Commissioning measurements on an Elekta Unity MR-Linac

Magnetic resonance-guided radiotherapy technology is relatively new and commissioning publications, quality assurance (QA) protocols and commercial products are limited. This work provides guidance for implementation measurements that may be performed on the Elekta Unity MR-Linac (Elekta, Stockholm, Sweden). Adaptations of vendor supplied phantoms facilitated determination of gantry angle accuracy and linac isocentre, whereas in-house developed phantoms were used for end-to-end testing and anterior coil attenuation measurements. Third-party devices were used for measuring beam quality, reference dosimetry and during treatment plan commissioning; however, due to several challenges, variations on standard techniques were required. Gantry angle accuracy was within 0.1°, confirmed with pixel intensity profiles, and MV isocentre diameter was < 0.5 mm. Anterior coil attenuation was approximately 0.6%. Beam quality as determined by TPR20,10 was 0.705 ± 0.001, in agreement with treatment planning system (TPS) calculations, and gamma comparison against the TPS for a 22.0 × 22.0 cm2 field was above 95.0% (2.0%, 2.0 mm). Machine output was 1.000 ± 0.002 Gy per 100 MU, depth 5.0 cm. During treatment plan commissioning, sub-standard results indicated issues with machine behaviour. Once rectified, gamma comparisons were above 95.0% (2.0%, 2.0 mm). Centres which may not have access to specialized equipment can use in-house developed phantoms, or adapt those supplied by the vendor, to perform commissioning work and confirm operation of the MRL within published tolerances. The plan QA techniques used in this work can highlight issues with machine behaviour when appropriate gamma criteria are set

Array programming with NumPy.

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis

Array programming with NumPy

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis