No abstract

We study the spectral and temporal properties of the black hole X-ray transient binary MAXI J1820+070 during the 2018 outburst with Insight-HXMT observations. The outburst of MAXI J1820+070 can be divided into three intervals. For the two intervals of the outburst, we find that low-energy (below 140 keV) photos lag high-energy (140-170 keV) ones, while in the decay of the outburst, high-energy photons lag low-energy photons, both with a time scale the order of days. Based on these results, the canonical hysteresis effect of the ’q’ shape in the hardness-intensity diagram can be reformed into a roughly linear shape by taking into account the lag corrections between different energy bands. Time analysis shows that the high-frequency break of hard X-rays, derived from the power density spectrum of the first interval of the outburst is in general larger and more variable than that of soft X-rays. The spectral fitting shows that the coverage fraction of the hard X-rays drops sharply at the beginning of the outburst to around 0.5, then increases slightly. The coverage fraction drops to roughly zero once the source steps into soft state and increases gradually to unity when the source returns to low hard state. We discuss the possible overall evolution scenario of corona hinted from these discoveries.

No abstract

Purpose: Marker‐based registration, needed especially when there is no sufficient bony anatomy for adequate match, plays an important role in the image guided radiation therapy (IGRT) systems, of which the accuracy of registration greatly depends on that of the location of the markers embed. Since the manual marker detection has many limits for its time consuming and labor intensive, it is essential to enable an automatic detection of makers to help to cut down the human error in registration as well as to get a speedup of the registration process, making IGRT systems more efficient. Methods: In this study, we present a simple and efficient clustering method based on the k‐means clustering algorithm, which we call threshold‐based clustering. This method is established in three procedures. First, we got a set of segmented 3D CT images using binary thresholding method since the value of the markers is far different from that of the CT images. Second, the calculation was done by comparing the spacial distance between every two possible marker candidates to make marker points with similar intensity clustered. Finally, the centers of markers can be obtained after a given number of iteration time which can be changed according to the prior estimated number of markers. Results: This method has been tested on 46 CT slices with a size of 512*512. Markers were successfully identified in at least 99.00%. It took less than a second to detect three markers with the average location accuracy of 0.56mm compared to 1.50mm for the conventional manual technique. Conclusions: It has been testified that the method proposed can accurately detect the location of the markers within 3D CT images. Our results demonstrate the subvoxel accuracy can be achieved completely automatically which makes the method well suited for clinical applications. Supported by the National Natural Science Foundation under grant No.30900386 and the Anhui Provincial Natural Science Foundation under grant No. 090413095 and 11040606Q55