The result of AD filtering is highly dependent on several parameters, especially the conductance parameter. However, there is no automatic method to select the optimal parameter values. This paper presents a general strategy for AD filtering of MR images using an automatic parameter selection method. The basic idea is to estimate the parameters through an optimization step on a synthetic image model, which is different from traditional analytical methods. This approach can be easily applied to more sophisticated diffusion models for better denoising results. We conducted a systematic study of parameter selection for the AD filter, including the dynamic parameter decreasing rate, the parameter selection range for different noise levels and the influence of the image contrast on parameter selection. The proposed approach was validated using both simulated and real MR images. The model image generated using our approach was shown to be highly suitable for the purpose of parameter optimization. The results confirm that our method outperforms most state-of-the-art methods in both quantitative measurement and visual evaluation. By testing on real images with different noise levels, we demonstrated that our method is sufficiently general to be applied to a variety of MR images.

Pupil dynamics in Chinese subjects with angle closure.

OBJECTIVE: To evaluate the speed of pupil constriction (SPC) in response to a standardized change in illumination from dark to light using anterior segment optical coherence tomography (AS-OCT) in eyes with closed angles, compared to those with open angles. METHODS: This was a prospective, comparative, observational study. Study subjects with primary angle-closure glaucoma (PACG) and/or primary angle-closure (PAC) were recruited along with a group of normal as controls. Videos of iris and anterior segment changes in response to illumination were captured with real-time video recording from AS-OCT and analysed frame by frame, beginning from a dilated to a constricted pupil. Customized software was used to measure speed of iris constriction, anterior chamber depth (ACD), anterior chamber width (ACW), iris thickness, and pupil diameter (PD). SPC was defined as the rate of pupil diameter change in response to illumination. RESULTS: One hundred and sixty three Chinese subjects were recruited in this study. A total of 137 of 163 eligible videos (82.5%) were available for analysis, comprising 87 subjects with closed angles (all had undergone laser peripheral iridotomies before) and 50 with open angles. SPC was less in eyes with closed angles (1.22 mm/sec vs 1.56 mm/sec, p<0.001), after adjusting for age and ACW, and was positively correlated with axial length, ACD, anterior chamber angle status, PD in the dark, and iris thickness in the dark, (all r>0.2, all p<0.05). In multivariate analysis, SPC was independently associated with anterior chamber angle status (closed angle vs open angle) (ß=0.276, p=0.016) and PD in dark (ß= 0.129, p=0.009), after adjusting for age, ACW, ACD, iris thickness and vertical cup-to-disc ratio. CONCLUSIONS: Compared to those with open angles, eyes with closed angles in Chinese subjects have slower speed of pupil constriction in response to dark-light change, even after adjusting for biometric factors associated with angle closure. The results suggest that differences in irido-pupillary dynamics may play a role in the pathogenesis of angle closure.

Modeling shear modulus distribution in magnetic resonance elastography with piecewise constant level sets.

Magnetic resonance elastography (MRE) is designed for imaging the mechanical properties of soft tissues. However, the interpretation of shear modulus distribution is often confusing and cumbersome. For reliable evaluation, a common practice is to specify the regions of interest and consider regional elasticity. Such an experience-dependent protocol is susceptible to intrapersonal and interpersonal variability. In this study we propose to remodel shear modulus distribution with piecewise constant level sets by referring to the corresponding magnitude image. Optimal segmentation and registration are achieved by a new hybrid level set model comprised of alternating global and local region competitions. Experimental results on the simulated MRE data sets show that the mean error of elasticity reconstruction is 11.33% for local frequency estimation and 18.87% for algebraic inversion of differential equation. Piecewise constant level set modeling is effective to improve the quality of shear modulus distribution, and facilitates MRE analysis and interpretation.

Myocardial segmentation of late gadolinium enhanced MR images by propagation of contours from cine MR images.

Automatic segmentation of myocardium in Late Gadolinium Enhanced (LGE) Cardiac MR (CMR) images is often difficult due to the intensity heterogeneity resulting from accumulation of contrast agent in infarcted areas. In this paper, we propose an automatic segmentation framework that fully utilizes shared information between corresponding cine and LGE images of a same patient. Given myocardial contours in cine CMR images, the proposed framework achieves accurate segmentation of LGE CMR images in a coarse-to-fine manner. Affine registration is first performed between the corresponding cine and LGE image pair, followed by nonrigid registration, and finally local deformation of myocardial contours driven by forces derived from local features of the LGE image. Experimental results on real patient data with expert outlined ground truth show that the proposed framework can generate accurate and reliable results for myocardial segmentation of LGE CMR images.