A New Type of Transient High-Energy Source in the Direction of the Galactic Centre

Sources of high-energy (greater than 20 keV) bursts fall into two distinct types: the non-repeating gamma-ray bursters, several thousand of which have been detected but whose origin remains unknown, and the soft gamma-ray repeaters (SGRs), of which there are only three. The SGRs are known to be associated with supernova remnants, suggesting that the burst events most probably originate from young neutron stars. Here we report the detection of a third type of transient high-energy source. On 2 December 1995, we observed the onset of a sequence of hard X-ray bursts from a direction close to that of the Galactic Center. The interval between bursts was initially several minutes, but after two days, the burst rate had dropped to about one per hour and has been largely unchanged since then. More than 1,000 bursts have now been detected, with remarkably similar light curves and intensities; this behaviour is unprecendented among transient X-ray and gamma-ray sources. We suggest that the origin of these bursts might be related to the spasmodic accretion of material onto a neutron star

On the Association of γ\gamma-Ray Bursts with Supernovae

The recent discovery of a supernova (SN 1998bw) seemingly associated with GRB~980425 adds a new twist to the decades-old debate over the origin of gamma-ray bursts. To investigate the possibility that some (or all) bursts are associated with supernovae, we performed a systematic search for temporal/angular correlations using catalogs of BATSE and BATSE/{\it Ulysses} burst locations. We find no associations with any of the precise BATSE/ Ulysses locations, which allows us to conclude that the fraction of high-fluence gamma-ray bursts associated with known supernovae is small ($<$0.2%). For the more numerous weaker bursts, the corresponding limiting fraction of 2.5% is far less constraining due to the imprecise locations of these events. This fraction (2.5%) of bursts corresponds to $\sim$30% of the recent supernovae used as a comparison data set. Thus, although we find no significant evidence to support a burst/supernova association, the possibility cannot be excluded for weak bursts

The Interplanetary Network Supplement to the BATSE 5B Catalog of Cosmic Gamma-Ray Bursts

We present Interplanetary Network (IPN) localization information for 343 gamma-ray bursts observed by the Burst and Transient Source Experiment (BATSE) between the end of the 4th BATSE catalog and the end of the Compton Gamma-Ray Observatory (CGRO) mission, obtained by analyzing the arrival times of these bursts at the Ulysses, Near Earth Asteroid Rendezvous (NEAR), and CGRO spacecraft. For any given burst observed by CGRO and one other spacecraft, arrival time analysis (or "triangulation") results in an annulus of possible arrival directions whose half-width varies between 11 arcseconds and 21 degrees, depending on the intensity, time history, and arrival direction of the burst,as well as the distance between the spacecraft. This annulus generally intersects the BATSE error circle, resulting in an average reduction of the area of a factor of 20. When all three spacecraft observe a burst, the result is an error box whose area varies between 1 and 48000 square arcminutes, resulting in an average reduction of the BATSE error circle area of a factor of 87.Comment: 60 pages, 8 figures. To be submitted to the Astrophysical Journal Supplement Series in conjunction with the BATSE 5B catalog. Revised version accepted for publication in ApJS 196, 1, 201

The Interplanetary Network Supplement to the BeppoSAX Gamma-Ray Burst Catalogs

Between 1996 July and 2002 April, one or more spacecraft of the interplanetary network detected 787 cosmic gamma-ray bursts that were also detected by the Gamma-Ray Burst Monitor and/or Wide-Field X-Ray Camera experiments aboard the BeppoSAX spacecraft. During this period, the network consisted of up to six spacecraft, and using triangulation, the localizations of 475 bursts were obtained. We present the localization data for these events.Comment: 89 pages, 3 figures. Submitted to the Astrophysical Journal Supplement Serie

The Interplanetary Network Supplement to the BATSE Catalogs of Untriggered Cosmic Gamma Ray Bursts

We present Interplanetary Network (IPN) detection and localization information for 211 gamma-ray bursts (GRBs) observed as untriggered events by the Burst and Transient Source Experiment (BATSE), and published in catalogs by Kommers et al. (2001) and Stern et al. (2001). IPN confirmations have been obtained by analyzing the data from 11 experiments. For any given burst observed by BATSE and one other distant spacecraft, arrival time analysis (or ``triangulation'') results in an annulus of possible arrival directions whose half-width varies between 14 arcseconds and 5.6 degrees, depending on the intensity, time history, and arrival direction of the burst, as well as the distance between the spacecraft. This annulus generally intersects the BATSE error circle, resulting in a reduction of the area of up to a factor of ~650. When three widely separated spacecraft observed a burst, the result is an error box whose area is as much as 30000 times smaller than that of the BATSE error circle. Because the IPN instruments are considerably less sensitive than BATSE, they generally did not detect the weakest untriggered bursts, but did detect the more intense ones which failed to trigger BATSE when the trigger was disabled. In a few cases, we have been able to identify the probable origin of bursts as soft gamma repeaters. The vast majority of the IPN-detected events, however, are GRBs, and the confirmation of them validates many of the procedures utilized to detect BATSE untriggered bursts.Comment: Minor revisions. Accepted for publication in the Astrophysical Journal Supplement Series, February 200

An X-ray Pulsar with a Superstrong Magnetic Field in the Soft Gamma-Ray Repeater SGR1806-20

Soft gamma-ray repeaters (SGRs) emit multiple, brief (approximately O.1 s) intense outbursts of low-energy gamma-rays. They are extremely rare; three are known in our galaxy and one in the Large Magellanic Cloud. Two SGRs are associated with young supernova remnants (SNRs), and therefore most probably with neutron stars, but it remains a puzzle why SGRs are so different from 'normal' radio pulsars. Here we report the discovery of pulsations in the persistent X-ray flux of SGR1806-20, with a period of 7.47 s and a spindown rate of 2.6 x 10(exp -3) s/yr. We argue that the spindown is due to magnetic dipole emission and find that the pulsar age and (dipolar) magnetic field strength are approximately 1500 years and 8 x 10(exp 14) gauss, respectively. Our observations demonstrate the existence of 'magnetars', neutron stars with magnetic fields about 100 times stronger than those of radio pulsars, and support earlier suggestions that SGR bursts are caused by neutron-star 'crust-quakes' produced by magnetic stresses. The 'magnetar' birth rate is about one per millenium, a substantial fraction of that of radio pulsars. Thus our results may explain why some SNRs have no radio pulsars

Preoperative Brain Tumor Imaging:Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.publishedVersio

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61\% Dice score, and the best classification performance was about 80\% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.Comment: 13 pages, 4 figures, 4 table