Distributed Biomedical Scheme for Controlled Recovery of Medical Encrypted Images

With an advancement in biomedical applications, many images are communicated over the public networks. Therefore, these medical images are prone to various security threats. Development of end to end secure communication protocol for these medical images is found to be a challenging task. Therefore, many researchers have proposed various image medica image encryption techniques to provide end to end security of medical images. However, the existing approaches of block-based recovery of the secret through progressive sharing paradigm have support for limited threshold value of the chosen blocks out of the total number of the blocks during the communication of the image. Most of the suggested scheme has fixed threshold value for the blocks during recovery of secret; works good for a limited threshold (k) value out of number of blocks (n) in which secret has been divided for security. A novel threshold based (any value of k and n) blockwide recovery of secret in progressive secret sharing has been introduced and analyzed for distributed environment. The proposed threshold block wise splitting using progressive visual secret sharing (T-BPVSS) achieves any general higher value of threshold for recovery of secret medical images. Proposed scheme is tested based on various parameters such as varying values of threshold for recovery of secret during enhanced security scenario, as well as changing dimensions of the images and introducing noise in the images. A detailed distributed computing recovery solution is also suggested for the original secret by using distribution technique of shares across the networks of computers. The scheme satisfies for perfect security condition in distributed environment using at least minimum decided threshold numbers of participants (k) before revealing any of the blocks of secret medical image.     

A secure lossless recovery for medical images based on image encoding and data self-embedding

To protect the security of data outsourced to the cloud, the tampers detection and recovery for outsourced image have aroused the concern of people. A secure tampering detection and lossless recovery for medical images (MI) using permutation ordered binary (POB) number system is proposed. In the proposed scheme, the region of interest (ROI) of MI is first extracted, and then, ROI is divided into some no-overlapping blocks, and image encoding is conducted on these blocks based on the better compression performance of JPEG-LS for medical image. After that, the generated compression data by all the blocks are divided into high 4-bit and low 4-bit planes, and shuffling and combination are used to generate two plane images. Owing to the substantial redundancies space in the compressed data, the data of each plane are spread to the size of the original image. Lastly, authentication data of two bits is obtained for every pixel and inserted into the pixel itself within the each plane, and the corresponding 10-bit data is transformed into the POB value of 8-bit. Furthermore, encryption is implemented on the above image to produce two shares which can be outsourced to the cloud server. The users can detect tampered part and recover original image when they down load the shares from the cloud. Extensive experiments on some ordinary medical image and COVID-19 image datasets show that the proposed approach can locate the tampered parts within the MI, and the original MI can be recovered without any loss even if one of the shares are totally destroyed, or two shares are tampered at the ration not more than 50%. Some comparisons and analysis are given to show the better performance of the scheme.

     

Machine learning in Magnetic Resonance Imaging: Image reconstruction

Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction.A wide range of approaches have been proposed, which can be applied in k-space and/or image-space.Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation.In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends.     

Évaluation de l’exactitude de latéralisation du foyer épileptogène par la tomographie par émission de positons au 18f-fluorodésoxyglucose dans le bilan préopératoire de l’épilepsie temporale : analyse visuelle simple et par index d’asymétrie inter-hémisphérique par soustraction d’images,analyse quantitative par statistical parametric mapping

IntroductionInter-ictal 18F-2-fluoro-deoxy-D-glucose-positron emission tomography (FDG-PET) plays a key role for the preoperative evaluation of patients with pharmacoresistant temporal lobe epilepsy. PET images are usually analyzed visually, a way that is reported to provide a high diagnostic value but that remains subjective, depending on the expertise and experience of the observer. By contrast, the voxel-based quantitative analyses, such as statistical parametric mapping (SPM), are objective and therefore, observer independent methods of analyses. In this study, the accuracy of the analyses of brain FDG-PET images to lateralize the temporal lobe epileptogenic zone was compared between: (1) a conventional visual method, (2) a quantitative SPM analysis, and (3) a visual analysis of inter-hemispheric asymmetry (IHA) obtained after images substraction.Materials and methodsFDG-PET scans of 31 patients presenting a severe temporal epilepsy and whom the temporal foci had been accurately lateralized (successful subsequent surgical treatment) were retrospectively analysed by (1) a consensual visual analysis from two experienced observers; (2) SPM analysis with voxel-wise comparisons of FDG-PET images of patients with those of age-matched healthy controls, using various statistical threshold (P) and cluster (k) values; and (3) visual assessment by the two same observers of images obtained for assessing the IHA. For this purpose, a flipped image was initially obtained by reversing in the left-right direction the FDG-PET images, which had been previously spacially normalized with the SPM template. Then, flipped and non-flipped images were substracted.ResultsThe temporal hypometabolic area was accurately identified: (1) by the conventional visual analysis in 87 % of patients and with a satisfactory interobserver reproducibility (interobserver Cohen's coefficient = 0.79); (2) by SPM analysis, in 90 % of patients (when using optimal thresholds of 0.01 for P value and of 50 voxels (400 mm³) for k value); and (3) with the visual analysis of IHA in 97 % of patients with an excellent interobserver reproductibility (interobserver Cohen's coefficient = 1).ConclusionIn patients presenting severe temporal epilepsy, visual assessment of FDG-PET images from IHA seems more accurate for lateralizing the epileptogenic temporal areas when compared with either conventional visual or quantitative SPM analyses. Moreover, this method is very easy to use in clinical practice, contrary to the quantitative method using SPM     

Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations—deformable and non-deformable sRBCs—utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R² = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring.     

Eye Tracking and Interhemispheric Interaction in the Distribution of Spatial Attention

The patterns of visual attention allocation were investigated in healthy subjects (n = 43) and patients with focal brain lesions (n = 17) using the original method developed for eye tracking in patients while memorizing a series of stimulatory image triplets. Two processes were estimated: delayed reproduction and recognition of stimuli in a series of consecutive visually similar distractors. In healthy subjects both processes correlated to a great extent (r = 0.6; p = 0.00001). The most significant disorders of voluntary verbal reproduction were observed when the left hemisphere of the brain was affected. The overall effectiveness of recognition in the case of brain damage decreased without significant dependence on the lateralization of the focus. Some correlation was observed between realized and remembered information and the patterns of visual fixations (concentrated on the semantic parts of the image or chaotically distributed in the space of stimulus exposure). Ineffective patterns of visual fixation in patients were more often observed in the area contralateral to the lesion. These contralateral stimuli were reproduced and recognized less efficiently in comparison with the central and ipsilateral images. Complete ignoring of the contralateral image in the triplet was observed both in the absence of visual fixation and in combination with the diffuse pattern.     

Model-based simulation of dynamic magnetic resonance imaging signals

This paper presents a model-based method to efficiently simulate dynamic magnetic resonance imaging signals. Using an analytical spatiotemporal object model, the method can approximate time-varying k-space signals such as those from objects in motion and/or during dynamic contrast enhancement. Both rigid-body and non-rigid-body motions can be simulated using the proposed method. In addition, it can simulate data with arbitrary data sampling order and/or non-uniform k-space trajectory. A set of simulated images were compared with real data acquired from a rat model on a 4.7 T scanner to verify the model. The efficient simulation method is expected to be useful for rapid testing of various imaging and image analysis algorithms such as image reconstruction, image registration, motion compensation, and kinetic parameter mapping.     

milRNApredictor: Genome-free prediction of fungi milRNAs by incorporating k-mer scheme and distance-dependent pair potential

MicroRNA-like small RNAs (milRNAs) with length of 21–22 nucleotides are a type of small non-coding RNAs that are firstly found in Neurospora crassa in 2010. Identifying milRNAs of species without genomic information is a difficult problem. Here, knowledge-based energy features are developed to identify milRNAs by tactfully incorporating k-mer scheme and distance-dependent pair potential. Compared with k-mer scheme, features developed here can alleviate the inherent curse of dimensionality in k-scheme once k becomes large. In addition, milRNApredictor built on novel features performs comparably to k-mer scheme, and achieves sensitivity of 74.21%, and specificity of 75.72% based on 10-fold cross-validation. Furthermore, for novel miRNA prediction, there exists high overlap of results from milRNApredictor and state-of-the-art mirnovo. However, milRNApredictor is simpler to use with reduced requirements of input data and dependencies. Taken together, milRNApredictor can be used to de novo identify fungi milRNAs and other very short small RNAs of non-model organisms.     

Short-term memory trace in rapidly adapting synapses of inferior temporal cortex

Visual short-term memory tasks depend upon both the inferior temporal cortex (ITC) and the prefrontal cortex (PFC). Activity in some neurons persists after the first (sample) stimulus is shown. This delay-period activity has been proposed as an important mechanism for working memory. In ITC neurons, intervening (nonmatching) stimuli wipe out the delay-period activity; hence, the role of ITC in memory must depend upon a different mechanism. Here, we look for a possible mechanism by contrasting memory effects in two architectonically different parts of ITC: area TE and the perirhinal cortex. We found that a large proportion (80%) of stimulus-selective neurons in area TE of macaque ITCs exhibit a memory effect during the stimulus interval. During a sequential delayed matching-to-sample task (DMS), the noise in the neuronal response to the test image was correlated with the noise in the neuronal response to the sample image. Neurons in perirhinal cortex did not show this correlation. These results led us to hypothesize that area TE contributes to short-term memory by acting as a matched filter. When the sample image appears, each TE neuron captures a static copy of its inputs by rapidly adjusting its synaptic weights to match the strength of their individual inputs. Input signals from subsequent images are multiplied by those synaptic weights, thereby computing a measure of the correlation between the past and present inputs. The total activity in area TE is sufficient to quantify the similarity between the two images. This matched filter theory provides an explanation of what is remembered, where the trace is stored, and how comparison is done across time, all without requiring delay period activity. Simulations of a matched filter model match the experimental results, suggesting that area TE neurons store a synaptic memory trace during short-term visual memory.     

Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning

PurposeLeukaemia is diagnosed conventionally by observing the peripheral blood and bone marrow smear using a microscope and with the help of advanced laboratory tests. Image processing-based methods, which are simple, fast, and cheap, can be used to detect and classify leukemic cells by processing and analysing images of microscopic smear. The proposed study aims to classify Acute Lymphoblastic Leukaemia (ALL) by Deep Learning (DL) based techniques.ProceduresThe study used Deep Convolutional Neural Networks (DNNs) to classify ALL according to WHO classification scheme without using any image segmentation and feature extraction that involves intense computations. Images from an online image bank of American Society of Haematology (ASH) were used for the classification.FindingsA classification accuracy of 94.12% is achieved by the study in isolating the B-cell and T-cell ALL images using a pretrained CNN AlexNet as well as LeukNet, a custom-made deep learning network designed by the proposed work. The study also compared the classification performances using three different training algorithms.ConclusionsThe paper detailed the use of DNNs to classify ALL, without using any image segmentation and feature extraction techniques. Classification of ALL into subtypes according to the WHO classification scheme using image processing techniques is not available in literature to the best of the knowledge of the authors. The present study considered the classification of ALL only, and detection of other types of leukemic images can be attempted in future research.     

Local homology recognition and distance measures in linear time using compressed amino acid alphabets

Methods for discovery of local similarities and estimation of evolutionary distance by identifying k-mers (contiguous subsequences of length k) common to two sequences are described. Given unaligned sequences of length L, these methods have O(L) time complexity. The ability of compressed amino acid alphabets to extend these techniques to distantly related proteins was investigated. The performance of these algorithms was evaluated for different alphabets and choices of k using a test set of 1848 pairs of structurally alignable sequences selected from the FSSP database. Distance measures derived from k-mer counting were found to correlate well with percentage identity derived from sequence alignments. Compressed alphabets were seen to improve performance in local similarity discovery, but no evidence was found of improvements when applied to distance estimates. The performance of our local similarity discovery method was compared with the fast Fourier transform (FFT) used in MAFFT, which has O(L log L) time complexity. The method for achieving comparable coverage to FFT is revealed here, and is more than an order of magnitude faster. We suggest using k-mer distance for fast, approximate phylogenetic tree construction, and show that a speed improvement of more than three orders of magnitude can be achieved relative to standard distance methods, which require alignments.     

Transition kinetics of the conversion of blue to purple bacteriorhodopsin upon magnesium binding

Magnesium binding to cation-depleted blue bacteriorhodopsin (b-bR) was studied spectrophotometrically as well as by following stopped-flow kinetics. There exist three kinetically different steps in the binding process, yielding purple bacteriorhodopsin (p-bR). Since only the firtst step is dependent on the concentration of the reactants, the reaction scheme

can be proposed as the simplest model, with MgbR being the first intermediate and ΣI denoting a set of successive intermediates. According to this model k₁, k₋₁ and k₂ are calculated to be 2.8 × 10⁴ M⁻¹ · s⁻¹, 5.0 × 10 s⁻¹ and 1 × 10⁻² s⁻¹, respectively.     

THEORY AND MEASUREMENT OF VISUAL MECHANISMS : X. MODIFICATIONS OF THE FLICKER RESPONSE CONTOUR,AND THE SIGNIFICANCE OF THE AVIAN PECTEN

1. When there is projected on the retina (man, monocularly) the shadow of a grid which forms a visual field in several distinct pieces (not including the fovea in the present tests), the ordinary properties of the flicker recognition contour (F vs. log I) as a function of the light-time cycle fraction (t_L) can be markedly disturbed. In the present experiments flicker was produced by the rotation of a cylinder with opaque vertical stripes. In the absence of such a grid shadow the "cone" segments of the contours form a set in which F_max. and the abscissa of inflection are opposite but rectilinear functions of t_L, while the third parameter of the probability integral (σ''_{log I}) remains constant. This is the case also with diverse other animals tested. In the data with the grid, however, analysis shows that even for low values of t_L (up to 0.50) there occurs an enhancement of the production of elements of neural effect, so that F_max. rises rather than falls as ordinarily with increase of t_L, although σ''_{log I} stays constant and hence the total number of acting units is presumed not to change. This constitutes valid evidence for neural integration of effects due to the illumination of separated retinal patches. Beginning at t_L = 0.75, and at 0.90, the slope of the "cone" curve is sharply increased, and the maximum F is far above its position in the absence of the grid. The decrease of σ''_{log I} (the slope constant) signifies, in terms of other information, an increase in the number of acting cone units. The abscissa of inflection is also much lowered, relatively, whereas without the grid it increases as t_L is made larger. These effects correspond subjectively to the fact that at the end-point flicker is most pronounced, on the "cone" curve, along the edges of the grid shadow where contrast is particularly evident with the longer light-times. The "rod" portion of the F - log I contour is not specifically affected by the presence of the grid shadow. Its form is obtainable at t_L = 0.90 free from the influence of summating "cone" contributions, because then almost no overlapping occurs. Analysis shows that when overlapping does occur a certain number of rod units are inhibited by concurrent cone excitation, and that the mean contribution of elements of neural action from each of the non-inhibited units is also reduced to an extent depending on the degree of overlap. The isolated "rod" curve at t_L = 0.90 is quite accurately in the form of a probability integral. The data thus give a new experimental proof of the occurrence of two distinct but interlocking populations of visual effects, and experimentally justify the analytical procedures which have been used to separate them. 2. The changing form of the F - log I contour as a function of t_L, produced in man when the illuminated field is divided into parts by a shadow pattern, is normally found with the bird Taeniopygia castenotis (Gould), the zebra finch. The retina has elements of one general structural type (cones), and the F - log I contour is a simplex symmetrical probability integral. The eye of this bird has a large, complex, and darkly pigmented pecten, which casts a foliated shadow on the retina. The change in form of the F - log I curve occurs with t_L above 0,50, and at t_L = 0.90 is quite extreme. It is more pronounced than the one that is secured in the human data with the particular grid we have used, but there is no doubt that it could be mimicked completely by the use of other grids. The increase of flicker acuity due to the pecten shadow is considerable, when the dark spaces are brief relative to the light. The evidence thus confirms the suggestion (Menner) drawn from comparative natural history that the visual significance of the avian pecten might be to increase the sensory effect of small moving images. It is theoretically important that (as in the human experiment) this may be brought about by an actual decrease of effective retinal area illuminated. It is also significant theoretically that despite the presence of shadows of pecten or of grid, and of the sensory influences thus introduced, the probability integral formulation remains effective.     

High-Throughput Phenotyping of Chlamydomonas Swimming Mutants Based on Nanoscale Video Analysis

Studies on biflagellated algae Chlamydomonas reinhardtii mutants have resulted in significant contributions to our understanding of the functions of cilia/flagella components. However, visual inspection conducted under a microscope to screen and classify Chlamydomonas swimming requires considerable time, effort, and experience. In addition, it is likely that identification of mutants by this screening is biased toward individual cells with severe swimming defects, and mutants that swim slightly more slowly than wild-type cells may be missed by these screening methods. To systematically screen Chlamydomonas swimming mutants, we have here developed the cell-locating-with-nanoscale-accuracy (CLONA) method to identify the cell position to within 10-nm precision through the analysis of high-speed video images. Instead of analyzing the shape of the flagella, which is not always visible in images, we determine the position of Chlamydomonas cell bodies by determining the cross-correlation between a reference image and the image of the cell. From these positions, various parameters related to swimming, such as velocity and beat frequency, can be accurately estimated for each beat cycle. In the examination of wild-type and seven dynein arm mutants of Chlamydomonas, we found characteristic clustering on scatter plots of beat frequency versus swimming velocity. Using the CLONA method, we have screened 38 Chlamydomonas strains and detected believed-novel motility-deficient mutants that would be missed by visual screening. This CLONA method can automate the screening for mutants of Chlamydomonas and contribute to the elucidation of the functions of motility-associated proteins.     

High-Throughput Phenotyping of Chlamydomonas Swimming Mutants Based on Nanoscale Video Analysis

Studies on biflagellated algae Chlamydomonas reinhardtii mutants have resulted in significant contributions to our understanding of the functions of cilia/flagella components. However, visual inspection conducted under a microscope to screen and classify Chlamydomonas swimming requires considerable time, effort, and experience. In addition, it is likely that identification of mutants by this screening is biased toward individual cells with severe swimming defects, and mutants that swim slightly more slowly than wild-type cells may be missed by these screening methods. To systematically screen Chlamydomonas swimming mutants, we have here developed the cell-locating-with-nanoscale-accuracy (CLONA) method to identify the cell position to within 10-nm precision through the analysis of high-speed video images. Instead of analyzing the shape of the flagella, which is not always visible in images, we determine the position of Chlamydomonas cell bodies by determining the cross-correlation between a reference image and the image of the cell. From these positions, various parameters related to swimming, such as velocity and beat frequency, can be accurately estimated for each beat cycle. In the examination of wild-type and seven dynein arm mutants of Chlamydomonas, we found characteristic clustering on scatter plots of beat frequency versus swimming velocity. Using the CLONA method, we have screened 38 Chlamydomonas strains and detected believed-novel motility-deficient mutants that would be missed by visual screening. This CLONA method can automate the screening for mutants of Chlamydomonas and contribute to the elucidation of the functions of motility-associated proteins.     

Truthful Channel Sharing for Self Coexistence of Overlapping Medical Body Area Networks

As defined by IEEE 802.15.6 standard, channel sharing is a potential method to coordinate inter-network interference among Medical Body Area Networks (MBANs) that are close to one another. However, channel sharing opens up new vulnerabilities as selfish MBANs may manipulate their online channel requests to gain unfair advantage over others. In this paper, we address this issue by proposing a truthful online channel sharing algorithm and a companion protocol that allocates channel efficiently and truthfully by punishing MBANs for misreporting their channel request parameters such as time, duration and bid for the channel. We first present an online channel sharing scheme for unit-length channel requests and prove that it is truthful. We then generalize our model to settings with variable-length channel requests, where we propose a critical value based channel pricing and preemption scheme. A bid adjustment procedure prevents unbeneficial preemption by artificially raising the ongoing winner’s bid controlled by a penalty factor λ. Our scheme can efficiently detect selfish behaviors by monitoring a trust parameter α of each MBAN and punish MBANs from cheating by suspending their requests. Our extensive simulation results show our scheme can achieve a total profit that is more than 85% of the offline optimum method in the typical MBAN settings.     

Linking species richness curves from non-contiguous sampling to contiguous-nested SAR: An empirical study

The species–area relationship (SAR) is one of the few generalizations in ecology. However, many different relationships are denoted as SARs. Here, we empirically evaluated the differences between SARs derived from nested-contiguous and non-contiguous sampling designs, using plants, birds and butterflies datasets from Great Britain, Greece, Massachusetts, New York and San Diego. The shape of SAR depends on the sampling scheme, but there is little empirical documentation on the magnitude of the deviation between different types of SARs and the factors affecting it. We implemented a strictly nested sampling design to construct nested-contiguous SAR (SA_CR), and systematic nested but non-contiguous, and random designs to construct non-contiguous species richness curves (SA_SRs for systematic and SACs for random designs) per dataset. The SA_CR lay below any SA_SR and most of the SACs. The deviation between them was related to the exponent f of the power law relationship between sampled area and extent. The lower the exponent f, the higher was the deviation between the curves. We linked SA_CR to SA_SR and SAC through the concept of “effective” area (A_e), i.e. the nested-contiguous area containing equal number of species with the accumulated sampled area (A_S) of a non-contiguous sampling. The relationship between effective and sampled area was modeled as log(A_e) = klog(A_S). A Generalized Linear Model was used to estimate the values of k from sampling design and dataset properties. The parameter k increased with the average distance between samples and with beta diversity, while k decreased with f. For both systematic and random sampling, the model performed well in predicting effective area in both the training set and in the test set which was totally independent from the training one. Through effective area, we can link different types of species richness curves based on sampling design properties, sampling effort, spatial scale and beta diversity patterns.     

Improvement of the contrast of electron microscope images by the optical shadow method

Amplitude contrast of images of weak phase and amplitude objects can be strengthened almost twice as compared to standard light-pole contrast by means of shadow method of image formation without special contrasting of the objects. In spite of the contour effects (electron-microscopic attenuation) appearing at image formation by asymmetrical shadow method, the quantitative interpretation of such image is quite possible. The symmetrical shadow image (image with cone illumination) is more complexly realized than the asymmetric one, but it has some additional advantages. Particularly efficient suppression of background noises is there possible. Several symmetrical shadow images can be synthetized by the differential method with or without colour coding into the final image with the signal/noise ratio increased by an order.     

Intérêt de l’imagerie hybride TEMP/TDM pour la scintigraphie des récepteurs de la somatostatine dans le bilan des tumeurs endocrines gastro-entéro-pancréatiques

In this work, we have evaluated the potential of image fusion and attenuation correction (AC) of SPECT-CT imaging for the assessment of gastro-entero-pancreatic endocrine tumors by somatostatin receptor scintigraphy (SRS).MethodAfter optimisation of acquisition and reconstruction parameters, we have evaluated, in a prospective study, SRS performed over a period of one year. We have compared visual interpretations of planar and tomographic images versus SPECT/CT images to determine if anatomical localisation and diagnostic contributions are improved. In a semi-quantitative analysis of pathological foci, we have measured maximal intensity values (T_max), tumour to background ratios (T/B) and tumour contrasts (Ct) with and without AC.ResultsIn 25 SRS, visual analysis has shown anatomical localisation improvements in 60% of cases (CI95%, 39–79) and diagnostic improvements in 64% of cases (CI95%, 43–82). Doubtful foci proportion changed from 44 to 11%. In the semi-quantitative analysis of 41 pathological foci, Wilcoxon matched-pairs tests showed significantly higher T_max, T/B and Ct values after AC.ConclusionSPECT/CT imaging improves diagnostic quality of SRS thanks to a better foci localisation and a better lesional contrast in the image.     

Nitrofuran induced mutagenesis and error prone repair in Escherichia coli

The binding of cis(c)- and trans(t)-Pt(NH₃)₂Cl₂ to DNA at platinum/DNA-nucleotide ratios (R_i) of 0.1 or less has been studied by means of radioactive ^195mPt-labeled compounds. Kinetic data are consistent with the following scheme:

At 25°C and pH 5–6 in 5 mM NaClO₄, the values for the rate constants in the above scheme for the c-isomer are k₂ = 2.2 × 10^?5 sec^?1, k₇ = 0.32 (sec M)^?1, and k₈ = 143 (sec M)^?1; for the t-isomer the values are k₂ < 0.5 × 10^?5 sec^?1 and k₇ = 0.95 (sec M)^?1. Platinum-DNA adducts do not undergo detectable exchange after 3 days at 37°C, indicating the absence of a dynamic equillibrium. For both isomers the rate of binding is the same for single- and double-stranded DNA. The conclusions derived from Ag⁺ and H⁺ titration studies are consistent with binding at guanine N(7) for R_i < 0.1. The reaction rate is competitively inhibited by various salts and buffers and is suppressed by raising the pH (50% inhibition of initial rates at pH 7.3). At 37°C and pH 7 in 0.15 M NaCl, 6–8% of both the c- and t-isomers bind to DNA in 24 h, suggesting that both compounds should bind to DNA under biological conditions.