Search

We demonstrate theoretically and experimentally that a specifically designed microcavity driven in the optical parametric oscillation regime exhibits lighthouse-like emission, i.e., an emission focused around a single direction. Remarkably, the emission direction of this micro-lighthouse is continuously controlled by the linear polarization of the incident laser, and angular beam steering over \unit{360}{\degree} is demonstrated. Theoretically, this unprecedented effect arises from the interplay between the nonlinear optical response of microcavity exciton-polaritons, the difference in the subcavities forming the microcavity, and the rotational invariance of the device., Comment: 16 pages, 4 figures, preprint version

Artificial intelligence (AI) applications have recently been proposed to detect errors in radiotherapy plans. External validation of such systems is essential to assess their performance and safety before applying them to clinical practice. We collected data from 5238 patients treated at Maastro Clinic and introduced a range of common radiotherapy plan errors for the model to detect. We estimated the model's discrimination by calculating the area under the receiver-operating characteristic curve (AUC). We also assessed its clinical usefulness as an alert system that could reduce the need for manual checks by calculating the percentage of values flagged as errors and the positive predictive value (PPV) for a range of high sensitivities (95 %-99 %) and error prevalence. The AUC when considering all variables was 67.8% (95% CI, 65.6%-69.9%). The AUC varied widely for different types of errors (from 90.4% for table angle errors to 54.5% for planning tumor volume-PTV dose errors). The percentage of flagged values ranged from 84% to 90% for sensitivities between 95% and 99% and the PPV was only slightly higher than the prevalence of the errors. The model's performance in the external validation was significantly worse than that in its original setting (AUC of 68% versus 89%). Its usefulness as an alert system to reduce the need for manual checks is questionable due to the low PPV and high percentage of values flagged as potential errors to achieve a high sensitivity. We analyzed the apparent limitations of the model and we proposed actions to overcome them.

Total body irradiation (TBI) is an integral part of stem cell transplant. However, patients are at risk of treatment-related toxicities, including radiation pneumonitis. While lung dose is one of the most crucial aspects of TBI dosimetry, currently available data are based on point doses. As volumetric dose distribution could be substantially altered by lung block parameters, we used 3D dosimetry in our treatment planning system to estimate volumetric lung dose and measure the impact of various lung block designs.We commissioned a TBI beam model in RayStation that matches the measured tissue-phantom ratio under our clinical TBI setup. Cerrobend blocks were automatically generated in RayStation on thoracic Computed Tomography (CT) scans from three anonymized patients using the lung, clavicle, spine, and diaphragmatic contours. The margin for block edge was varied to 0, 1, or 2 cm from the superior, lateral, and inferior thoracic borders, with a uniform margin 2.5 cm lateral to the vertebral bodies. The lung dose was calculated and compared with a prescription dose of 1200 cGy in six fractions (three with blocks and three without).The point dose at midplane under the block and the average lung dose are at the range of 73%-76% and 80%-88% of prescription dose respectively regardless of the block margins. In contrast, the percent lung volume receiving 10 Gy increased by nearly two-fold, from 31% to 60% over the margins from 0 to 2 cm.The TPS-derived 3D lung dose is substantially different from the nominal dose assumed with HVL lung blocks. Point doses under the block are insufficient to accurately gauge the relationship between dose and pneumonitis, and TBI dosimetry could be highly variable between patients and institutions as more descriptive parameters are not included in protocols. Much progress remains to be made to optimize and standardize technical aspects of TBI, and better dosimetry could provide more precise dosimetric predictors for pneumonitis risk.

PURPOSE: The current process for radiotherapy treatment plan quality assurance relies on human inspection of treatment plans, which is time‐consuming, error prone and oft reliant on inconsistently applied professional judgments. A previous proof‐of‐principle paper describes the use of a Bayesian network (BN) to aid in this process. This work studied how such a BN could be expanded and trained to better represent clinical practice. METHODS: We obtained 51 540 unique radiotherapy cases including diagnostic, prescription, plan/beam, and therapy setup factors from a de‐identified Elekta oncology information system from the years 2010–2017 from a single institution. Using a knowledge base derived from clinical experience, factors were coordinated into a 29‐node, 40‐edge BN representing dependencies among the variables. Conditional probabilities were machine learned using expectation maximization module using all data except a subset of 500 patient cases withheld for testing. Different classes of errors that were obtained from incident learning systems were introduced to the testing set of cases which were withheld from the dataset used for building the BN. Different sizes of datasets were used to train the network. In addition, the BN was trained using data from different length epochs as well as different eras. Its performance under these different conditions was evaluated by means of Areas Under the receiver operating characteristic Curve (AUC). RESULTS: Our performance analysis found AUCs of 0.82, 0.85, 0.89, and 0.88 in networks trained with 2‐yr, 3‐yr 4‐yr and 5‐yr windows. With a 4‐yr sliding window, we found AUC reduction of 3% per year when moving the window back in time in 1‐yr steps. Compared to the 4‐yr window moved back by 4 yrs (2010–2013 vs 2014–2017), the largest component of overall reduction in AUC over time was from the loss of detection performance in plan/beam error types. CONCLUSIONS: The expanded BN method demonstrates the ability to detect classes of errors commonly encountered in radiotherapy planning. The results suggest that a 4‐yr training dataset optimizes the performance of the network in this institutional dataset, and that yearly updates are sufficient to capture the evolution of clinical practice and maintain fidelity.

Ontologies are a formal, computer-compatible method for representing scientific knowledge about a given domain. They provide a standardized vocabulary, taxonomy and set of relations between concepts. When formatted in a standard way, they can be read and reasoned upon by computers as well as by humans. At the 2019 International Conference on the Use of Computers in Radiation Therapy, there was a session devoted to ontologies in radiation therapy. This paper is a compilation of the material presented, and is meant as an introduction to the subject. This is done by means of a didactic introduction to the topic followed by a series of applications in radiation therapy. The goal of this article is to provide the medical physicist and related professionals with sufficient background that they can understand their construction as well as their practical uses.

The recent explosion in machine learning efforts in the quality assurance (QA) space has produced a variety of proofs-of-concept many with promising results. Expected outcomes of model implementation include improvements in planning time, plan quality, advanced dosimetric QA, predictive machine maintenance, increased safety checks, and developments key for new QA paradigms driven by adaptive planning. In this article, we outline several areas of research and discuss some of the unique challenges each area presents.

Over the past decade, spontaneously emerging patterns in the density of polaritons in semiconductor microcavities were found to be a promising candidate for all-optical switching. But recent approaches were mostly restricted to scalar fields, did not benefit from the polariton's unique spin-dependent properties, and utilized switching based on hexagon far-field patterns with 60{\deg} beam switching (i.e. in the far field the beam propagation direction is switched by 60{\deg}). Since hexagon far-field patterns are challenging, we present here an approach for a linearly polarized spinor field, that allows for a transistor-like (e.g., crucial for cascadability) orthogonal beam switching, i.e. in the far field the beam is switched by 90{\deg}. We show that switching specifications such as amplification and speed can be adjusted using only optical means.

US NSF [ECCS-1406673]; German DFG [TRR142, SCHU 1980/5]; DFG Heisenberg programme; TRIF SEOS

In this paper, we present a polariton description of a semiconductor double microcavity system. The polariton formalism is derived from a microscopic theory for the exciton fields inside the quantum wells coupled to the confined optical cavity fields in a double cavity system. The polariton picture helps simplify theoretical studies of the observed phenomena in the double cavity system, such as nonlinear optical spin Hall effect.

Exciton polaritons in semiconductor microcavities exhibit many fundamental physical effects, with some of them amenable to being controlled by external fields. The polariton transport is affected by the polaritonic spin-orbit interaction, which is caused by the splitting of transverse-electric and transverse-magnetic (TE-TM) modes. This is the basis for a polaritonic Hall effect, called optical spin Hall effect (OSHE), which is related to the formation of spin/polarization textures in momentum space, determining anisotropic ballistic transport, as well as related textures in real space. Owing to Coulombic interactions between the excitonic components of the polaritons, optical excitation of polaritons can affect the OSHE. We present a theoretical analysis of the OSHE and its optical control in semiconductor double microcavities, i.e. two optically coupled cavities, which are particularly well suited for the creation of polaritonic reservoirs that affect the spin-texture-forming polaritons. The theory is formulated in terms of a set of double-cavity spinor-polariton Gross-Pitaevskii equations. Numerical solutions feature, among other things, a controlled rotation of the spin texture in momentum space. The theory also allows for an identification of the effective magnetic field component that determines the optical control in phenomenological pseudo-spin models in terms of exciton interactions and the polariton density in the second lower polariton branch., Comment: arXiv admin note: text overlap with arXiv:1607.03530

Phase-conjugate scattering can trigger modulational instabilities in a fluid of exciton-polaritons created in a pumped semiconductor quantum-well microcavity. These instabilities can settle into density patterns, e.g. hexagons and stripes, which produce corresponding patterns in the emitted light. The density patterns can be switched by relatively weak control optical beams. This paper reviews progress in our theoretical understanding of the physical processes that regulate the competitions among various patterns and drive the optical switching. Simulation results of pattern switching using a microscopic model of polariton dynamics are shown, and the mechanisms underlying competitions and switching are analyzed using reduced models that restrict the polariton motions to a limited number of relevant modes. We also briefly indicate the effects of the spin dependence of the polariton dynamics on the patterns.