Ultrasound parameters to identify and diagnose carpal tunnel syndrome. A review of the literature.

Numerous works of literature have assessed the use of ultrasound to detect carpal tunnel syndrome, suggesting various techniques and cut-off values. Currently, an effective parameter and cut-off value are still debated. The aim of this review is to determine if these parameters have sufficient rigour to allow their use in clinical practice. Twenty-one studies using sonographic parameters to identify carpal tunnel syndrome in comparison with electrodiagnostic testing (EDx) were selected for review. Methodological differences were found between studies in the use of EDx criteria, scanning and recruitment protocols, with participant biometrics often not reported. Parameters including the cross-sectional area of the median nerve at the level of the pisiform bone in addition to the wrist-to-forearm difference demonstrated high diagnostic utility for set cut-off values. Doppler techniques and mobility are promising, and further research is required to understand the effectiveness of these techniques.

Clinical Utility of Out-of-Hours Chest Radiographs.

PURPOSE: In Australia, radiology services are provided as full 24-hour services, 24-hour urgent out-of-hours (on-call) services, and business hours-only service. The primary purpose of this study was to determine whether out-of-hours (11 PM-7 AM) chest x-ray (CXR) referrals are consistent with out-of-hours service expectations associated with the cost and inconvenience of calling staff in from home. A secondary objective was to determine whether the mobile chest plain film examinations are consistent with expectations of a patient's increased degree of infirmary. METHOD: A retrospective analysis was undertaken over 6 contiguous months for patients referred from the emergency department for CXRs out-of-hours and within standard hours (in-hours). The study population included 436 out-of-hours patients recruited into the investigation cohort and a matched cohort of 438 CXR examinations performed in-hours. The key information gleaned from the study was concordance or discordance between the clinical details relating to the actual referral and the findings of the CXR. RESULTS: The total sample comprised 414 females (47.4%) and 460 males (52.6%). The mean age was 55.3 years, median was 56.5 years, with a range of 0-97 years. The examination type performed was 8.9% mobile compared with 91.1% departmental for the sample. It was found that there was 43.5% prevalence of abnormalities, 27.0% significant abnormalities, and 8.7% clinically significant abnormality. The predictors found for clinically significant abnormalities were increasing patient age (P < .001) and the need for mobile examination (P < .001). Performing the examination out-of-hours did not predict a clinically significant abnormality (P = .491) and similarly, gender did not predict clinically significant abnormality (P = .152). CONCLUSION: The results suggest that similar approaches to referrals for CXRs are applied in-hours and during the out-of-hours period which are inconsistent with the "urgent" philosophy that should accompany an out-of-hours service. Only increasing patient age and the need for a mobile CXR offered predictors of a clinical significant abnormality and this offers an insight into the potential approach to the development of referral guidelines for out-of-hours procedures.

Extraction of the midsagittal plane from morphological neuroimages using the Kullback-Leibler's measure.

A theoretically simple and computationally efficient method to extract the midsagittal plane (MSP) from volumetric neuroimages is presented. The method works in two stages (coarse and fine) and is based on calculation of the Kullback and Leibler's (KL) measure, which characterizes the difference between two distributions. Slices along the sagittal direction are analyzed with respect to a reference slice to determine the coarse MSP. To calculate the final MSP, a local search algorithm is applied. The proposed method does not need any preprocessing, like reformatting, skull stripping, etc. The algorithm was validated quantitatively on 75 MRI datasets of different pulse sequences (T1WI, T2WI, FLAIR and SPGR) and MRA. The angular and distance errors between the calculated MSP and the ground truth lines marked by the expert were calculated. The average distance and angular deviation were 1.25 pixels and 0.63 degrees , respectively. In addition, the algorithm was tested qualitatively on PD, FLAIR, MRA, and CT datasets. To analyze the robustness of the method against rotation, inhomogeneity and noise, the phantom data were used.

A virtual reality simulator for remote interventional radiology: concept and prototype design.

We present a virtual reality simulator to realize interventional radiology (IR) procedures remotely. The simulator contains two subsystems: one at the local site and the other at the remote site. At the local site, the interventional radiologist interacts with a three-dimensional (3-D) vascular model extracted from the patient's data and inserts IR devices through the Motion Tracking Box (MTB), which converts physical motion (translation and rotation) of IR devices into digital signal. This signal is transferred to the Actuator Box (AB) at the remote site that drives the IR devices in the patient. The status of the IR devices is subsequently fed back to the local site and displayed on the vascular model. To prove the concept, the prototype developed employs a physical angiography phantom (mimicking the patient) and its corresponding 3-D digital model. A magnetic tracking system provides information about positioning of the IR devices in the phantom. The initial results are encouraging. The AB controlled remotely drives IR devices with resolution of 0.00288 mm/step in translation and 0.079 deg/step in rotation.

Fast Talairach Transformation for magnetic resonance neuroimages.

We introduce and validate the Fast Talairach Transformation (FTT). FTT is a rapid version of the Talairach transformation (TT) with the modified Talairach landmarks. Landmark identification is fully automatic and done in 3 steps: calculation of midsagittal plane, computing of anterior commissure (AC) and posterior commissure (PC) landmarks, and calculation of cortical landmarks. To perform these steps, we use fast and anatomy-based algorithms employing simple operations. FTT was validated for 215 diversified T1-weighted and spoiled gradient recalled (SPGR) MRI data sets. It calculates the landmarks and warps the Talairach-Tournoux atlas fully automatically in about 5 sec on a standard computer. The average distance errors in landmark localization are (in mm): 1.16 (AC), 1.49 (PC), 0.08 (left), 0.13 (right), 0.48 (anterior), 0.16 (posterior), 0.35 (superior), and 0.52 (inferior). Extensions to FTT by introducing additional landmarks and applying nonlinear warping against the ventricular system are addressed. Application of FTT to other brain atlases of anatomy, function, tracts, cerebrovasculature, and blood supply territories is discussed. FTT may be useful in a clinical setting and research environment: (1) when the TT is used traditionally, (2) when a global brain structure positioning with quick searching and labeling is required, (3) in urgent cases for quick image interpretation (eg, acute stroke), (4) when the difference between nonlinear and piecewise linear warping is negligible, (5) when automatic processing of a large number of cases is required, (6) as an initial atlas-scan alignment before performing nonlinear warping, and (7) as an initial atlas-guided segmentation of brain structures before further local processing.

Quantitative analysis of brain asymmetry by using the divergence measure: normal-pathological brain discrimination.

RATIONALE AND OBJECTIVES: The human brain demonstrates approximate bilateral symmetry of anatomy, function, neurochemical activity, and electrophysiology. This symmetry reflected in radiological images may be affected by pathology. Hence quantitative analysis of brain symmetry may enable the normal and pathological brain discrimination. We propose a method based on the Jeffreys divergence measure (J-divergence), which attempts to quantify "approximate symmetry" and also aids to classify the brain as bilaterally symmetrical/asymmetrical (normal/abnormal). MATERIALS AND METHODS: The dataset included studies of 101 patients (59 without detectable pathologies and 42 with different abnormalities). First, the midsagittal plane is computed for the volume data that divides the head into two hemispheres. The J-divergence is calculated from the density functions of intensities of both the hemispheres. Statistical analysis was conducted to find the best distribution for normal/abnormal datasets. RESULTS: Statistical tests showed that the lognormal distribution best characterizes the values of the J-divergence for both normal and abnormal cases, and the threshold value for the Jeffreys divergence measure to classify the brains with and without detectable pathologies is T = 0.007. The threshold value had a sensitivity of 88.1% and specificity of 90.9%. CONCLUSION: The proposed method is fast and simple to compute. The high sensitivity and specificity indicate the results are encouraging. This method can be used for the initial analysis of data, detection of pathology, classification of dataset as presumably normal/abnormal, and localization of abnormality.

Rapid and automatic localization of the anterior and posterior commissure point landmarks in MR volumetric neuroimages.

RATIONALE AND OBJECTIVE: Accurate identification of the anterior commissure (AC) and posterior commissure (PC) is critical in neuroradiology, functional neurosurgery, human brain mapping, and neuroscience research. Moreover, major stereotactic brain atlases are based on the AC and PC. Our goal is to provide an algorithm for a rapid, robust, accurate and automatic identification of AC and PC. MATERIALS AND METHOD: The method exploits anatomical and radiological properties of AC, PC and surrounding structures, including morphological variability. The localization is done in two stages: coarse and fine. The coarse stage locates the AC and PC on the midsagittal plane by analyzing their relationships with the corpus callosum, fornix, and brainstem. The fine stage refines the AC and PC in a well-defined volume of interest, analyzing locations of lateral and third ventricles, interhemispheric fissure, and massa intermedia. RESULTS: The algorithm was developed using simple operations, like histogramming, thresholding, region growing, 1D projections. It was tested on 94 diversified T1W and SPGR datasets. After the fine stage, 71 (76%) volumes had an error between 0-1 mm for the AC and 55 (59%) for the PC. The mean errors were 1.0 mm (AC) and 1.0 mm (PC). The accuracy has improved twice due to fine stage processing. The algorithm took about 1 second for coarse and 4 seconds for fine processing on P4, 2.5 GHz. CONCLUSION: The use of anatomical and radiological knowledge including variability in algorithm formulation aids in localization of structures more accurately and robustly. This fully automatic algorithm is potentially useful in clinical setting and for research.

Informatics in radiology (infoRAD): multimedia extension of medical imaging resource center teaching files.

A new method has been developed for multimedia enhancement of electronic teaching files created by using the standard protocols and formats offered by the Medical Imaging Resource Center (MIRC) project of the Radiological Society of North America. The typical MIRC electronic teaching file consists of static pages only; with the new method, audio and visual content may be added to the MIRC electronic teaching file so that the entire image interpretation process can be recorded for teaching purposes. With an efficient system for encoding the audiovisual record of on-screen manipulation of radiologic images, the multimedia teaching files generated are small enough to be transmitted via the Internet with acceptable resolution. Students may respond with the addition of new audio and visual content and thereby participate in a discussion about a particular case. MIRC electronic teaching files with multimedia enhancement have the potential to augment the effectiveness of diagnostic radiology teaching.

Geometric modeling of the human normal cerebral arterial system.

We propose an anatomy-based approach for an efficient construction of a three-dimensional human normal cerebral arterial model from segmented and skeletonized angiographic data. The centerline-based model is used for an accurate angiographic data representation. A vascular tree is represented by tubular segments and bifurcations whose construction takes into account vascular anatomy. A bifurcation is defined quantitatively and the algorithm calculating it is given. The centerline is smoothed by means of a sliding average filter. As the vessel radius is sensitive to quality of data as well as accuracy of segmentation and skeletonization, radius outlier removal and radius regression algorithms are formulated and applied. In this way, the approach compensates for some inaccuracies introduced during segmentation and skeletonization. To create the frame of vasculature, we use two different topologies: tubular and B-subdivision based. We also propose a technique to prevent vessel twisting. The analysis of the vascular model is done on a variety of data containing 258 vascular segments and 131 bifurcations. Our approach gives acceptable results from anatomical, topological and geometrical standpoints as well as provides fast visualization and manipulation of the model. The approach is applicable for building a reference cerebrovascular atlas, developing applications for simulation and planning of interventional radiology procedures and vascular surgery, and in education.

Informatics in Radiology (infoRAD): three-dimensional atlas of the brain anatomy and vasculature.

Of the existing atlases of the brain anatomy and cerebrovasculature, none integrates the anatomy and vasculature by providing for direct manipulation of three-dimensional (3D) cerebral models. An atlas-based application was developed in four steps: (a) construction of 3D anatomic models, (b) construction of 3D vascular models, (c) interactive spatial coregistration of the anatomic and vascular models, and (d) development of functionality and a user interface for the application. Three-dimensional anatomic models were imported from an electronic brain atlas database derived from classic print atlases. A novel vascular modeling technique was developed and applied to create a vascular atlas from magnetic resonance angiographic data. The use of 3D polygonal models allows smooth navigation (rotation, zooming, panning) and interactive labeling of anatomic structures and vascular segments. This application enables the user to examine 3D anatomic structures and 3D cerebral vasculature and to gain a better understanding of the relationships between the two. The combined anatomic-vascular atlas is a user-friendly neuroeducational tool that is useful for medical students and neuroscience researchers as well as for educators in preparing teaching materials.

Segmentation of brain from computed tomography head images.

An algorithm to determine the human brain (gray matter (GM) and white matter (WM)) from computed tomography (CT) head volumes with large slice thickness is proposed based on thresholding and brain mask propagation. Firstly, a 2D reference image is chosen to represent the intensity characteristics of the original 3D data set. Secondly, the region of interest of the reference image is determined as the space enclosed by the skull. Fuzzy C-means clustering is employed to determine the threshold for head mask and the low threshold for brain segmentation. The high threshold is calculated as the weighted intensity average of the boundary pixels between bones and GM/WM. Based on the low and high thresholds, the CT volume is binarized, followed by finding the brain candidates through distance criterion. Finally the brain is identified through brain mask propagation using the spatial relationship of neighboring axial slices. The algorithm has been validated against one non-enhanced CT and one enhanced CT volume with pathology.

High resolution CT anatomy of the pulmonary fissures.

RATIONALE AND OBJECTIVES: Pulmonary interlobar fissures are important landmarks for proper identification of normal pulmonary anatomy and evaluation of disease. The purpose of this study was to define the radiologic anatomy of the pulmonary fissures using high resolution computed tomography (HRCT) in a large population. METHODS: HRCT of the lungs from aortic arch to diaphragm was performed in 622 patients, with a slice thickness of 1 mm and slice interval of 10 mm. Major, minor, and accessory fissures were studied for their orientation and completeness. RESULTS: Both major fissures were mostly facing laterally in their upper parts (100% and 89% right and left, respectively). The left major fissure faced medially (69%) while the right major fissure faced lateral (60%) in their lower parts. The right major fissure was more often incomplete (48% as compared with 43% on the left, P < 0.05). Minor fissures were convex superiorly with the apex in the anterolateral part of the base of the upper lobe, and were incomplete in 63% of cases. Azygos, inferior accessory, superior accessory, and left minor fissures were also seen in 1.2%, 8.6%, 4.6%, and 6.1% of the cases, respectively. CONCLUSION: The pulmonary fissures are highly variable and the right major fissure differs considerably from the left. The fissures are often incomplete.

A knowledge-driven algorithm for a rapid and automatic extraction of the human cerebral ventricular system from MR neuroimages.

A knowledge-driven algorithm for a rapid, robust, accurate, and automatic extraction of the human cerebral ventricular system from MR neuroimages is proposed. Its novelty is in combination of neuroanatomy, radiological properties, and variability of the ventricular system with image processing techniques. The ventricular system is divided into six 3D regions: bodies and inferior horns of the lateral ventricles, third ventricle, and fourth ventricle. Within each ventricular region, a 2D region of interest (ROI) is defined based on anatomy and variability. Each ventricular region is further subdivided into subregions, and conditions detecting and preventing leakage into the extra-ventricular space are specified for each subregion. The algorithm extracts the ventricular system by (1) processing each ROI (to calculate its local statistics, determine local intensity ranges of cerebrospinal fluid and gray and white matters, set a seed point within the ROI, grow region directionally in 3D, check anti-leakage conditions, and correct growing if leakage occurred) and (2) connecting all unconnected regions grown by relaxing growing conditions. The algorithm was validated qualitatively on 68 and quantitatively on 38 MRI normal and pathological cases (30 clinical, 20 MGH Brain Repository, and 18 MNI BrainWeb data sets). It runs successfully for normal and pathological cases provided that the slice thickness is less than 3.0 mm in axial and less than 2.0 mm in coronal directions, and the data do not have a high inter-slice intensity variability. The algorithm also works satisfactorily in the presence of up to 9% noise and up to 40% RF inhomogeneity for the BrainWeb data. The running time is less than 5 s on a Pentium 4, 2.0 GHz PC. The best overlap metric between the results of a radiology expert and the algorithm is 0.9879 and the worst 0.9527; the mean and standard deviation of the overlap metric are 0.9723 and 0.01087, respectively.

Knowledge-driven automated extraction of the human cerebral ventricular system from MR images.

This work presents an efficient and automated method to extract the human cerebral ventricular system from MRI driven by anatomic knowledge. The ventricular system is divided into six three-dimensional regions; six ROIs are defined based on the anatomy and literature studies regarding variability of the cerebral ventricular system. The distribution histogram of radiological properties is calculated in each ROI, and the intensity thresholds for extracting each region are automatically determined. Intensity inhomogeneities are accounted for by adjusting intensity threshold to match local situation. The extracting method is based on region-growing and anatomical knowledge, and is designed to include all ventricular parts, even if they appear unconnected on the image. The ventricle extraction method was implemented on the Window platform using C++, and was validated qualitatively on 30 MRI studies with variable parameters.