Data Descriptor : Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition

Cloud condensation nuclei (CCN) number concentrations alongside with submicrometer particle number size distributions and particle chemical composition have been measured at atmospheric observatories of the Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS) as well as other international sites over multiple years. Here, harmonized data records from 11 observatories are summarized, spanning 98,677 instrument hours for CCN data, 157,880 for particle number size distributions, and 70,817 for chemical composition data. The observatories represent nine different environments, e.g., Arctic, Atlantic, Pacific and Mediterranean maritime, boreal forest, or high alpine atmospheric conditions. This is a unique collection of aerosol particle properties most relevant for studying aerosol-cloud interactions which constitute the largest uncertainty in anthropogenic radiative forcing of the climate. The dataset is appropriate for comprehensive aerosol characterization (e.g., closure studies of CCN), model-measurement intercomparison and satellite retrieval method evaluation, among others. Data have been acquired and processed following international recommendations for quality assurance and have undergone multiple stages of quality assessment.Peer reviewe

GPU Implementation of List-mode DRAMA for Real-time OpenPET Image Reconstruction

The OpenPET, which have a physically opened space between two detector rings, is our new geometry to enable PET imaging during radiation therapy. Especially, tracking a moving target such as a tumor in the lung will become possible if the real-time imaging system is realized. In this paper, therefore, we developed a list-mode image reconstruction method using general purpose graphic processing units (GPGPUs). We used the list-mode dynamic row-action maximum likelihood algorithm (DRAMA) with new relaxation parameter calculated by the vector of image update. For GPU implementation, the efficiency of acceleration depends on the implementation method which is required to avoid conditional statements and to use efficient memory accesses. We developed a system model in which each element of system matrix is calculated as the value of detector response function (DRF) of the length between the center of a voxel and a line of response (LOR). The system model was suited to GPU implementations that enable us to calculate each element of the system matrix with reduced number of the conditional statements. We applied the developed method to a small OpenPET prototype, which was developed for a proof-of-concept. We measured the micro-Derenzo phantom placed at the gap. The results showed that the same quality of reconstructed images using GPU as using CPU were achieved, and calculation speed on the GPU was 35.5 times faster than that on the CPU.2010 Nuclear Science Symposium and Medical Imaging Conferenc

GPU-based image reconstruction method including geometrical detector response functions for OpenPET

The OpenPET, which has a physical gap between two detector rings, is our new geometry. In order to realize future radiation therapy guided by OpenPET, real-time imaging is required. Therefore we developed a list-mode image reconstruction method using general purpose graphic processing units (GPUs). For GPU implementation, the efficiency of acceleration depends on the implementation method which is required to avoid conditional statements. In this paper, therefore, we developed a new system model suitable for GPU implementation. In the proposed system model, each element of system matrix was calculated as the value of detector response function (DRF) of the length between the center of a voxel and a line of response (LOR). The DRF, which was calculated analytically to represent the probability distribution of each LOR, was modeled by a sixth-order polynomial function. The system model enabled us to calculate each element of the system matrix with reduced number of the conditional statements. We used the list-mode dynamic row-action maximum likelihood algorithm (DRAMA) which could reduce the number of iterations to only one. We applied the developed method to a small OpenPET prototype, which was developed for a proof-of-concept. The results showed that high quality reconstructed images were obtained using the proposed system model with 14.8 times faster than using the conventional system model.International Forum on Medical Imaging in Asi