Receive coil quality assurance procedure and automated analysis for ViewRay MRIdian MR‐Linac

Abstract

AbstractPurposeRegular receiving coil quality assurance (QA) is required to ensure image quality of an MRIdian Linac system. The manufacturer provides a spherical phantom and positioning tube for single‐slice signal‐to‐noise ratio (SNR) and uniformity assessments. We aimed to improve imaging setup and coverage and eliminate inter‐scan variability by employing multi‐slice imaging of a stable phantom. Additionally, we strived to expedite analysis by developing objective, automated analysis software.MethodsA 5300 mL cylindrical plastic bottle placed in plastic bins was scanned at isocenter using a spin‐echo sequence with NEMA‐recommended parameters and 18 axial slices, avoiding phantom repositioning. Acquisition was repeated with and without prescan normalization filtering and by saving uncombined element images. Obtained data were analyzed using custom open‐source MATLAB code. Signal and noise images were automatically assigned, and ROIs for SNR and uniformity calculations were defined using image thresholding. SNR and uniformity pass/fail decisions were made using baseline comparisons.ResultsThe proposed method was successfully implemented as monthly coil QA for 3.5 years. Setup and scanning took 41 min on average for a coil set. Automated image analysis was completed in a few minutes. Signal intensity peaked around +90 or ‐90 mm for Torso or Head/Neck coil unfiltered images. Noise peaked and minimized SNR inside ±30 mm from isocenter, while maximizing it around ±130 mm. Prescan normalization smoothed signal response, reduced SNR and increased uniformity. Individual coil element image analysis identified their position, signal or noise response and SNR. SNR and uniformity pass/fail thresholds were set for already tested and new coils. Conspicuous and subtle Torso coil malfunctions were detected considering baseline deviations of combined and individual element results.ConclusionsOur QA method eliminated observer bias and provided insights into coil function, image filtering performance and coil element location. It provided SNR and uniformity thresholds and identified faulty coil elements.