Cross-calibration of the Siemens mMR:easily acquired accurate PET phantom measurements, long-term stability and reproducibility

BACKGROUND: We present a quick and easy method to perform quantitatively accurate PET scans of typical water-filled PET plastic shell phantoms on the Siemens Biograph mMR PET/MR system. We perform regular cross-calibrations (Xcal) of our PET systems, including the PET/MR, using a Siemens mCT water phantom. LONG-TERM STABILITY: The mMR calibration stability was evaluated over a 3-year period where 54 cross-calibrations were acquired, showing that the mMR on average underestimated the concentration by 16 %, consistently due to the use of MR-based μ-maps. The mMR produced the narrowest calibration ratio range with the lowest standard deviation, implying it is the most stable of the six systems in the study over a 3-year period. MMR ACCURACY WITH PREDEFINED μ-MAPS: With the latest mMR software version, VB20P, it is possible to utilize predefined phantom μ-maps. We evaluated both the system-integrated, predefined μ-map of the long mMR water phantom and our own user-defined CT-based μ-map of the mCT water phantom, which is used for cross-calibration. For seven scans, which were reconstructed with correctly segmented μ-maps, the mMR produced cross-calibration ratios of 1.00–1.02, well within the acceptance range [0.95–1.05], showing high accuracy. CONCLUSIONS: The mMR is the most stable PET system in this study, and the mean underestimation is no longer an issue with the easily accessible μ-map, which resulted in correct cross-calibration ratios in all seven tests. We will share the user-defined μ-map of the mCT phantom and the protocol with interested mMR users

Robustness and Generalizability of Deep Learning Synthetic Computed Tomography for Positron Emission Tomography/Magnetic Resonance Imaging–Based Radiation Therapy Planning of Patients With Head and Neck Cancer

PURPOSE: Radiotherapy planning based only on positron emission tomography/magnetic resonance imaging (PET/MRI) lacks computed tomography (CT) information required for dose calculations. In this study, a previously developed deep learning model for creating synthetic CT (sCT) from MRI in patients with head and neck cancer was evaluated in 2 scenarios: (1) using an independent external dataset, and (2) using a local dataset after an update of the model related to scanner software-induced changes to the input MRI. METHODS AND MATERIALS: Six patients from an external site and 17 patients from a local cohort were analyzed separately. Each patient underwent a CT and a PET/MRI with a Dixon MRI sequence over either one (external) or 2 (local) bed positions. For the external cohort, a previously developed deep learning model for deriving sCT from Dixon MRI was directly applied. For the local cohort, we adapted the model for an upgraded MRI acquisition using transfer learning and evaluated it in a leave-one-out process. The sCT mean absolute error for each patient was assessed. Radiotherapy dose plans based on sCT and CT were compared by assessing relevant absorbed dose differences in target volumes and organs at risk. RESULTS: The MAEs were 78 ± 13 HU and 76 ± 12 HU for the external and local cohort, respectively. For the external cohort, absorbed dose differences in target volumes were within ± 2.3% and within ± 1% in 95% of the cases. Differences in organs at risk were <2%. Similar results were obtained for the local cohort. CONCLUSIONS: We have demonstrated a robust performance of a deep learning model for deriving sCT from MRI when applied to an independent external dataset. We updated the model to accommodate a larger axial field of view and software-induced changes to the input MRI. In both scenarios dose calculations based on sCT were similar to those of CT suggesting a robust and reliable method