Calculating real-world travel routes instead of straight-line distance in the community response to out-of-hospital cardiac arrest

Using straight-line distance to estimate the proximity of public-access Automated External Defibrillators (AEDs) or volunteer first-responders to potential out-of-hospital cardiac arrests (OHCAs) does not reflect real-world travel distance. The difference between estimates may be an important consideration for bystanders and first-responders responding to OHCAs and may potentially impact patient outcome. To explore how calculating real-world travel routes instead of using straight-line distance estimates might impact the community response to OHCA. We mapped 4355 OHCA (01/04/2016-31/03/2017) and 2677 AEDs in London (UK), and 1263 OHCA (18/06/2017-17/06/2018) and 4704 AEDs in East Midlands (UK) using ArcGIS mapping software. We determined the distance from OHCAs to the nearest AED using straight-line estimates and real-world travel routes. We mapped locations of potential OHCAs (London: n = 9065, 20/09/2019-22/03/2020; East Midlands: n = 7637, 20/09/2019-17/03/2020) for which volunteer first-responders were alerted by the GoodSAM mobile-phone app, and calculated response distance using straight-line estimates and real-world travel routes. We created Receiver Operating Characteristic (ROC) curves and calculated the Area Under the Curve (AUC) to determine if travel distance predicted whether or not a responder accepted an alert. Real-world travel routes to the nearest AED were (median) 219 m longer (623 m vs 406 m) than straight-line estimates in London, and 211 m longer (568 m vs 357 m) in East Midlands. The identity of the nearest AED changed on 26% occasions in both areas when calculating real-world travel routes. GoodSAM responders' real-world travel routes were (median) 222 m longer (601 m vs 379 m) in London, and 291 m longer (814 m vs 523 m) in East Midlands. AUC statistics for both areas demonstrated that neither straight-line nor real-world travel distance predicted whether or not a responder accepted an alert. Calculating real-world travel routes increases the estimated travel distance and time for those responding to OHCAs. Calculating straight-line distance may overestimate the benefit of the community response to OHCA

Modeling an Interwoven Collimator for A 3D Endocavity Gamma Camera

Positron emission tomography (PET) and single-photon emission-computed tomography (SPECT) are important nuclear-medical imaging tools in diagnosing cancers and creating effective treatment plans. Commercially imaging systems are operated externally and can create 3D images of the whole body or of specific organs by rotating the gamma-ray detectors, and then employing software to reconstruct the 3D images from the multiple 2D projections at different angles of view. However, their uses in intraoperative environments or for imaging specific small organs, e.g., the prostate, ovary, and cervix, are limited because of their bulky designs and the long working-distance, hence causing low efficiency and poor spatial-resolution. In such situations, compact imaging devices, e.g., the trans-rectal gamma camera developed at Brookhaven National Laboratory (BNL) and Hybridyne Imaging Technologies, are preferable for detecting intra-prostatic tumors. The camera uses pixilated cadmium zinc telluride (CdZnTe) detectors with a matched parallel-hole collimator. However, their lack of 3D imaging capability limits their use in clinics, because the acquired images cannot be interpreted easily due to missing depth information. Given the constraint on space in such operations, the traditional 3D-image acquisition methods are impractical. For this reason, we designed an interwoven collimator dedicated for 3D imaging using an endocavity probe. This novel collimator allows us to take two or multiple views of a specific organ or tissue without rotating the camera. At the first stage of design for the collimator, we carried out Monte-Carlo simulations to study the response of the collimator and the attached detectors to gamma rays, and then developed a maximum-likelihood-based algorithm for reconstructing 3D images. In this paper, we detail our modeling of the collimator on a cluster Linux computer, and discuss the imaging capability of this novel collimator