Low-Dose CT Image Denoising using Image Decomposition and Sparse Representation

X-ray computed tomography (CT) is now a widely used imaging modality for numerous medical purposes. The risk of high X-ray radiation may induce genetic, cancerous and other diseases, demanding the development of new image processing methods that are able to enhance the quality of low-dose CT images. However, lowering the radiation dose increases the noise in acquired images and hence affects important diagnostic information. This paper contributes an efficient denoising method for low-dose CT images. A noisy image is decomposed into three component images of low, medium and high frequency bands; noise is mainly presented in the medium and high component images. Then, by exploiting the fact that a small image patch of the noisy image can be approximated by a linear combination of several elements in a given dictionary of noise-free image patches generated from noise-free images taken at nearly the same position with the noisy image, noise in these medium and high component images are effectively eliminated.Specifically, we give new solutions for image decomposition to easily control the filter parameters, for dictionary construction to improve the effectiveness and reduce the running-time. Instead of using a large dataset of patches, only a structured small part of patches extracted from the raw data is used to form a dictionary, to be used in sparse coding. In addition, we illustrate the effectiveness of the proposed method in preserving image details which are subtle but clinically important. Experimental results conducted on both synthetic and real noise data demonstrate that the proposed method is competitive with the state-of-the-art methods

3D surface reconstruction using dense optical flow combined to feature matching: Application to endoscopy

International audienceIn structure from motion (SfM) algorithms, the surface reconstruction performance strongly depends on the quality of the determination of homologous points between images. Classical feature matching-based methods as integrated in the state-of the-art SfM-algorithms are often inoperative for scenes including weak structures and textures (e.g., as those in medical endoscopic videos). This contribution introduces an effective solution based on the combination of dense optical flow and feature matching. The accuracy and robustness of the proposed method were validated using results obtained for a phantom with known dimensions and with patient data, respectively. Apart from the high performance obtained for cystoscopy and gastroscopy, the proposed solution has a high potential in other medical and non-medical scenes.Dans les algorithmes de structures à partir du mouvement (SfM), la performance de la reconstruction des surfaces dépend fortement de la qualité de la détermination des points homologues entre images. Les méthodes SfM de référence sont souvent inopérantes pour les scènes avec peu de structures et textures faiblement contrastées car elles reposent uniquement sur l'appariement de caractéristiques. Cette contribution présente une solution associant un flot optique dense à la mise en correspondance de caractéristiques. La précision et la robustesse de la reconstruction ont été validées via des résultats obtenus pour un fantôme avec des dimensions connues et avec des données patient en cystoscopie et en gastroscopie, respectivement. Plus généralement, cette approche a un fort potentiel pour toute scène peu constrastée, médicales ou non

Ventilator-associated respiratory infection in a resource-restricted setting: impact and etiology.

BACKGROUND: Ventilator-associated respiratory infection (VARI) is a significant problem in resource-restricted intensive care units (ICUs), but differences in casemix and etiology means VARI in resource-restricted ICUs may be different from that found in resource-rich units. Data from these settings are vital to plan preventative interventions and assess their cost-effectiveness, but few are available. METHODS: We conducted a prospective observational study in four Vietnamese ICUs to assess the incidence and impact of VARI. Patients ≥ 16 years old and expected to be mechanically ventilated > 48 h were enrolled in the study and followed daily for 28 days following ICU admission. RESULTS: Four hundred fifty eligible patients were enrolled over 24 months, and after exclusions, 374 patients' data were analyzed. A total of 92/374 cases of VARI (21.7/1000 ventilator days) were diagnosed; 37 (9.9%) of these met ventilator-associated pneumonia (VAP) criteria (8.7/1000 ventilator days). Patients with any VARI, VAP, or VARI without VAP experienced increased hospital and ICU stay, ICU cost, and antibiotic use (p < 0.01 for all). This was also true for all VARI (p < 0.01 for all) with/without tetanus. There was no increased risk of in-hospital death in patients with VARI compared to those without (VAP HR 1.58, 95% CI 0.75-3.33, p = 0.23; VARI without VAP HR 0.40, 95% CI 0.14-1.17, p = 0.09). In patients with positive endotracheal aspirate cultures, most VARI was caused by Gram-negative organisms; the most frequent were Acinetobacter baumannii (32/73, 43.8%) Klebsiella pneumoniae (26/73, 35.6%), and Pseudomonas aeruginosa (24/73, 32.9%). 40/68 (58.8%) patients with positive cultures for these had carbapenem-resistant isolates. Patients with carbapenem-resistant VARI had significantly greater ICU costs than patients with carbapenem-susceptible isolates (6053 USD (IQR 3806-7824) vs 3131 USD (IQR 2108-7551), p = 0.04) and after correction for adequacy of initial antibiotics and APACHE II score, showed a trend towards increased risk of in-hospital death (HR 2.82, 95% CI 0.75-6.75, p = 0.15). CONCLUSIONS: VARI in a resource-restricted setting has limited impact on mortality, but shows significant association with increased patient costs, length of stay, and antibiotic use, particularly when caused by carbapenem-resistant bacteria. Evidence-based interventions to reduce VARI in these settings are urgently needed

Illumination-invariant optical flow: Application to endoscopic image mosaicing

International audienceThis contribution deals with the optical flow computation in weakly textured scenes affected by strong illumination changes. The data-term is based on a new illumination invariant patch-based descriptor which also preserves the texture information. An illumination change model was used to theoretically prove that the descriptor is invariant to complex illumination changes. Tests with complicated simulated images and challenging clinical endoscopic data allowed us to assess the accuracy of the OF and to highlight the robustness induced by the descriptor.Cette contribution traite du calcul du flot optique dans des scènes faiblement texturées et affectées par des changements d'illuminations importants. Un nouveau descripteur invariant à l'illumination est proposé dans le terme d'attache aux données de la fonctionnelle a minimiser. Un modèle local de changement d'illumination a été utilisé pour prouver théoriquement que le descripteur est invariant à des changements complexes d'éclairage. Des tests avec des images simulées et des données endoscopiques réelles (cliniques) permettent d'évaluer la précision du flot optique et de mettre en lumìère la robustesse induite par le descripteur

Dense optical flow for the reconstruction of weakly textured and structured surfaces: Application to endoscopy

International audienceThis paper introduces a structure from motion (SfM)-based surface reconstruction method for images including weak textures and structures. In SfM, the quality of the determination of homologous points between images plays a key role in terms of reconstruction performances. However, classical feature matching-based methods as integrated in the state-of-the-art SfM-algorithms are often inoperative for images with weak structures and textures. This contribution describes a dense optical flow-based solution enabling the point correspondence determination in such scenes. The accuracy and robustness of the proposed method were validated using results obtained for a phantom with known dimensions and with real medical data, respectively. Complex internal stomach wall surfaces were constructed using gastroscopic images

Dependence of Particle Size and Geometry of Copper Powder on the Porosity and Capillary Performance of Sintered Porous Copper Wicks for Heat Pipes

Permeability and capillary performance are the most important parameters relating to the thermal performance of heat pipes. These parameters are deeply linked to pore structure, which has been influenced by the starting powder utilized. In this paper, the effect of particle size and geometry of copper powder on the porosity and capillary performance of porous wicks were systematically studied. Sintered porous wicks were made from different-sized spherical (58 &mu;m, 89 &mu;m, 125 &mu;m) and dendritic (59 &mu;m, 86 &mu;m, 130 &mu;m) Cu powders. The results demonstrated that the porosity and capillary performance of both types of copper powder increase with particle size due to an increase in the connectivity between internal pores. In comparison to the spherical powder, the dendritic powder demonstrated superior capillary efficiency as well as greater porosity. Additionally, a model was proposed for the capillary performance and permeability of sintered porous copper. The predicted results were quite comparable to the experimental data, demonstrating the effect of the starting powder. These findings suggest that porosity and capillary performance of porous wicks are strongly related to powder geometry as well as particle size

Dependence of Particle Size and Geometry of Copper Powder on the Porosity and Capillary Performance of Sintered Porous Copper Wicks for Heat Pipes

Permeability and capillary performance are the most important parameters relating to the thermal performance of heat pipes. These parameters are deeply linked to pore structure, which has been influenced by the starting powder utilized. In this paper, the effect of particle size and geometry of copper powder on the porosity and capillary performance of porous wicks were systematically studied. Sintered porous wicks were made from different-sized spherical (58 μm, 89 μm, 125 μm) and dendritic (59 μm, 86 μm, 130 μm) Cu powders. The results demonstrated that the porosity and capillary performance of both types of copper powder increase with particle size due to an increase in the connectivity between internal pores. In comparison to the spherical powder, the dendritic powder demonstrated superior capillary efficiency as well as greater porosity. Additionally, a model was proposed for the capillary performance and permeability of sintered porous copper. The predicted results were quite comparable to the experimental data, demonstrating the effect of the starting powder. These findings suggest that porosity and capillary performance of porous wicks are strongly related to powder geometry as well as particle size

On the in vivo recognition of kidney stones using machine learning

Determining the type of kidney stones allows urologists to prescribe a treatment to avoid recurrence of renal lithiasis. An automated in-vivo image-based classification method would be an important step towards an immediate identification of the kidney stone type required as a first phase of the diagnosis. In the literature it was shown on ex-vivo data (i.e., in very controlled scene and image acquisition conditions) that an automated kidney stone classification is indeed feasible. This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies. This contribution details the database construction and the design of the tested kidney stones classifiers. Even if the best results were obtained by the Inception v3 architecture (weighted precision, recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown that choosing an appropriate colour space and texture features allows a shallow machine learning method to approach closely the performances of the most promising deep-learning methods (the XGBoost classifier led to weighted precision, recall and F1-score values of 0.96). This paper is the first one that explores the most discriminant features to be extracted from images acquired during ureteroscopies.Comment: Paper submitted to Computer Methods and Programs in Biomedicine (CMPB

Ventilator-associated respiratory infection in a resource-restricted setting: impact and etiology

Ventilator-associated respiratory infection (VARI) is a significant problem in resource-restricted intensive care units (ICUs), but differences in casemix and etiology means VARI in resource-restricted ICUs may be different from that found in resource-rich units. Data from these settings are vital to plan preventative interventions and assess their cost-effectiveness, but few are available.We conducted a prospective observational study in four Vietnamese ICUs to assess the incidence and impact of VARI. Patients ≥ 16 years old and expected to be mechanically ventilated &gt; 48 h were enrolled in the study and followed daily for 28 days following ICU admission.Four hundred fifty eligible patients were enrolled over 24 months, and after exclusions, 374 patients' data were analyzed. A total of 92/374 cases of VARI (21.7/1000 ventilator days) were diagnosed; 37 (9.9%) of these met ventilator-associated pneumonia (VAP) criteria (8.7/1000 ventilator days). Patients with any VARI, VAP, or VARI without VAP experienced increased hospital and ICU stay, ICU cost, and antibiotic use (p &lt; 0.01 for all). This was also true for all VARI (p &lt; 0.01 for all) with/without tetanus. There was no increased risk of in-hospital death in patients with VARI compared to those without (VAP HR 1.58, 95% CI 0.75-3.33, p = 0.23; VARI without VAP HR 0.40, 95% CI 0.14-1.17, p = 0.09). In patients with positive endotracheal aspirate cultures, most VARI was caused by Gram-negative organisms; the most frequent were Acinetobacter baumannii (32/73, 43.8%) Klebsiella pneumoniae (26/73, 35.6%), and Pseudomonas aeruginosa (24/73, 32.9%). 40/68 (58.8%) patients with positive cultures for these had carbapenem-resistant isolates. Patients with carbapenem-resistant VARI had significantly greater ICU costs than patients with carbapenem-susceptible isolates (6053 USD (IQR 3806-7824) vs 3131 USD (IQR 2108-7551), p = 0.04) and after correction for adequacy of initial antibiotics and APACHE II score, showed a trend towards increased risk of in-hospital death (HR 2.82, 95% CI 0.75-6.75, p = 0.15).VARI in a resource-restricted setting has limited impact on mortality, but shows significant association with increased patient costs, length of stay, and antibiotic use, particularly when caused by carbapenem-resistant bacteria. Evidence-based interventions to reduce VARI in these settings are urgently needed