Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the large-scale dynamic environments. In this paper, we develop a novel deep neural network, named LPD-Net (Large-scale Place Description Network), which can extract discriminative and generalizable global descriptors from the raw 3D point cloud. Two modules, the adaptive local feature extraction module and the graph-based neighborhood aggregation module, are proposed, which contribute to extract the local structures and reveal the spatial distribution of local features in the largescale point cloud, with an end-to-end manner. We implement the proposed global descriptor in solving point cloud based retrieval tasks to achieve the large-scale place recognition. Comparison results show that our LPD-Net is much better than PointNetVLAD and reaches the state-of-the-art. We also compare our LPD-Net with the vision-based solutions to show the robustness of our approach to different weather and light conditions.

Concerns about the geometric accuracy of MRI in radiation therapy (RT) have been present since its invention. Although modern scanners typically have system levels of geometric accuracy that meet requirements of RT, subject-specific distortion is variable, and methods to in vivo assess and control patient-induced geometric distortion are not yet resolved. This study investigated the nature and magnitude of the subject-induced susceptibility effect on geometric distortions in clinical brain MRI, and tested the feasibility of in vivo quality control using field inhomogeneity mapping. For 19 consecutive patients scanned on a dedicated 3T MR scanner, B0 field inhomogeneity maps were acquired and analyzed to determine subject-induced distortions. For 3D T1 weighted images frequency-encoded with a bandwidth of 180 Hz/pixel, 86.9% of the estimated displacements were <0.5 mm, 97.4% <1 mm, and only 0.1% of displacements > 2 mm. The maximum displacement was <4 mm. The greatest distortions were observed at the interfaces with air at the sinuses. Displacements decayed to less than 1 mm over a distance of 8 mm. Metal surgical wires generated smaller distortions, with an averaged maximum displacement of 0.76 mm. Repeat acquisition of the field maps in 17 patients revealed a within-subject standard deviation of 0.25 ppm, equivalent to 0.22 mm displacement in the frequency-encoding direction in the 3D T1 weighted images. Susceptibility-induced voxel displacements in the brain are generally small, but should be monitored for precision RT. These effects are manageable at 3T and lower fields, and the methods applied can be used to monitor for potential local errors in individual patients, as well as to correct for local distortions as needed.

Advances in multimodality imaging, providing accurate information of the irradiated target volume and the adjacent critical structures or organs at risk (OAR), has made significant improvements in delivery of the external beam radiation dose. Radiation therapy conventionally has used computed tomography (CT) imaging for treatment planning and dose delivery. However, magnetic resonance imaging (MRI) provides unique advantages: added contrast information that can improve segmentation of the areas of interest, motion information that can help to better target and deliver radiation therapy, and posttreatment outcome analysis to better understand the biologic effect of radiation. To take advantage of these and other potential advantages of MRI in radiation therapy, radiologists and MRI physicists will need to understand the current radiation therapy workflow and speak the same language as our radiation therapy colleagues. This review article highlights the emerging role of MRI in radiation dose planning and delivery, but more so for MR-only treatment planning and delivery. Some of the areas of interest and challenges in implementing MRI in radiation therapy workflow are also briefly discussed. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1468-1478.

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The purpose of this paper is to give a necessary and sufficient condition under which for a given plant of descriptor system model there exists a normal, internally stabilizing controller of order no greater than rankE that satisfies a closed-loop H , norm bound. The approach used in this paper is based on a generalized version of Bounded Real Lemma, thus the proofs are simple.