Automated model-based vertebra detection,identification, and segmentation in CT images

For many orthopaedic, neurological, and oncological applications, an exact segmentation of the vertebral column including an identification of each vertebra is essential. However, although bony structures show high contrast in CT images, the segmentation and labelling of individual vertebrae is challenging.In this paper, we present a comprehensive solution for automatically detecting, identifying, and segmenting vertebrae in CT images. A framework has been designed that takes an arbitrary CT image, e.g., head-neck, thorax, lumbar, or whole spine, as input and provides a segmentation in form of labelled triangulated vertebra surface models. In order to obtain a robust processing chain, profound prior knowledge is applied through the use of various kinds of models covering shape, gradient, and appearance information. The framework has been tested on 64 CT images even including pathologies. In 56 cases, it was successfully applied resulting in a final mean point-to-surface segmentation error of 1.12 ± 1.04 mm.One key issue is a reliable identification of vertebrae. For a single vertebra, we achieve an identification success of more than 70%. Increasing the number of available vertebrae leads to an increase in the identification rate reaching 100% if 16 or more vertebrae are shown in the image.     

Clavicle segmentation in chest radiographs

Automated delineation of anatomical structures in chest radiographs is difficult due to superimposition of multiple structures. In this work an automated technique to segment the clavicles in posterior-anterior chest radiographs is presented in which three methods are combined. Pixel classification is applied in two stages and separately for the interior, the border and the head of the clavicle. This is used as input for active shape model segmentation. Finally dynamic programming is employed with an optimized cost function that combines appearance information of the interior of the clavicle, the border, the head and shape information derived from the active shape model. The method is compared with a number of previously described methods and with independent human observers on a large database. This database contains both normal and abnormal images and will be made publicly available. The mean contour distance of the proposed method on 249 test images is 1.1 ± 1.6 mm and the intersection over union is 0.86 ± 0.10.     

Segmentation by retrieval with guided random walks: Application to left ventricle segmentation in MRI

In this paper, a new segmentation framework with prior knowledge is proposed and applied to the left ventricles in cardiac Cine MRI sequences. We introduce a new formulation of the random walks method, coined as guided random walks, in which prior knowledge is integrated seamlessly. In comparison with existing approaches that incorporate statistical shape models, our method does not extract any principal model of the shape or appearance of the left ventricle. Instead, segmentation is accompanied by retrieving the closest subject in the database that guides the segmentation the best. Using this techniques, rare cases can also effectively exploit prior knowledge from few samples in training set. These cases are usually disregarded in statistical shape models as they are outnumbered by frequent cases (effect of class population). In the worst-case scenario, if there is no matching case in the database to guide the segmentation, performance of the proposed method reaches to the conventional random walks, which is shown to be accurate if sufficient number of seeds is provided. There is a fast solution to the proposed guided random walks by using sparse linear matrix operations and the whole framework can be seamlessly implemented in a parallel architecture. The method has been validated on a comprehensive clinical dataset of 3D+t short axis MR images of 104 subjects from 5 categories (normal, dilated left ventricle, ventricular hypertrophy, recent myocardial infarction, and heart failure). The average segmentation errors were found to be 1.54 mm for the endocardium and 1.48 mm for the epicardium. The method was validated by measuring different algorithmic and physiologic indices and quantified with manual segmentation ground truths, provided by a cardiologist.     

Model driven quantification of left ventricular function from sparse single-beat 3D echocardiography

This paper presents a novel model based segmentation technique for quantification of left ventricular (LV) function from sparse single-beat 3D echocardiographic data acquired with a fast rotating ultrasound (FRU) transducer. This transducer captures cardiac anatomy in a sparse set of radially sampled, curved cross-sections within a single cardiac cycle. The method employs a 3D Active Shape Model of the left ventricle (LV) in combination with local appearance models as prior knowledge to steer the segmentation. A set of local appearance patches generate the model update points for fitting the model to the LV in the curved FRU cross-sections. Updates are then propagated over the dense 3D model mesh to overcome correspondence problems due to the data sparsity, whereas the 3D Active Shape Model serves to retain the plausibility of the generated shape.Leave-one-out cross-validation was carried out on single-beat FRU data from 28 patients suffering from various cardiac pathologies. Detection succeeded in 24 cases, and failed in 4 cases due to large dropouts in echo signal. For the successful 24 cases, detection yielded Point to Point errors of 3.1 ± 1.1 mm, Point to Surface errors of 1.7 ± 0.9 mm and an EF error of 7.3 ± 4.9%. Comparison of fitting on single-beat versus denser multi-beat data showed a similar performance for both types of data irrespective of frame angles of the intersections. Robustness tests with respect to different model initializations showed acceptable performance for initial positions within a range of 26 mm for displacement and 12° for orientation. Furthermore, a comparison study between the proposed method and global LV function measured from MR studies of the same patients showed an underestimation of volumes estimated from echocardiographic data compared to MR derived volumes, similar to other results reported in literature. All experiments demonstrate that the proposed method combines robustness with respect to initialization with an acceptable accuracy, while using sparse single-beat FRU data.     

A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo

The development of sophisticated and high throughput whole body small animal imaging technologies has created a need for improved image analysis and increased automation. The registration of a digital mouse atlas to individual images is a prerequisite for automated organ segmentation and uptake quantification. This paper presents a fully-automatic method for registering a statistical mouse atlas with individual subjects based on an anterior–posterior X-ray projection and a lateral optical photo of the mouse silhouette. The mouse atlas was trained as a statistical shape model based on 83 organ-segmented micro-CT images. For registration, a hierarchical approach is applied which first registers high contrast organs, and then estimates low contrast organs based on the registered high contrast organs. To register the high contrast organs, a 2D-registration-back-projection strategy is used that deforms the 3D atlas based on the 2D registrations of the atlas projections. For validation, this method was evaluated using 55 subjects of preclinical mouse studies. The results showed that this method can compensate for moderate variations of animal postures and organ anatomy. Two different metrics, the Dice coefficient and the average surface distance, were used to assess the registration accuracy of major organs. The Dice coefficients vary from 0.31 ± 0.16 for the spleen to 0.88 ± 0.03 for the whole body, and the average surface distance varies from 0.54 ± 0.06 mm for the lungs to 0.85 ± 0.10 mm for the skin. The method was compared with a direct 3D deformation optimization (without 2D-registration-back-projection) and a single-subject atlas registration (instead of using the statistical atlas). The comparison revealed that the 2D-registration-back-projection strategy significantly improved the registration accuracy, and the use of the statistical mouse atlas led to more plausible organ shapes than the single-subject atlas. This method was also tested with shoulder xenograft tumor-bearing mice, and the results showed that the registration accuracy of most organs was not significantly affected by the presence of shoulder tumors, except for the lungs and the spleen.     

Optimizing boundary detection via Simulated Search with applications to multi-modal heart segmentation

Segmentation of medical images can be achieved with the help of model-based algorithms. Reliable boundary detection is a crucial component to obtain robust and accurate segmentation results and to enable full automation. This is especially important if the anatomy being segmented is too variable to initialize a mean shape model such that all surface regions are close to the desired contours. Several boundary detection algorithms are widely used in the literature. Most use some trained image appearance model to characterize and detect the desired boundaries. Although parameters of the boundary detection can vary over the model surface and are trained on images, their performance (i.e., accuracy and reliability of boundary detection) can only be assessed as an integral part of the entire segmentation algorithm. In particular, assessment of boundary detection cannot be done locally and independently on model parameterization and internal energies controlling geometric model properties.In this paper, we propose a new method for the local assessment of boundary detection called Simulated Search. This method takes any boundary detection function and evaluates its performance for a single model landmark in terms of an estimated geometric boundary detection error. In consequence, boundary detection can be optimized per landmark during model training. We demonstrate the success of the method for cardiac image segmentation. In particular we show that the Simulated Search improves the capture range and the accuracy of the boundary detection compared to a traditional training scheme. We also illustrate how the Simulated Search can be used to identify suitable classes of features when addressing a new segmentation task. Finally, we show that the Simulated Search enables multi-modal heart segmentation using a single algorithmic framework. On computed tomography and magnetic resonance images, average segmentation errors (surface-to-surface distances) for the four chambers and the trunks of the large vessels are in the order of 0.8 mm. For 3D rotational X-ray angiography images of the left atrium and pulmonary veins, the average error is 1.3 mm. In all modalities, the locally optimized boundary detection enables fully automatic segmentation.     

Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map

Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information in localizing most of boundaries and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping applied to 2D and 3D OCT datasets is composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers. In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node. The weights of the graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity. The first diffusion map clusters the data into three parts, the second of which is the area of interest. The other two sections are eliminated from the remaining calculations. In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities. The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals). The mean unsigned border positioning errors (mean ± SD) was 8.52 ± 3.13 and 7.56 ± 2.95 μm for the 2D and 3D methods, respectively.     

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images

A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73 ± 11.24 years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation–Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63 and 0.85, 0.60 and 0.98, 0.56 and 0.94, and 0.83 and 0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org).     

Comparison of atlas-based techniques for whole-body bone segmentation

We evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean square distance (MSD) as image similarity measures for calculating the weighting factors, along with other atlas-dependent algorithms, such as (v) shape-based averaging (SBA) and (vi) Hofmann's pseudo-CT generation method. The performance evaluation of the different segmentation techniques was carried out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice criterion, global weighting atlas fusion methods provided moderate improvement of whole-body bone segmentation (DSC = 0.65 ± 0.05) compared to non-weighted IA (DSC = 0.60 ± 0.02). The local weighed atlas fusion approach using the MSD similarity measure outperformed the other strategies by achieving a DSC of 0.81 ± 0.03 while using the NCC and NMI measures resulted in a DSC of 0.78 ± 0.05 and 0.75 ± 0.04, respectively. Despite very long computation time, the extracted bone obtained from both SBA (DSC = 0.56 ± 0.05) and Hofmann's methods (DSC = 0.60 ± 0.02) exhibited no improvement compared to non-weighted IA. Finding the optimum parameters for implementation of the atlas fusion approach, such as weighting factors and image similarity patch size, have great impact on the performance of atlas-based segmentation approaches. The voxel-wise atlas fusion approach exhibited excellent performance in terms of cancelling out the non-systematic registration errors leading to accurate and reliable segmentation results. Denoising and normalization of MR images together with optimization of the involved parameters play a key role in improving bone extraction accuracy.     

Evaluation of a nurse-led management program to complement the treatment of adolescent acute lymphoblastic leukemia patients

PurposeTo evaluate a nurse-led management model of adolescent acute lymphoblastic leukemia (ALL) patients and improve their psychological care and quality of life.MethodsSeventy-three adolescent ALL patients participated in an open, controlled clinical trial and were randomized into a nurse-led management model group (n = 36) and a doctor-led management model group (n = 37). Two assessment questionnaires were administered to assess and compare the 2 models during a 1.5-year follow-up period: the hospital anxiety and depression scale (HADS) questionnaire was administered at 6 different time points, and the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 (QLQ-C30) at 3 different time points.ResultsThere were no differences in anxiety and depression between the groups according to the first-administered questionnaire (the mean anxiety and depression scores of the nurse-led group were 14.2 ± 4.1 and 10.8 ± 2.7, respectively; those of the doctor-led group were 13.8 ± 3.8 and 10.6 ± 2.2, respectively). However, repeated measures analysis of variance detected differences in subsequent HADS-based scores as a function of time between the 2 groups (p < 0.05). Moreover, the Holm–Sidak's multiple comparisons tests showed that patients of the nurse-led group had significantly decreased mean anxiety scores compared to those in the doctor-led group at the third and subsequent sessions, as well as in mean depression scores from the second session onwards (all p < 0.05). According to the last-administered EORTC QLQ-C30 questionnaire, there were statistical differences in cognitive, emotional, social, and quality of life scales between the 2 groups (all p < 0.05), but not in role and physical scales (all p > 0.05).ConclusionsIt is necessary to offer unique cognitive, psychological, and behavioral management models to adolescent ALL patients that are tailored toward their age group. Strengthening such management is more conducive to alleviating or even reversing psychological problems, and to improving patients' quality of life while ensuring complication-free follow-up periods.     

Chest wall segmentation in automated 3D breast ultrasound scans

In this paper, we present an automatic method to segment the chest wall in automated 3D breast ultrasound images. Determining the location of the chest wall in automated 3D breast ultrasound images is necessary in computer-aided detection systems to remove automatically detected cancer candidates beyond the chest wall and it can be of great help for inter- and intra-modal image registration. We show that the visible part of the chest wall in an automated 3D breast ultrasound image can be accurately modeled by a cylinder. We fit the surface of our cylinder model to a set of automatically detected rib-surface points. The detection of the rib-surface points is done by a classifier using features representing local image intensity patterns and presence of rib shadows. Due to attenuation of the ultrasound signal, a clear shadow is visible behind the ribs. Evaluation of our segmentation method is done by computing the distance of manually annotated rib points to the surface of the automatically detected chest wall. We examined the performance on images obtained with the two most common 3D breast ultrasound devices in the market. In a dataset of 142 images, the average mean distance of the annotated points to the segmented chest wall was 5.59 ± 3.08 mm.     

Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations

The automatic segmentation of the musculoskeletal system from medical images is a particularly challenging task, due to its morphological complexity, its large variability in the population and its potentially large deformations. In this paper we propose a novel approach for musculoskeletal segmentation and registration based on simplex meshes. Such discrete models have already proven to be efficient and versatile for medical image segmentation. We extend the current framework by introducing a multi-resolution approach and a reversible medial representation, in order to reduce the complexity of geometric and non-penetration constraints computation. Our framework allows both inter and intra-patient registration (involving both rigid and elastic matching). We also show that the introduced representations facilitate morphological analysis. As a case study, we demonstrate that muscles, bones, ligaments and cartilages of the hip and the thigh can be registered at an interactive frame rate, in a time-efficient way (<30 min), with a satisfactory accuracy (～1.5 mm), and with a minimal amount of manual tasks.     

Trainable COSFIRE filters for vessel delineation with application to retinal images

Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis.We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding.The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.     

Statistical coronary motion models for 2D + t/3D registration of X-ray coronary angiography and CTA

Accurate alignment of intra-operative X-ray coronary angiography (XA) and pre-operative cardiac CT angiography (CTA) may improve procedural success rates of minimally invasive coronary interventions for patients with chronic total occlusions. It was previously shown that incorporating patient specific coronary motion extracted from 4D CTA increases the robustness of the alignment. However, pre-operative CTA is often acquired with gating at end-diastole, in which case patient specific motion is not available.For such cases, we investigate the possibility of using population based coronary motion models to provide constraints for the 2D + t/3D registration. We propose a methodology for building statistical motion models of the coronary arteries from a training population of 4D CTA datasets. We compare the 2D + t/3D registration performance of the proposed statistical models with other motion estimates, including the patient specific motion extracted from 4D CTA, the mean motion of a population, the predicted motion based on the cardiac shape.The coronary motion models, constructed on a training set of 150 patients, had a generalization accuracy of 1 mm root mean square point-to-point distance. Their 2D + t/3D registration accuracy on one cardiac cycle of 12 monoplane XA sequences was similar to, if not better than, the 4D CTA based motion, irrespective of which respiratory model and which feature based 2D/3D distance metric was used. The resulting model based coronary motion estimate showed good applicability for registration of a subsequent cardiac cycle.     

Effect of different calcineurin inhibitors on AOPP and TAS after kidney transplantation

ObjectivesLittle is known about the influence of calcineurin inhibitors on advanced oxidation protein products (AOPP) and total antioxidant status (TAS) after renal transplantation.Design and methodsAOPP and TAS were evaluated in transplanted patients on different calcineurin inhibitors. Thirty-five patients were treated with cyclosporine A (group A) and 33 with tacrolimus (group B).ResultsOver 6 months, the mean levels of AOPP in group A decreased from 205.9 ± 125.7 to 140.9 ± 78.9 µmol/L and TAS from 1.89 ± 0.30 to 1.75 ± 0.27 mmol/L. In group B, the mean levels of AOPP decreased from 196.5 ± 123.9 to 129.6 ± 63.8 µmol/L and TAS from 1.80 ± 0.39 to 1.78 ± 0.23 mmol/L.ConclusionNo significant differences in AOPP and TAS were found with respect to treatment. The only exception was the higher mean concentration of AOPP at month 1 in group A (p = 0.026).     

Automatic MRI segmentation and morphoanatomy analysis of the vestibular system in adolescent idiopathic scoliosis

The vestibular system is the sensory organ responsible for perceiving head rotational movements and maintaining postural balance of human body. The objectives of this study are to propose an innovative computational technique capable of automatically segmenting the vestibular system and to analyze its geometrical features from high resolution T2-weighted MR images. In this study, the proposed technique was used to test the hypothesis that the morphoanatomy of vestibular system in adolescent idiopathic scoliosis (AIS) patients is different from healthy control subjects. The findings could contribute significantly to the understanding of the etiopathogenesis of AIS. The segmentation pipeline consisted of extraction of region of interest, image pre-processing, K-means clustering, and surface smoothing. The geometry of this high-genus labyrinth structure was analyzed through automatic partition into genus-0 units and approximation using the best-fit circle and plane for each unit. The metrics of the best-fit planes and circles were taken as shape measures. The proposed technique was applied on a cohort of 20 right-thoracic AIS patients (mean age 14.7 years old) and 20 age-matched healthy girls. The intermediate results were validated by subjective scoring. The result showed that the distance between centers of lateral and superior canals and the angle with vertex at the center of posterior canal were significantly smaller in AIS than in healthy controls in the left-side vestibular system with p = 0.0264 and p = 0.0200 respectively, but not in the right-side counterparts. The detected morphoanatomical changes are likely to be associated with subclinical postural, vestibular and proprioceptive dysfunctions reported frequently in AIS. This study has demonstrated that the proposed method could be applied in MRI-based morphoanatomy studies of vestibular system clinically.     

Does trunk and hip muscles strength predict performance during a core stability test?

BackgroundA better understanding about the relationship between trunk and hip muscles strength and core stability may improve evaluation and interventions proposed to improve core stability.ObjectivesTo investigate if trunk and hip muscles strength predict pelvic posterior rotation during the bridge test with unilateral knee extension.MethodsThis is a cross-sectional study. Sixty-one healthy individuals of both sexes (age, 28 ± 6.4 years, weight, 66.5 ± 10.9 kg, height, 167 ± 9.5 cm) performed the bridge test with unilateral knee extension. The pelvic posterior rotation during the bridge test was obtained with two-dimensional video analysis. Isometric strength of the trunk extensors and rotators, and hip abductors, external and internal rotators and extensors were measured with a hand-held dynamometer. Multiple linear regression analysis was performed to identify if the strength variables could explain the pelvic posterior rotation during the test.ResultsMuscle strength predicted pelvic posterior rotation during the bridge test (r = 0.54; p = 0.003). Strength of the trunk rotators (p = 0.045) and hip internal rotators (p = 0.015) predicted reduced magnitude of pelvic posterior rotation during the bridge test, and strength of the hip extensors (p = 0.003) predicted increased magnitude of pelvic posterior rotation.ConclusionsTrunk rotators and hip internal rotators and extensors strength predict 29% of the performance during the bridge test with unilateral knee extension. The strength of these muscles should be evaluated in individuals with increased pelvic posterior rotation during the bridge test with unilateral knee extension.     

Quantifying acromiohumeral distance in elite male field hockey players compared to a non-athletic population

BackgroundShoulders of elite field hockey players are loaded continuously during play. Frequent high shoulder loading is known to influence certain shoulder variables, such as acromiohumeral distance. However, the influence of elite field hockey play on acromiohumeral distance is not examined yet.ObjectiveTo examine the acromiohumeral distance in elite male field hockey players compared to a non-athletic control group.DesignCross-sectional study.MethodsFifty male participants were included; 25 elite field hockey players (age: 24.0 ± 2.72 years; weight: 77.2 ± 5.29 kg; height; 180.5 ± 5.37 cm) and 25 age- and gender matched non-athletes (23.0 ± 2.29 years; weight: 75.7 ± 9.53 kg; height: 181.8 ± 5.52 cm). A Telemed-Colormaster-128 EXT-IZ device was used to bilaterally obtain ultrasound acromiohumeral distance images at 3 different angles of shoulder abduction in the frontal plane.ResultsField hockey players showed a bilateral larger acromiohumeral distance at 45° (mean difference: 1.46 mm [95% CI 0.46; 2.46]; p = 0.005) and 60° (mean difference: 1.07 mm [95% CI 0.21; 1.93]; p = 0.016) compared to controls. In both groups, a significant but clinically less relevant, side difference was established for the acromiohumeral distance at 60° (mean difference: 0.79 mm [95% CI 0.21; 1.34]; p = 0.009).ConclusionWith active shoulder abduction, elite field hockey players show a larger acromiohumeral distance in comparison to non-athletic participants. This may be a protective sport-specific adaptation, to better guard the shoulder from injury. Thus, acromiohumeral distance measurement may help physical therapists/coaches decide which athletes could benefit from specific, additional exercises aimed at enlarging the subacromial space.     

Amygdala volume in depressed patients with bipolar disorder assessed using high resolution 3T MRI: The impact of medication

MRI-based reports of both abnormally increased and decreased amygdala volume in bipolar disorder (BD) have surfaced in the literature. Two major methodological weaknesses characterizing extant studies are treatment with medication and inaccurate segmentation of the amygdala due to limitations in spatial and tissue contrast resolution. Here, we acquired high-resolution images (voxel size = 0.55 × 0.55 × 0.60 mm) using a GE 3T MRI scanner, and a pulse sequence optimized for tissue contrast resolution. The amygdala was manually segmented by one rater blind to diagnosis, using coronal images. Eighteen unmedicated (mean medication-free period 11 ± 10 months) BD subjects were age and gender matched with 18 healthy controls, and 17 medicated (lithium or divalproex) subjects were matched to 17 different controls. The unmedicated BD patients displayed smaller left and right amygdala volumes than their matched control group (p < 0.01). Conversely, the BD subjects undergoing medication treatment showed a trend towards greater amygdala volumes than their matched HC sample (p = 0.051). Right and left amygdala volumes were larger (p < 0.05) or trended larger, respectively, in the medicated BD sample compared with the unmedicated BD sample. The two control groups did not differ from each other in either left or right amygdala volume. BD patients treated with lithium have displayed increased gray matter volume of the cortex and hippocampus relative to untreated BD subjects in previous studies. Here we extend these results to the amygdala. We raise the possibility that neuroplastic changes in the amygdala associated with BD are moderated by some mood stabilizing medications.     

Trends and Racial Differences in the Use of Androgen Deprivation Therapy for Metastatic Prostate Cancer

ContextAndrogen deprivation therapy (ADT) is widely used to manage the symptoms of advanced prostate cancer and has been shown to slow the progression of the disease. Previous research investigating racial differences in the use of ADT has reported inconsistent findings.ObjectivesThe purpose of this study was to assess use trends for ADT overall and by type (orchiectomy and luteinizing hormone-releasing hormone [LHRH] agonists) and the factors associated with time to receipt for metastatic prostate cancer.MethodsData from the Surveillance, Epidemiology, and End Results (SEER) cancer registry and Medicare claims database were obtained for 5,273 men, aged 65 years and older and diagnosed with Stage IV prostate cancer during 1991–1999 from seven SEER regions. An accelerated failure time regression model with log-normal distribution was used to examine factors associated with mean time to receipt of ADT.ResultsAfrican-American men were less likely than white men to receive any ADT after diagnosis (P < 0.001). Differences were noted in the time to receipt of ADT, with African-American men having a longer mean time to receipt of orchiectomy (time ratio [TR] = 1.50; 95% confidence interval [CI] = 1.03, 2.17) or LHRH agonist (TR = 1.42; 95% CI = 1.06, 1.89) than white men.ConclusionAfrican-American men with metastatic prostate cancer were significantly less likely to receive ADT and, when treated, had a slightly longer time to receipt than white men, which has implications for patients and physicians involved in the palliative management of metastatic prostate cancer.