Effect of quantitative values on shortened acquisition duration in brain tumor 11C-methionine PET/CT

Background The amount of signal decreases when the acquisition duration is shortened. However, it is not clear how this affects the quantitative values. This study aims to clarify the effect of acquisition time shortening in brain tumor PET/CT using 11C-methionine on the quantitative values. Method This study was a retrospective analysis of 30 patients who underwent clinical 11C-methionine PET/CT examination. PET images were acquired in list mode for 10 min. PET images of acquisition duration from 1 to 10 min with 1-min step were reconstructed. We examined the effect on the quantitative values of acquisition duration. We placed a volume of interest to include the entire tumor and regions of interest in the shape of a large crescent in the contralateral hemisphere in 5 contiguous axial slices as normal tissue. Quantitative values examined were maximum, peak, and mean standardized uptake values (SUVmax, SUVpeak, SUVmean), metabolic tumor volume (MTV), and maximum tumor to normal tissue ratio (TNRmax), with each duration compared to that with 10 min. Results SUVmax, MTV, and TNRmax showed the highest values due to the effects of statistical noise when the acquisition time was 1 min. These values were stable when the acquisition duration was > 6 min. SUVpeak and SUVmean showed mostly consistent values regardless of duration. Conclusions SUVmax, MTV, and TNRmax are affected by acquisition time. If the acquisition duration was > 6 min, the fluctuation could be suppressed within 5% in these quantitative values. However, SUVpeak was suggested to be a robust index regardless of the acquisition duration.

AutoScanJ: A Suite of ImageJ Scripts for Intelligent Microscopy

We developed AutoscanJ, a suite of ImageJ scripts enabling to image targets of interest by automatically driving a motorized microscope at the corresponding locations. For live samples, our software can sequentially detect biological events from their onset and further image them at high resolution, an action that would be impractical by user operation. For fixed samples, the software can dramatically reduce the amount of data acquired and the acquisition duration in situations where statistically few targets of interest are observed per field of view. AutoScanJ is compatible with motorized fluorescence microscopes controlled by Leica LAS AF/X or Micro-Manager. The software is straightforward to set up and new custom image analysis workflows to detect targets of interest can be simply implemented and shared with minimal efforts as independent ImageJ macro functions. We illustrate five different application scenarios with the system ranging from samples fixed on micropatterned surfaces to live cells undergoing several rounds of division. The target detection functions for these applications are provided and can be used as a starting point and a source of inspiration for new applications. Overall, AutoScanJ helps to optimize microscope usage by autonomous operation, and it opens up new experimental avenues by enabling the real-time detection and selective imaging of transient events in live microscopy.

Yttrium-90 Quantitative Phantom Study Using Digital Photon Counting PET

Background: PET imaging of 90Y-microspheres distribution following radioembolisation is a challenging task due to the count-starved statistics from the low branching ratio producing e+/e- pairs during 90Y decay. The recent PET systems using silicon photo-multipliers technology has shown better 90Y image quality compared to photo-multiplier tubes. The aim of the present study was to quantitatively evaluate the impact of 90Y imaging conditions and reconstruction parameters on the dosimetry calculations using a digital photon counting PET.Methods: Quantitative PET and dosimetry accuracy were evaluated using two uniform cylindrical phantoms specific for PET calibration validation. A body phantom with an 9:1 hot sphere-to-background ratio was scanned at different activity concentrations of 90Y. Reconstructions were performed using OSEM algorithm with varying parameters. Time-of-flight and point-spread function modellings were included in all reconstructions. Absorbed dose calculations were carried out using Voxel S-Values convolution and were compared to reference Monte Carlo simulations. Dose-volume histograms and root-mean-square deviations were used to evaluate reconstruction parameter sets. Thanks to listmode data, datasets for phantoms and patients were rebinned into varying lengths of time to assess the influence of acquisition duration on the calculation of absorbed dose. Results: A 2 mm full width at half maximum post-reconstruction Gaussian filter size can be used for image reconstruction, keeping the same accuracy as when no filter is applied for dosimetry purposes and reducing noise. Larger filter sizes should not be used. An acquisition duration of more than 10 min/bed reduces image noise but has no significant impact in the quantification of phantom and patient data for the digital photon counting PET. 3 iterations with 10 subsets was found suitable for large spheres whereas 1 iteration with 30 subsets could improve dosimetry for smaller spheres. Conclusion: The choice of iterations and subsets combination depends on the size of the spheres. However, one should be careful on this choice, depending on the imaging conditions and setup. This study can be useful in this choice for future studies for more accurate 90Y post-dosimetry using a digital photon counting PET.

Robust Bayesian Joint Inversion of Gravimetric and Muographic Data for the Density Imaging of the Puy de Dôme Volcano (France)

Imaging the internal structure of volcanoes helps highlighting magma pathways and monitoring potential structural weaknesses. We jointly invert gravimetric and muographic data to determine the most precise image of the 3D density structure of the Puy de Dôme volcano (Chaîne des Puys, France) ever obtained. With rock thickness of up to 1,600 m along the muon lines of sight, it is, to our knowledge, the largest volcano ever imaged by combining muography and gravimetry. The inversion of gravimetric data is an ill-posed problem with a non-unique solution and a sensitivity rapidly decreasing with depth. Muography has the potential to constrain the absolute density of the studied structures but the use of the method is limited by the possible number of acquisition view points, by the long acquisition duration and by the noise contained in the data. To take advantage of both types of data in a joint inversion scheme, we develop a robust method adapted to the specificities of both the gravimetric and muographic data. Our method is based on a Bayesian formalism. It includes a smoothing relying on two regularization parameters (an a priori density standard deviation and an isotropic correlation length) which are automatically determined using a leave one out criterion. This smoothing overcomes artifacts linked to the data acquisition geometry of each dataset. A possible constant density offset between both datasets is also determined by least-squares. The potential of the method is shown using the Puy de Dôme volcano as case study as high quality gravimetric and muographic data are both available. Our results show that the dome is dry and permeable. Thanks to the muographic data, we better delineate a trachytic dense core surrounded by a less dense talus.

ACQUISITION COMPLETION OR ABANDONMENT: The effect of revealed comparative advantage in the M&A pre-integration process

This paper explores the effect of revealed comparative advantage in the M&A pre-integration process. Revealed comparative advantage reflects the advantage of a particular industry in trade compared to other industries. It is measured by the share of a sector’s exports in the overall country-wide exports, compared to the share of that sector’s exports in the total exports of a group of countries. In this study, we examine whether revealed comparative advantage could determine the completion likelihood of an M&A deal and the duration of M&A pre-integration process. A binary logistic regression model and a multiple regression model were performed with a sample of 260 mergers and acquisitions to test for the possible relationships. The evidence demonstrates that revealed comparative advantage of targets can reduce the likelihood of consummating acquisition deals as well as prolong the decision-making period of M&A announcements. Additionally, revealed comparative advantage of acquirers’ industries can help to reduce the length of the pre-integration phase. Keywords: acquisition completion, acquisition abandonment, acquisition duration, revealed comparative advantage.