SerpinB1 is critical for neutrophil survival through cell-autonomous inhibition of cathepsin G

Bone marrow (BM) holds a large reserve of polymorphonuclear neutrophils (PMNs) that are rapidly mobilized to the circulation and tissues in response to danger signals. SerpinB1 is a potent inhibitor of neutrophil serine proteases neutrophil elastase (NE) and cathepsin G (CG). SerpinB1 deficiency (sB1(-/-)) results in a severe reduction of the BM PMN reserve and failure to clear bacterial infection. Using BM chimera, we found that serpinB1 deficiency in BM cells was necessary and sufficient to reproduce the BM neutropenia of sB1(-/-) mice. Moreover, we showed that genetic deletion of CG, but not NE, fully rescued the BM neutropenia in sB1(-/-) mice. In mixed BM chimera and in vitro survival studies, we showed that CG modulates sB1(-/-) PMN survival through a cell-intrinsic pathway. In addition, membrane permeabilization by lysosomotropic agent l-leucyl-l-leucine methyl ester that allows cytosolic release of granule contents was sufficient to induce rapid PMN death through a CG-dependent pathway. CG-mediated PMN cytotoxicity was only partly blocked by caspase inhibition, suggesting that CG cleaves a distinct set of targets during apoptosis. In conclusion, we have unveiled a new cytotoxic function for the serine protease CG and showed that serpinB1 is critical for maintaining PMN survival by antagonizing intracellular CG activity.

Fully automatic X-ray image segmentation via joint estimation of image displacements.

We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

PURPOSE    Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS    Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS    A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was [Formula: see text], requiring [Formula: see text] s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS    A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.

Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).

Pelvic tilt is minimally changed by total hip arthroplasty

BACKGROUND While surgical navigation offers the opportunity to accurately place an acetabular component, questions remain as to the best goal for acetabular component positioning in individual patients. Overall functional orientation of the pelvis after surgery is one of the most important variables for the surgeon to consider when determining the proper goal for acetabular component orientation. QUESTIONS/PURPOSES We measured the variation in pelvic tilt in 30 patients before THA and the effect of THA on pelvic tilt in the same patients more than a year after THA. METHODS Each patient had a CT study for CT-based surgical navigation and standing and supine radiographs before and after surgery. Pelvic tilt was calculated for each of the radiographs using a novel and validated two-dimensional/three-dimensional matching technique. RESULTS Mean supine pelvic tilt changed less than 2°, from 4.4° ± 6.4° (range, -7.7° to 20.8°) before THA to 6.3° ± 6.6° (range, -5.7° to 19.6°) after THA. Mean standing pelvic tilt changed less than 1°, from 1.5° ± 7.2° (range, -13.1° to 12.8°) before THA to 2.0° ± 8.3° (range, -12.3° to 16.8°) after THA. Preoperative pelvic tilt correlated with postoperative tilt in both the supine (r(2) = 0.75) and standing (r(2) = 0.87) positions. CONCLUSIONS In this population, pelvic tilt had a small and predictable change after surgery. However, intersubject variability of pelvic tilt was high, suggesting preoperative pelvic tilt should be considered when determining desired acetabular component positioning on a patient-specific basis.

3D volumetric intensity reconsturction from 2D x-ray images using partial least squares regression

Reconstruction of shape and intensity from 2D x-ray images has drawn more and more attentions. Previously introduced work suffers from the long computing time due to its iterative optimization characteristics and the requirement of generating digitally reconstructed radiographs within each iteration. In this paper, we propose a novel method which uses a patient-specific 3D surface model reconstructed from 2D x-ray images as a surrogate to get a patient-specific volumetric intensity reconstruction via partial least squares regression. No DRR generation is needed. The method was validated on 20 cadaveric proximal femurs by performing a leave-one-out study. Qualitative and quantitative results demonstrated the efficacy of the present method. Compared to the existing work, the present method has the advantage of much shorter computing time and can be applied to both DXA images as well as conventional x-ray images, which may hold the potentials to be applied to clinical routine task such as total hip arthroplasty (THA).

Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.

Expectation Conditional Maximization-Based Deformable Shape Registration

This paper addresses the issue of matching statistical and non-rigid shapes, and introduces an Expectation Conditional Maximization-based deformable shape registration (ECM-DSR) algorithm. Similar to previous works, we cast the statistical and non-rigid shape registration problem into a missing data framework and handle the unknown correspondences with Gaussian Mixture Models (GMM). The registration problem is then solved by fitting the GMM centroids to the data. But unlike previous works where equal isotropic covariances are used, our new algorithm uses heteroscedastic covariances whose values are iteratively estimated from the data. A previously introduced virtual observation concept is adopted here to simplify the estimation of the registration parameters. Based on this concept, we derive closed-form solutions to estimate parameters for statistical or non-rigid shape registrations in each iteration. Our experiments conducted on synthesized and real data demonstrate that the ECM-DSR algorithm has various advantages over existing algorithms.

Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition

Knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic and robust approach for landmarking and segmentation of both pelvis and femur in a conventional AP X-ray. Our approach is based on random forest regression and hierarchical sparse shape composition. Experiments conducted on 436 clinical AP pelvis x-rays show that our approach achieves an average point-to-curve error around 1.3 mm for femur and 2.2 mm for pelvis, both with success rates around 98%. Compared to existing methods, our approach exhibits better performance in both the robustness and the accuracy.

Synchrotron microbeam radiation therapy induces hypoxia in intracerebral gliosarcoma but not in the normal brain

PURPOSE Synchrotron microbeam radiation therapy (MRT) is an innovative irradiation modality based on spatial fractionation of a high-dose X-ray beam into lattices of microbeams. The increase in lifespan of brain tumor-bearing rats is associated with vascular damage but the physiological consequences of MRT on blood vessels have not been described. In this manuscript, we evaluate the oxygenation changes induced by MRT in an intracerebral 9L gliosarcoma model. METHODS Tissue responses to MRT (two orthogonal arrays (2 × 400Gy)) were studied using magnetic resonance-based measurements of local blood oxygen saturation (MR_SO2) and quantitative immunohistology of RECA-1, Type-IV collagen and GLUT-1, marker of hypoxia. RESULTS In tumors, MR_SO2 decreased by a factor of 2 in tumor between day 8 and day 45 after MRT. This correlated with tumor vascular remodeling, i.e. decrease in vessel density, increases in half-vessel distances (×5) and GLUT-1 immunoreactivity. Conversely, MRT did not change normal brain MR_SO2, although vessel inter-distances increased slightly. CONCLUSION We provide new evidence for the differential effect of MRT on tumor vasculature, an effect that leads to tumor hypoxia. As hypothesized formerly, the vasculature of the normal brain exposed to MRT remains sufficiently perfused to prevent any hypoxia.

Synchrotron x ray induced axonal transections in the brain of rats assessed by high-field diffusion tensor imaging tractography

Since approximately two thirds of epileptic patients are non-eligible for surgery, local axonal fiber transections might be of particular interest for them. Micrometer to millimeter wide synchrotron-generated X-ray beamlets produced by spatial fractionation of the main beam could generate such fiber disruptions non-invasively. The aim of this work was to optimize irradiation parameters for the induction of fiber transections in the rat brain white matter by exposure to such beamlets. For this purpose, we irradiated cortex and external capsule of normal rats in the antero-posterior direction with a 4 mm×4 mm array of 25 to 1000 µm wide beamlets and entrance doses of 150 Gy to 500 Gy. Axonal fiber responses were assessed with diffusion tensor imaging and fiber tractography; myelin fibers were examined histopathologically. Our study suggests that high radiation doses (500 Gy) are required to interrupt axons and myelin sheaths. However, a radiation dose of 500 Gy delivered by wide minibeams (1000 µm) induced macroscopic brain damage, depicted by a massive loss of matter in fiber tractography maps. With the same radiation dose, the damage induced by thinner microbeams (50 to 100 µm) was limited to their paths. No macroscopic necrosis was observed in the irradiated target while overt transections of myelin were detected histopathologically. Diffusivity values were found to be significantly reduced. A radiation dose ≤ 500 Gy associated with a beamlet size of < 50 µm did not cause visible transections, neither on diffusion maps nor on sections stained for myelin. We conclude that a peak dose of 500 Gy combined with a microbeam width of 100 µm optimally induced axonal transections in the white matter of the brain.