Selective excitation enables encoding and measurement of multiple diffusion parameters in a single experiment

Band selectivity to address specific resonances in a spectrum enables one to encode individual settings for diffusion experiments. In a single experiment, this could include different gradient strengths (enabling coverage of a larger range of diffusion constants), different diffusion delays, or different gradient directions (enabling anisotropic diffusion measurement). In this report, a selective variant of the bipolar pulsed gradient eddy current delay (BPP-LED) experiment, enabling selective encoding of three resonances, was implemented. As proof of principle, the diffusion encoding gradient amplitude was assigned a range dependent on the selected signal, thereby allowing the extraction of the diffusion coefficient for water and a tripeptide (Met-Ala-Ser) with optimal settings in a single experiment.

Diffusion and quantification of diffusion of prostate cancer

For assessing a cancer treatment, and for detecting and characterizing cancer, Diffusion-weighted imaging (DWI) is commonly used. The key in DWI’s use extracranially has been due to the emergence of of high-gradient amplitude and multichannel coils, parallelimaging, and echo-planar imaging. The benefit has been fewer motion artefacts and high-quality prostate images.Recently, new techniques have been developed to improve the signal-to-noise ratio of DWI with fewer artefacts, allowing an increase in spatial resolution. For apparent diffusion coefficient quantification, non-Gaussian diffusion models have been proposed as additional tools for prostate cancer detection and evaluation of its aggressiveness. More recently, radiomics and machine learning for prostate magnetic resonance imaging have emerged as novel techniques for the non-invasive characterisation of prostate cancer. This review presents recent developments in prostate DWI and discusses its potential use in clinical practice.

Selective excitation enables encoding and measurement of multiple diffusion parameters in a single experiment

Band selectivity to address specific resonances in a spectrum enables one to encode individual settings for diffusion experiments. In a single experiment, this could include different gradient strengths (enabling coverage of a larger range of diffusion constants), different diffusion delays, or different gradient directions (enabling anisotropic diffusion measurement). In this report a selective variant of the bipolar pulsed gradient, eddy-current delay (BPP-LED) experiment enabling selective encoding of three resonances was implemented. As proof-of-principle, the diffusion encoding gradient amplitude was assigned a range dependent on the selected signal, thereby allowing the extraction of the diffusion coefficient for water and a tripeptide (Met-Ala-Ser) with optimal settings in a single experiment.

Multi-exposure X-ray image fusion quality evaluation based on CSF and gradient amplitude similarity

Due to the limitation of dynamic range of the imaging device, the fixed-voltage X-ray images often produce overexposed or underexposed regions. Some structure information of the composite steel component is lost. This problem can be solved by fusing the multi-exposure X-ray images taken by using different voltages in order to produce images with more detailed structures or information. Due to the lack of research on multi-exposure X-ray image fusion technology, there is no evaluation method specially for multi-exposure X-ray image fusion. For the multi-exposure X-ray fusion images obtained by different fusion algorithms may have problems such as the detail loss and structure disorder. To address these problems, this study proposes a new multi-exposure X-ray image fusion quality evaluation method based on contrast sensitivity function (CSF) and gradient amplitude similarity. First, with the idea of information fusion, multiple reference images are fused into a new reference image. Next, the gradient amplitude similarity between the new reference image and the test image is calculated. Then, the whole evaluation value can be obtained by weighting CSF. In the experiments of MEF Database, the SROCC of the proposed algorithm is about 0.8914, and the PLCC is about 0.9287, which shows that the proposed algorithm is more consistent with subjective perception in MEF Database. Thus, this study demonstrates a new objective evaluation method, which generates the results that are consistent with the subjective feelings of human eyes.

A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network

Image semantic data have multilevel feature information. In the actual segmentation, the existing segmentation algorithms have some limitations, resulting in the fact that the final segmentation accuracy is too small. To solve this problem, a segmentation algorithm of image semantic sequence data based on graph convolution network is constructed. The graph convolution network is used to construct the image search process. The semantic sequence data are extracted. After the qualified data points are accumulated, the gradient amplitude forms complete rotation field and no scatter field in the diffusion process, which enhances the application scope of the algorithm, controls the accuracy of the segmentation algorithm, and completes the construction of the data segmentation algorithm. After the experimental dataset is prepared and the semantic segmentation direction is defined, we compare our method with four methods. The results show that the segmentation algorithm designed in this paper has the highest accuracy.

Estimating Effectiveness of Some Methods for Detecting Edge of Potential Field Sources

This paper presents a comparative study of effectiveness of edge detection methods such as total horizontal gradient, analytic signal amplitude, tilt angle, gradient amplitude of tilt angle, theta map, horizontal tilt angle, tilt angle of total horizontal gradient, tilt angle of analytic signal, improved theta map, and total horizontal gradient of improved tilt angle. The effectiveness of each method was estimated on synthetic magnetic data and synthetic gravity anomaly data with and without noise. The obtained results show that the tilt angle of gradient amplitude can detect all the edges more clearly and precisely. The applicability of each method is demonstrated on the aeromagnetic anomaly data from the Zhurihe region of Northeast China, and Bouguer gravity anomaly data from a region of North Vietnam. The results computed by the tilt angle of horizontal gradient were also in accord with the geologic structures of the areas.

A NEW IRIS LOCATION METHOD

A new iris location method is presented in this paper. When locating the iris inner boundary, a method for locating the iris inner boundary based on the variation coefficient and multiple disk-structural elements is proposed. The variation coefficient of the iris image is first calculated to be used for the image gray stretch, then the mathematical morphology operations with multiple disk-structural elements are applied to tackle the binary iris image, finally the connected region in line with geometric features of the circle is picked up and its center and radius are calculated, namely for obtaining the iris inner boundary. When positioning the iris outer boundary, a method for locating the iris outer boundary based on the variation coefficient and the gradient amplitude is presented. First, the Gaussian function is employed to filter the iris image using the variation coefficient as its standard deviation, then for each pixel on a circle which is centered at each neighborhood pixel near the circle enter of the iris inner boundary and has a radius with greater than that of the inner boundary, the radial gradient amplitude and tangential gradient amplitude of are computed, finally the iris outer boundary is determined on the maximum of the tangential gradient amplitude. The experiments reveal that this proposed method is effective, and has a fairly simple implementation, a low computational load, a fast location and good accuracy.

Automatic Detection of Amplitude-Distorted Samples from Clipped Seismic Waveforms

Seismic waveforms are essential for seismology but are clipped when their actual amplitudes are too high to be faithfully recorded by seismometers. The clipping effects are popular for both big earthquakes and small earthquakes within a short epicentral distance. Here, we illustrate potential risks of direct usage of clipped waveforms by examining the frequency leakage and show the failure of bandpass filtering for different clipping levels; then we summarize two characteristics of clipped records: (1) The temporal gradient is unusually large around the clipped segment compared with the unclipped portions, and (2) the clipped samples cluster into one segment or several if many samples are involved. Next, we propose three criteria for distinguishing clipped samples from the perfect samples based on these two characteristics. Finally, we design a numerical algorithm for automatic detection of clipped samples using constraints on the gradient, amplitude, and gradient-varying range. Numerical experiments show the excellent performance of our algorithm on automatically detecting the clipped samples. Our algorithm seamlessly integrates all necessary constraints for both flat-top type and back-to-zero type and thus can correctly recognize these two types simultaneously; in addition, it is basically data driven and thus can work well without considering seismometer configuration and instrument type, which would be helpful for real-time detection of clipped records without interruption from human operations. As a robust and swift tool of automatic detection on amplitude-clipped samples, our algorithm could identify most typical clipped records and reduce potential risks due to using unrecognizable clipped waveforms; furthermore, it would be helpful for fast detection and possible restoration of clipped waveforms in the presence of huge volumes of data.

Dullard-mediated Smad1/5/8 inhibition controls mouse cardiac neural crest cells condensation and outflow tract septation

The establishment of separated pulmonary and systemic circulation in vertebrates, via cardiac outflow tract (OFT) septation, is a sensitive developmental process accounting for 10% of all congenital anomalies. Neural Crest Cells (NCC) colonising the heart condensate along the primitive endocardial tube and force its scission into two tubes. Here, we show that NCC aggregation progressively decreases along the OFT distal-proximal axis following a BMP signalling gradient. Dullard, a nuclear phosphatase, tunes the BMP gradient amplitude and prevents NCC premature condensation. Dullard maintains transcriptional programs providing NCC with mesenchymal traits. It attenuates the expression of the aggregation factor Sema3c and conversely promotes that of the epithelial-mesenchymal transition driver Twist1. Altogether, Dullard-mediated fine-tuning of BMP signalling ensures the timed and progressive zipper-like closure of the OFT by the NCC and prevents the formation of a heart carrying the congenital abnormalities defining the tetralogy of Fallot.