Parallel Line Sign-A New MRI Sign Associated With Chronic Sacroiliitis: Prevalence, Characteristics, and Associations.

Purpose: The purpose is to demonstrate the existence of the parallel line sign (PLS), a dark line parallel to the sacroiliac joint (SIJ), and determine its prevalence, characteristics, and associations. Methods: 200 consecutive SIJ MRIs referred by rheumatologists were retrospectively reviewed for the presence of the PLS. Presence and extent of imaging features of sacroiliitis (bone marrow edema, fatty infiltration, erosions, sclerosis, and ankylosis) were evaluated. Results: Prevalence of PLS was 11.5% (23/200), with 9 subjects having bilateral PLS, resulting in 32 SIJs showing a PLS. Every PLS involved the synovial portion of the SIJ, and almost all (31/32, 96.9%) involved the iliac (rather than sacral) side of the SIJ. Every PLS occurred with at least one established imaging feature of sacroiliitis. Presence of a PLS was associated with higher prevalence of erosions (78.3% vs 36.7% in those without PLS, P < .001), greater extent of SIJ involvement by erosions (3.6 ± 1.3 vs 2.3 ± 1.1 quadrants of the SIJ involved, P < .001), and higher density of erosions per centimeter (88.9% vs 46.2% with >2 erosions/cm, P = .001). There was higher prevalence of bone marrow edema, fatty infiltration, and sclerosis in those with PLS compared to those without PLS (P = .001, P < .001, and P = .006, respectively). Extent of involvement by any of these features was not significantly different between the two groups (P = .22, P = .16, and P = .46, respectively). Conclusions: The PLS is associated with imaging features of chronic sacroiliitis, especially erosions. Knowledge of the existence of the PLS may help avoid misdiagnosis of an insufficiency fracture and increase confidence in the diagnosis of sacroiliitis.

Augmented reality simulator for ultrasound-guided percutaneous renal access.

PURPOSE: Traditional training for percutaneous renal access (PCA) relies on apprenticeship, which raises concerns about patient safety, limited training opportunities, and inconsistent quality of feedback. In this study, we proposed the development of a novel augmented reality (AR) simulator for ultrasound (US)-guided PCA and evaluated its validity and efficacy as a teaching tool. METHODS: Our AR simulator allows the user to practice PCA on a silicone phantom using a tracked needle and US probe emulator under the guidance of simulated US on a tablet screen. 6 Expert and 24 novice participants were recruited to evaluate the efficacy of our simulator. RESULTS: Experts highly rated the realism and usefulness of our simulator, reflected by the average face validity score of 4.39 and content validity score of 4.53 on a 5-point Likert scale. Comparisons with a Mann-Whitney U test revealed significant differences [Formula: see text] in performances between the experts and novices on 6 out of 7 evaluation metrics, demonstrating strong construct validity. Furthermore, a paired T-test indicated significant performance improvements [Formula: see text] of the novices in both objective and subjective evaluation after training with our simulator. CONCLUSION: Our cost-effective, flexible, and easily customizable AR training simulator can provide opportunities for trainees to acquire basic skills of US-guided PCA in a safe and stress-free environment. The effectiveness of our simulator is demonstrated through strong face, content, and construct validity, indicating its value as a novel training tool.

Injection of Saline Into the Biopsy Tract and Rapid Patient Rollover Decreases Pneumothorax Size Following Computed Tomography-Guided Transthoracic Needle Biopsy.

PURPOSE: To determine if saline tract injection and rapid patient rollover following computed tomography (CT)-guided transthoracic needle biopsy (TTNB) affects pneumothorax incidence and size. METHODS: A retrospective cohort design was used to compare 278 patients who underwent post-biopsy saline injection and rapid rollover so that the biopsy site was dependent (N = 180) to a control group with routine post-biopsy care (N = 98). Post-procedure radiographs and CT were assessed for presence and size of pneumothorax, as well as requirement for chest tube placement. RESULTS: Pneumothorax size as estimated on post-procedure CT was 3.33% in the treatment group and 6.63% in the control group (P < .05). There was also a reduction in chest tube placements in the treatment group (3.9% vs 10%, P < .05). On post-procedure radiographs, pneumothorax rates were 20% in the treatment group, and 25% in the control group (P > .05). CONCLUSION: Saline injection with rapid patient rollover following TTNB significantly decreased pneumothorax size and chest tube placement but not incidence.

Spine labeling in MRI via regularized distribution matching.

PURPOSE: This study investigates an efficient (nearly real-time) two-stage spine labeling algorithm that removes the need for an external training while being applicable to different types of MRI data and acquisition protocols. METHODS: Based solely on the image being labeled (i.e., we do not use training data), the first stage aims at detecting potential vertebra candidates following the optimization of a functional containing two terms: (i) a distribution-matching term that encodes contextual information about the vertebrae via a density model learned from a very simple user input, which amounts to a point (mouse click) on a predefined vertebra; and (ii) a regularization constraint, which penalizes isolated candidates in the solution. The second stage removes false positives and identifies all vertebrae and discs by optimizing a geometric constraint, which embeds generic anatomical information on the interconnections between neighboring structures. Based on generic knowledge, our geometric constraint does not require external training. RESULTS: We performed quantitative evaluations of the algorithm over a data set of 90 mid-sagittal MRI images of the lumbar spine acquired from 45 different subjects. To assess the flexibility of the algorithm, we used both T1- and T2-weighted images for each subject. A total of 990 structures were automatically detected/labeled and compared to ground-truth annotations by an expert. On the T2-weighted data, we obtained an accuracy of 91.6% for the vertebrae and 89.2% for the discs. On the T1-weighted data, we obtained an accuracy of 90.7% for the vertebrae and 88.1% for the discs. CONCLUSION: Our algorithm removes the need for external training while being applicable to different types of MRI data and acquisition protocols. Based on the current testing data, a subject-specific model density and generic anatomical information, our method can achieve competitive performances when applied to T1- and T2-weighted MRI images.

Spine labeling in axial magnetic resonance imaging via integral kernels.

This study investigates a fast integral-kernel algorithm for classifying (labeling) the vertebra and disc structures in axial magnetic resonance images (MRI). The method is based on a hierarchy of feature levels, where pixel classifications via non-linear probability product kernels (PPKs) are followed by classifications of 2D slices, individual 3D structures and groups of 3D structures. The algorithm further embeds geometric priors based on anatomical measurements of the spine. Our classifier requires evaluations of computationally expensive integrals at each pixel, and direct evaluations of such integrals would be prohibitively time consuming. We propose an efficient computation of kernel density estimates and PPK evaluations for large images and arbitrary local window sizes via integral kernels. Our method requires a single user click for a whole 3D MRI volume, runs nearly in real-time, and does not require an intensive external training. Comprehensive evaluations over T1-weighted axial lumbar spine data sets from 32 patients demonstrate a competitive structure classification accuracy of 99%, along with a 2D slice classification accuracy of 88%. To the best of our knowledge, such a structure classification accuracy has not been reached by the existing spine labeling algorithms. Furthermore, we believe our work is the first to use integral kernels in the context of medical images.

TRIC: trust region for invariant compactness and its application to abdominal aorta segmentation.

This study investigates segmentation with a novel invariant compactness constraint. The proposed prior is a high-order fractional term, which is not directly amenable to powerful optimizers. We derive first-order Gateâux derivative approximations of our compactness term and adopt an iterative trust region paradigm by splitting our problem into constrained sub-problems, each solving the approximation globally via a Lagrangian formulation and a graph cut. We apply our algorithm to the challenging task of abdominal aorta segmentation in 3D MRI volumes, and report quantitative evaluations over 30 subjects, which demonstrate that the results correlate well with independent manual segmentations. We further show the use of our method in several other medical applications and demonstrate that, in comparison to a standard level-set optimization, our algorithm is one order of magnitude faster.

Gradient competition anisotropy for centerline extraction and segmentation of spinal cords.

Centerline extraction and segmentation of the spinal cord--an intensity varying and elliptical curvilinear structure under strong neighboring disturbance are extremely challenging. This study proposes the gradient competition anisotropy technique to perform spinal cord centerline extraction and segmentation. The contribution of the proposed method is threefold--1) The gradient competition descriptor compares the image gradient obtained at different detection scales to suppress neighboring disturbance. It reliably recognizes the curvilinearity and orientations of elliptical curvilinear objects. 2) The orientation coherence anisotropy analyzes the detection responses offered by the gradient competition descriptor. It enforces structure orientation consistency to sustain strong disturbance introduced by high contrast neighboring objects to perform centerline extraction. 3) The intensity coherence segmentation quantifies the intensity difference between the centerline and the voxels in the vicinity of the centerline. It effectively removes the object intensity variation along the structure to accurately delineate the target structure. They constitute the gradient competition anisotropy method which can robustly and accurately detect the centerline and boundary of the spinal cord. It is validated and compared using 25 clinical datasets. It is demonstrated that the proposed method well suits the applications of spinal cord centerline extraction and segmentation.

Scenes from the past: multidetector CT of Egyptian mummies of the Redpath Museum.

As a nondestructive method of historical and anthropologic inquiry, imaging has played an important role in mummy studies over the past several decades. Recent technologic advances have made multidetector computed tomography (CT) an especially useful means for deepening the present understanding of ancient cultures by examining preserved human remains. In April 2011, three ancient Egyptian human mummies from the Redpath Museum of McGill University were examined with 320-section multidetector CT as part of the IMPACT Radiological Mummy Database project headquartered at the University of Western Ontario. Whole-body scanning was performed with a section thickness of 0.5 mm and a peak voltage of 120 kVp, and the raw CT datasets were postprocessed by using smooth body and high-resolution bone convolution filters. Two of the mummies were scanned at different energy levels (80 and 135 keV). The high-resolution CT scans revealed the details of mummification and allowed observations about the socioeconomic and health status of the human subjects based on both the mummification technique used and the appearance of the remains, particularly the bones and teeth. The paleopathologic information obtained from the scans confirmed some findings in studies performed in the same mummies in the late 19th and 20th centuries. The CT scans also demonstrated a high degree of variability in Egyptian mortuary practice, variability that is not generally recognized in the literature. Unusual features that were observed included a relatively uncommon retained heart in mummy RM2718, retained lungs in a mummy from which the heart had been extracted (RM2720), and a cartonnage plaque placed over the left abdomen of a mummy that had been eviscerated transperineally (RM2717).

Vertebral body segmentation in MRI via convex relaxation and distribution matching.

We state vertebral body (VB) segmentation in MRI as a distribution-matching problem, and propose a convex-relaxation solution which is amenable to parallel computations. The proposed algorithm does not require a complex learning from a large manually-built training set, as is the case of the existing methods. From a very simple user input, which amounts to only three points for a whole volume, we compute a multi-dimensional model distribution of features that encode contextual information about the VBs. Then, we optimize a functional containing (1) a feature-based constraint which evaluates a similarity between distributions, and (2) a total-variation constraint which favors smooth surfaces. Our formulation leads to a challenging problem which is not directly amenable to convex-optimization techniques. To obtain a solution efficiently, we split the problem into a sequence of sub-problems, each can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Our parallelized implementation on a graphics processing unit (GPU) demonstrates that the proposed solution can bring a substantial speed-up of more than 30 times for a typical 3D spine MRI volume. We report quantitative performance evaluations over 15 subjects, and demonstrate that the results correlate well with independent manual segmentations.

A parameterization of deformation fields for diffeomorphic image registration and its application to myocardial delineation.

This study investigates a new parameterization of deformation fields for image registration. Instead of standard displacements, this parameterization describes a deformation field with its transformation Jacobian and curl of end velocity field. It has two important features which make it appealing to image registration: 1) it relaxes the need of an explicit regularization term and the corresponding ad hoc weight in the cost functional; 2) explicit constraints on transformation Jacobian such as topology preserving and incompressibility constraints are straightforward to impose in a unified framework. In addition, this parameterization naturally describes a deformation field in terms of radial and rotational components, making it especially suited for processing cardiac data. We formulate diffeomorphic image registration as a constrained optimization problem which we solve with a step-then-correct strategy. The effectiveness of the algorithm is demonstrated with several examples and a comprehensive evaluation of myocardial delineation over 120 short-axis cardiac cine MRIs acquired from 20 subjects. It shows competitive performance in comparison to two recent segmentation based approaches.