FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN).

OBJECTIVE: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due to the difficulties of data collection and data privacy, finding an appropriate dataset (balanced, enough samples, etc.) is quite a challenge. Although image synthesis could be beneficial to overcome this issue, synthesizing 3D images is a hard task. The main objective of this paper is to generate 3D T1 weighted MRI corresponding to FDG-PET. In this study, we propose a separable convolution-based Elicit generative adversarial network (E-GAN). The proposed architecture can reconstruct 3D T1 weighted MRI from 2D high-level features and geometrical information retrieved from a Sobel filter. Experimental results on the ADNI datasets for healthy subjects show that the proposed model improves the quality of images compared with the state of the art. In addition, the evaluation of E-GAN and the state of art methods gives a better result on the structural information (13.73% improvement for PSNR and 22.95% for SSIM compared to Pix2Pix GAN) and textural information (6.9% improvements for homogeneity error in Haralick features compared to Pix2Pix GAN).

B-type natriuretic peptide measurement for early diagnosis of acute pulmonary edema during pregnancy.

Calcium-channel blockers administered to pregnant women as tocolytic agents can cause acute pulmonary edema. The first signs of this severe complication can be atypical and so delay introduction of appropriate therapy. We describe three cases in whom B-type natriuretic peptide measurements proved to be relevant in early diagnosis and monitoring of pregnant women with acute pulmonary edema. B-type natriuretic peptide measurement in this setting could contribute to timely diagnosis and improve follow-up.

SBA-15 supported Ni-Cu catalysts for hydrodeoxygenation of m-cresol to toluene.

Amidst concerns over fossil fuel dependency and environmental sustainability, the utilization of biomass-derived aromatic compounds emerges as a viable solution across diverse industries. In this scheme, the conversion of biomass involves pyrolysis, followed by a hydrodeoxygenation (HDO) step to reduce the oxygen content of pyrolysis oils and stabilize the end products including aromatics. In this study, we explored the properties of size controlled NiCu bimetallic catalysts supported on ordered mesoporous silica (SBA-15) for the catalytic gas-phase HDO of m-cresol, a lignin model compound. We compared their performances with monometallic Ni and Cu catalysts. The prepared catalysts contained varying Ni to Cu ratios and featured an average particle size of approximately 2 nm. The catalytic tests revealed that the introduction of Cu alongside Ni enhanced the selectivity for the direct deoxygenation (DDO) pathway, yielding toluene as the primary product. Optimal performance was observed with a catalyst composition comprising 5 wt.% Ni and 5 wr.% Cu, achieving 85 % selectivity to toluene. Further increasing the Cu content improved turnover frequency (TOF) values, but reduced DDO selectivity. These findings underscore the importance of catalyst design in facilitating biomass-derived compound transformations and offer insights into optimizing catalyst composition for more selective HDO reactions.

Theoretical study of the catalytic hydrodeoxygenation of furan, methylfuran and benzofurane on MoS2.

Herein, we have studied the direct deoxygenation (DDO) (without prior hydrogenation) of furan, 2-methylfuran and benzofuran on the metal edge of MoS2 with a vacancy created under pressure of dihydrogen. For the three molecules, we found that the desorption of the water molecule for the regeneration of the vacancy is the most endothermic. Based on the thermodynamic and kinetic aspects, the reactivity order of the oxygenated compounds is furan ≈ 2-methylfuran > benzofuran, which is in agreement with literature. We present the key stages of the mechanisms and highlight the effects of substituents.

First combined in vivo X-ray tomography and high-resolution molecular electron paramagnetic resonance (EPR) imaging of the mouse knee joint taking into account the disappearance kinetics of the EPR probe.

Although laboratory data clearly suggest a role for oxidants (dioxygen and free radicals derived from dioxygen) in the pathogenesis of many age-related and degenerative diseases (such as arthrosis and arthritis), methods to image such species in vivo are still very limited. This methodological problem limits physiopathologic studies about the role of those species in vivo, the effects of their regulation using various drugs, and the evaluation of their levels for diagnosis of degenerative diseases. In vivo electron paramagnetic resonance (EPR) imaging and spectroscopy are unique, noninvasive methods used to specifically detect and quantify paramagnetic species. However, two problems limit their application: the anatomic location of the EPR image in the animal body and the relative instability of the EPR probes. Our aim is to use EPR imaging to obtain physiologic and pathologic information on the mouse knee joint. This article reports the first in vivo EPR image of a small tissue, the mouse knee joint, with good resolution (≈ 160 µm) after intra-articular injection of a triarylmethyl radical EPR probe. It was obtained by combining EPR and x-ray micro-computed tomography for the first time and by taking into account the disappearance kinetics of the EPR probe during image acquisition to reconstruct the image. This multidisciplinary approach opens the way to high-resolution EPR imaging and local metabolism studies of radical species in vivo in different physiologic and pathologic situations.

Surface Regularity via the Estimation of Fractional Brownian Motion Index.

The recent definition of fractional Brownian motions on surfaces has raised the statistical issue of estimating the Hurst index characterizing these models. To deal with this open issue, we propose a method which is based on a spectral representation of surfaces built upon their Laplace-Beltrami operator. This method includes a first step where the surface supporting the motion is recovered using a mean curvature flow, and a second one where the Hurst index is estimated by linear regression on the motion spectrum. The method is evaluated on synthetic surfaces. The interest of the method is further illustrated on some fetal cortical surfaces extracted from magnetic resonance images as a means to quantify the brain complexity during the gestational age.

Threatened preterm birth: Validation of a nomogram to predict the individual risk of very preterm delivery in a secondary care center.

INTRODUCTION: Very preterm delivery (22-32 weeks of gestation) remains a major cause of neonatal morbidity and mortality. The objective of this study was to validate a statistical model allowing to predict the risk of preterm delivery to use as a clinical decision-making tool for in utero transfer from a secondary to a tertiary care center. METHODS: Retrospective observational study in a secondary care center (approximately 2500 births) in Paris, France. 137 women were admitted for threatened preterm delivery between 22 and 32 weeks. Women were retrospectively allocated to the following groups based on medical decision: "transfer group" (in utero transfer to a tertiary care unit) and "no transfer group" (no in utero transfer). The risk of preterm delivery within 48 h and before 32 weeks gestation was assessed for each group using a nomogram previously validated in a tertiary care center. The primary objective of the study was to determine the accuracy of the prediction model. RESULTS: The discrimination and calibration of the nomogram were excellent (preterm delivery risk within 48 h, ROC AUC: 0.98, 95% CI: 0.95-1.00; probability of preterm delivery before 32 weeks gestation, ROC AUC: 0.94, 95% CI: 0.89-0.99). A threshold set at 0.16 helped minimize the risk of unnecessary in utero transfers with an excellent negative predictive value of 0.99. CONCLUSIONS: We validated nomograms to predict the individual probability of preterm birth after admission in a secondary care center. Those nomograms could be helpful when making decisions regarding an in utero transfer to a tertiary care unit.

LDL-apheresis to decrease sFlt-1 during early severe preeclampsia: Report of two cases from a discontinued phase II trial.

OBJECTIVE: Severe preeclampsia may require the delivery of the placenta to avoid life-threatening complications for the mother. Before 26 weeks of gestation, this often results in perinatal death. A decrease in soluble fms-like tyrosine kinase 1 (sFlt1), an anti-angiogenic factor central to the pathophysiology of the maternal syndrome, has been reported after LDL- apheresis. The present study tested whether LDL-apheresis could be used to allow women with early and severe preeclampsia to reach a gestational age where the baby had a viable chance of survival. STUDY DESIGN: A phase II prospective study. Adult women were included if they had very early (<26 weeks of gestation) preeclampsia without severe (<5th percentile) intra-uterine growth retardation. Treatment consisted of two weekly sessions (90 min each) of LDL-apheresis of whole blood. The primary endpoint was the status of the baby (dead or living) at 6 months post-delivery. Sample size and stopping rules were calculated assuming a desired success rate of at least 90%. RESULTS: The study was interrupted for safety reasons after the inclusion of two patients: both developed secondary uncontrolled hypertension and blurred vision during the first week of treatment. The first neonate, born at 25 + 3 weeks of gestation, died of sepsis at day 5; the second, born at 26 + 2 weeks of gestation, is still alive and well. In these two patients, the impact of apheresis sessions on sFlt1 concentrations was inconsistent. CONCLUSION: LDL-apheresis did not result in the prolongation of pregnancy in this phase II trial. Further studies will be needed to delineate the appropriate contours of this therapeutic strategy.

Local Spectral Analysis of the Cerebral Cortex: New Gyrification Indices.

Gyrification index (GI) is an appropriate measure to quantify the complexity of the cerebral cortex. There is, however, no universal agreement on the notion of surface complexity and there are various methods in literature that evaluate different aspects of cortical folding. In this paper, we give two intuitive interpretations on folding quantification based on the magnitude and variation of the mean curvature of the cortical surface. We then present a local spectral analysis of the mean curvature to introduce two local gyrification indices that satisfy our interpretations. For this purpose, the graph windowed Fourier transform is extended to the framework of surfaces discretized with triangular meshes. An adaptive window function is also proposed to deal with the intersubject cortical size variability. The intrinsic nature of the method allows us to compute the degree of folding at different spatial scales. Our experiments show that while more classical surface area-based GIs may fail at differentiating deep folds from very convoluted ones, our spectral GIs overcome this issue. The method is applied to the cortical surfaces of 124 healthy adult subjects of OASIS database and average gyrification maps are computed and compared with other GI definitions. In order to illustrate the capacity of our method to capture and quantify important aspects of gyrification, we study the relationship between brain volume and cortical complexity, and design a scaling analysis with a power law model. Results indicate an allometric relation and confirm the well-known observations that larger brains are more folded. We also perform the scaling analysis at the vertex level to investigate how the degree of folding varies locally with the brain volume. Results reveal that in our healthy adult brain database, cortical regions which are the least folded on average show an increased folding complexity when brain size increases.

Mammogram registration: a phantom-based evaluation of compressed breast thickness variation effects.

The temporal comparison of mammograms is complex; a wide variety of factors can cause changes in image appearance. Mammogram registration is proposed as a method to reduce the effects of these changes and potentially to emphasize genuine alterations in breast tissue. Evaluation of such registration techniques is difficult since ground truth regarding breast deformations is not available in clinical mammograms. In this paper, we propose a systematic approach to evaluate sensitivity of registration methods to various types of changes in mammograms using synthetic breast images with known deformations. As a first step, images of the same simulated breasts with various amounts of simulated physical compression have been used to evaluate a previously described nonrigid mammogram registration technique. Registration performance is measured by calculating the average displacement error over a set of evaluation points identified in mammogram pairs. Applying appropriate thickness compensation and using a preferred order of the registered images, we obtained an average displacement error of 1.6 mm for mammograms with compression differences of 1-3 cm. The proposed methodology is applicable to analysis of other sources of mammogram differences and can be extended to the registration of multimodality breast data.

Management of pregnancy in woman with suspected malignant deep infiltrating endometriosis fistulised to the uterine cervix.

Deep infiltrating endometriosis (DIE) is a well-known cause of pelvic pain and infertility. Malignant transformation of DIE is rare but can be suggested by MRI. We report a case of a spontaneous pregnancy in a woman with suspicion of malignant transformation of DIE with fistulisation to the posterior uterine isthmus through to the cervical canal. The pregnancy was closely monitored and an uneventful caesarian section was performed at 34 weeks of gestation. This case raises the issue of the relevance of imaging techniques and management of pregnancy.

Bayesian technique for image classifying registration.

In this paper, we address a complex image registration issue arising while the dependencies between intensities of images to be registered are not spatially homogeneous. Such a situation is frequently encountered in medical imaging when a pathology present in one of the images modifies locally intensity dependencies observed on normal tissues. Usual image registration models, which are based on a single global intensity similarity criterion, fail to register such images, as they are blind to local deviations of intensity dependencies. Such a limitation is also encountered in contrast-enhanced images where there exist multiple pixel classes having different properties of contrast agent absorption. In this paper, we propose a new model in which the similarity criterion is adapted locally to images by classification of image intensity dependencies. Defined in a Bayesian framework, the similarity criterion is a mixture of probability distributions describing dependencies on two classes. The model also includes a class map which locates pixels of the two classes and weighs the two mixture components. The registration problem is formulated both as an energy minimization problem and as a maximum a posteriori estimation problem. It is solved using a gradient descent algorithm. In the problem formulation and resolution, the image deformation and the class map are estimated simultaneously, leading to an original combination of registration and classification that we call image classifying registration. Whenever sufficient information about class location is available in applications, the registration can also be performed on its own by fixing a given class map. Finally, we illustrate the interest of our model on two real applications from medical imaging: template-based segmentation of contrast-enhanced images and lesion detection in mammograms. We also conduct an evaluation of our model on simulated medical data and show its ability to take into account spatial variations of intensity dependencies while keeping a good registration accuracy.

A local technique based on vectorized surfaces for craniofacial reconstruction.

In this paper, we focus on the automation of facial reconstruction. Since they consider the whole head as the object of interest, usual reconstruction techniques are global and involve a large number of parameters to be estimated. We present a local technique which aims at reaching a good trade-off between bias and variance following the paradigm of non-parametric statistics. The estimation is localized on patches delimited by surface geodesics between anatomical points of the skull. The technique relies on a continuous representation of the individual surfaces embedded in the vectorial space of extended normal vector fields. This allows to compute deformations and averages of surfaces. It consists in estimating the soft-tissue surface over patches. Using a homogeneous database described in [31], we obtain results on the chin and nasal regions with an average error below 1mm, outperforming the global reconstruction techniques.

A classifying registration technique for the estimation of enhancement curves of DCE-CT scan sequences.

In this paper, we propose a new technique for the estimation of contrast enhancement curves of Dynamic Contrast-Enhanced sequences, which takes the most from the interdependence between this estimation problem and the registration problem raised by possible movements occurring in sequences. The technique solves the estimation and registration problems simultaneously in an iterative way. However, unlike previous techniques, a pixel classification scheme is included within the estimation so as to compute enhancement curves on pixel classes instead of single pixels. The classification scheme is designed using a descendant hierarchical approach. Due to this tree approach, the number of classes is set automatically and the whole technique is entirely unsupervised. Moreover, some specific prior information about the shape of enhancement curves are included in the splitting and pruning steps of the classification scheme. Such an information ensures that created classes include pixels having homogeneous and relevant enhancement properties. The technique is applied to DET-CT scan sequences and evaluated using ground truth data. Results show that classifications are anatomically sound and that contrast enhancements are accurately estimated from sequences.

Construction and analysis of a head CT-scan database for craniofacial reconstruction.

This paper is devoted to the construction of a complete database which is intended to improve the implementation and the evaluation of automated facial reconstruction. This growing database is currently composed of 85 head CT-scans of healthy European subjects aged 20-65 years old. It also includes the triangulated surfaces of the face and the skull of each subject. These surfaces are extracted from CT-scans using an original combination of image-processing techniques which are presented in the paper. Besides, a set of 39 referenced anatomical skull landmarks were located manually on each scan. Using the geometrical information provided by triangulated surfaces, we compute facial soft-tissue depths at each known landmark positions. We report the average thickness values at each landmark and compare our measures to those of the traditional charts of [J. Rhine, C.E. Moore, Facial Tissue Thickness of American Caucasoïds, Maxwell Museum of Anthropology, Albuquerque, New Mexico, 1982] and of several recent in vivo studies [M.H. Manhein, G.A. Listi, R.E. Barsley, et al., In vivo facial tissue depth measurements for children and adults, Journal of Forensic Sciences 45 (1) (2000) 48-60; S. De Greef, P. Claes, D. Vandermeulen, et al., Large-scale in vivo Caucasian facial soft tissue thickness database for craniofacial reconstruction, Forensic Science International 159S (2006) S126-S146; R. Helmer, Schödelidentifizierung durch elektronische bildmischung, Kriminalistik Verlag GmbH, Heidelberg, 1984].