A pathfinder version of CHIME (the Canadian Hydrogen Intensity Mapping Experiment) is currently being commissioned at the Dominion Radio Astrophysical Observatory (DRAO) in Penticton, BC. The instrument is a hybrid cylindrical interferometer designed to measure the large scale neutral hydrogen power spectrum across the redshift range 0.8 to 2.5. The power spectrum will be used to measure the baryon acoustic oscillation (BAO) scale across this poorly probed redshift range where dark energy becomes a significant contributor to the evolution of the Universe. The instrument revives the cylinder design in radio astronomy with a wide field survey as a primary goal. Modern low-noise amplifiers and digital processing remove the necessity for the analog beamforming that characterized previous designs. The Pathfinder consists of two cylinders 37 m long by 20 m wide oriented northsouth for a total collecting area of 1,500 square meters. The cylinders are stationary with no moving parts, and form a transit instrument with an instantaneous field of view of ∼100 degrees by 1-2 degrees. Each CHIME Send correspondence to K.Bandura: E-mail: kevin.bandura@mcgill.ca arXiv:1406.2288v1 [astro-ph.IM] 9 Jun 2014Pathfinder cylinder has a feedline with 64 dual polarization feeds placed every ∼30 cm which Nyquist sample the north-south sky over much of the frequency band. The signals from each dual-polarization feed are independently amplified, filtered to 400-800 MHz, and directly sampled at 800 MSps using 8 bits. The correlator is an FX design, where the Fourier transform channelization is performed in FPGAs, which are interfaced to a set of GPUs that compute the correlation matrix. The CHIME Pathfinder is a 1/10th scale prototype version of CHIME and is designed to detect the BAO feature and constrain the distance-redshift relation.The lessons learned from its implementation will be used to inform and improve the final CHIME design.

A new continuous flow diffusion chamber (CFDC) has been designed and constructed to study the ice nucleation efficiency of natural and anthropogenic aerosol particles over a range of temperatures and supersaturations. The CFDC system at Dalhousie University, Canada is based on the design of Rogers et al. (1988, 1994) at Colorado State University, USA. A steady airflow (2.83 lpm) composed of sheath flows and an aerosol flow passes through the annular gap of the diffusion chamber. The walls of the chamber are ice-covered and are held at different temperatures. Aerosol particles are injected into the center of the gap near the location of maximum supersaturation. Particles greater than 5 µm in aerodynamic diameter are removed with impactors before entry to the chamber. Ice crystals are identified with an optical particle counter at the outlet of the chamber. In this article we report on the ice nucleation results of two mineral dust particles of potential atmospheric relevance, kaolinite and montmorillonite. Our results indicate that kaolinite and montmorillonite act as efficient ice nuclei in deposition/condensation nucleation mode. The onset relative humidity of both kaolinite and montmorillonite mineral dust particles were determined. The percentage of active ice nuclei is higher in montmorillonite compared to kaolinite at each temperature within the experimental conditions. The fraction of active ice nuclei increases with decreasing temperature and also with increasing relative humidity.

Purpose To evaluate pix2pix and CycleGAN and to assess the effects of multiple combination strategies on accuracy for patch‐based synthetic computed tomography (sCT) generation for magnetic resonance (MR)‐only treatment planning in head and neck (HN) cancer patients. Materials and methods Twenty‐three deformably registered pairs of CT and mDixon FFE MR datasets from HN cancer patients treated at our institution were retrospectively analyzed to evaluate patch‐based sCT accuracy via the pix2pix and CycleGAN models. To test effects of overlapping sCT patches on estimations, we (a) trained the models for three orthogonal views to observe the effects of spatial context, (b) we increased effective set size by using per‐epoch data augmentation, and (c) we evaluated the performance of three different approaches for combining overlapping Hounsfield unit (HU) estimations for varied patch overlap parameters. Twelve of twenty‐three cases corresponded to a curated dataset previously used for atlas‐based sCT generation and were used for training with leave‐two‐out cross‐validation. Eight cases were used for independent testing and included previously unseen image features such as fused vertebrae, a small protruding bone, and tumors large enough to deform normal body contours. We analyzed the impact of MR image preprocessing including histogram standardization and intensity clipping on sCT generation accuracy. Effects of mDixon contrast (in‐phase vs water) differences were tested with three additional cases. The sCT generation accuracy was evaluated using mean absolute error (MAE) and mean error (ME) in HU between the plan CT and sCT images. Dosimetric accuracy was evaluated for all clinically relevant structures in the independent testing set and digitally reconstructed radiographs (DRRs) were evaluated with respect to the plan CT images. Results The cross‐validated MAEs for the whole‐HN region using pix2pix and CycleGAN were 66.9 ± 7.3 vs 82.3 ± 6.4 HU, respectively. On the independent testing set with additional artifacts and previously unseen image features, whole‐HN region MAEs were 94.0 ± 10.6 and 102.9 ± 14.7 HU for pix2pix and CycleGAN, respectively. For patients with different tissue contrast (water mDixon MR images), the MAEs increased to 122.1 ± 6.3 and 132.8 ± 5.5 HU for pix2pix and CycleGAN, respectively. Our results suggest that combining overlapping sCT estimations at each voxel reduced both MAE and ME compared to single‐view non‐overlapping patch results. Absolute percent mean/max dose errors were 2% or less for the PTV and all clinically relevant structures in our independent testing set, including structures with image artifacts. Quantitative DRR comparison between planning CTs and sCTs showed agreement of bony region positions to <1 mm. Conclusions The dosimetric and MAE based accuracy, along with the similarity between DRRs from sCTs, indicate that pix2pix and CycleGAN are promising methods for MR‐only treatment planning for HN cancer. Our methods investigated for overlapping patch‐based HU estimations also ...

Inverse treatment planning in radiation therapy is formulated as solving optimization problems. The objective function and constraints consist of multiple terms designed for different clinical and practical considerations. Weighting factors of these terms are needed to define the optimization problem. While a treatment planning optimization engine can solve the optimization problem with given weights, adjusting the weights to yield a high-quality plan is typically performed by a human planner. Yet the weight-tuning task is labor intensive, time consuming, and it critically affects the final plan quality. An automatic weight-tuning approach is strongly desired. The procedure of weight adjustment to improve the plan quality is essentially a decision-making problem. Motivated by the tremendous success in deep learning for decision making with human-level intelligence, we propose a novel framework to adjust the weights in a human-like manner. This study uses inverse treatment planning in high-dose-rate brachytherapy (HDRBT) for cervical cancer as an example. We develop a weight-tuning policy network (WTPN) that observes dose volume histograms of a plan and outputs an action to adjust organ weighting factors, similar to the behaviors of a human planner. We train the WTPN via end-to-end deep reinforcement learning. Experience replay is performed with the epsilon greedy algorithm. After training is completed, we apply the trained WTPN to guide treatment planning of five testing patient cases. It is found that the trained WTPN successfully learns the treatment planning goals and is able to guide the weight tuning process. On average, the quality score of plans generated under the WTPN's guidance is improved by ~8.5% compared to the initial plan with arbitrarily set weights, and by 10.7% compared to the plans generated by human planners. To our knowledge, this is the first time that a tool is developed to adjust organ weights for the treatment planning optimization problem in a human-like fashion based on intelligence learnt from a training process. This is different from existing strategies based on pre-defined rules. The study demonstrates potential feasibility to develop intelligent treatment planning approaches via deep reinforcement learning.