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Attenuation correction in hybrid PET/MR scanners is still a challenging task. This paper describes a methodology for synthesizing a pseudo-CT volume from a single T1-weighted volume, thus allowing us to create accurate attenuation correction maps. Methods: We propose a fast pseudo-CT volume generation from a patient-specific MR T1-weighted image using a groupwise patch-based approach and an MRI-CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel to the patches of all MR images in the database that lie in a certain anatomic neighborhood. The pseudo-CT volume is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a graphical processing unit (GPU). Results: We evaluated our method both qualitatively and quantitatively for PET/MR correction. The approach performed successfully in all cases considered. We compared the SUVs of the PET image obtained after attenuation correction using the patient-specific CT volume and using the corresponding computed pseudo-CT volume. The patient-specific correlation between SUV obtained with both methods was high (R 2 5 0.9980, P , 0.0001), and the Bland-Altman test showed that the average of the differences was low (0.0006 ± 0.0594). A region-of-interest analysis was also performed. The correlation between SUV mean and SUV max for every region was high (R 2 5 0.9989, P , 0.0001, and R 2 5 0.9904, P , 0.0001, respectively). Conclusion:The results indicate that our method can accurately approximate the patient-specific CT volume and serves as a potential solution for accurate attenuation correction in hybrid PET/MR systems. The quality of the corrected PET scan using our pseudo-CT volume is comparable to having acquired a patient-specific CT scan, thus improving the results obtained with the ultrashort-echo-timebased attenuation correction maps currently used in the scanner. The GPU implementation substantially decreases computational time, making the approach suitable for real applications.

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in

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Abstract-This work presents a graphics processing unit (GPU)-based implementation of a fully 3-D PET iterative reconstruction code, FIRST (Fast Iterative Reconstruction Software for [PET] Tomography), which was developed by our group. We describe the main steps followed to convert the FIRST code (which can run on several CPUs using the message passing interface [MPI] protocol) into a code where the main time-consuming parts of the reconstruction process (forward and backward projection) are massively parallelized on a GPU. Our objective was to obtain significant acceleration of the reconstruction without compromising the image quality or the flexibility of the CPU implementation. Therefore, we implemented a GPU version using an abstraction layer for the GPU, namely, CUDA C. The code reconstructs images from sinogram data, and with the same System Response Matrix obtained from Monte Carlo simulations than the CPU version. The use of memory was optimized to ensure good performance in the GPU. The code was adapted for the VrPET small-animal PET scanner. The CUDA version is more than 70 times faster than the original code running in a single core of a high-end CPU, with no loss of accuracy.

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This paper presents the computation of feasible paths for mobile robots in known and unknown environments using a QAPF learning algorithm. Q-learning is a reinforcement learning algorithm that has increased in popularity in mobile robot path planning in recent times, due to its self-learning capability without requiring a priori model of the environment. However, Q-learning shows slow convergence to the optimal solution, notwithstanding such an advantage. To address this limitation, the concept of partially guided Q-learning is employed wherein, the artificial potential field (APF) method is utilized to improve the classical Q-learning approach. Therefore, the proposed QAPF learning algorithm for path planning can enhance learning speed and improve final performance using the combination of Q-learning and the APF method. Criteria used to measure planning effectiveness include path length, path smoothness, and learning time. Experiments demonstrate that the QAPF algorithm successfully achieves better learning values that outperform the classical Q-learning approach in all the test environments presented in terms of the criteria mentioned above in offline and online path planning modes. The QAPF learning algorithm reached an improvement of 18.83% in path length for the online mode, an improvement of 169.75% in path smoothness for the offline mode, and an improvement of 74.84% in training time over the classical approach.INDEX TERMS Path planning, Q-learning, Artificial potential field, Reinforcement learning, Mobile robots.