Local quality assessment of patient specific synthetic-CT via voxel-wise analysis

Synthetic-Computed Tomography (sCT) generation is a critical component of Magnetic Resonance Imaging (MRI)-only radiation therapy workflows. The sCT computed from MRI is generally assessed by measuring Hounsfield Units (HU) discrepancies with a reference CT. The aim of this work was to propose a process for the blind assessment of local errors in generated sCTs where a reference CT is unavailable, allowing for safe MRI-only radiation therapy treatment planning. A personalised inter-patient registration method was applied to align a cohort of reference CTs into the same coordinate system. This process resulted in probability maps for each segmented organ, a mean CT image and a standard deviation map. These data were propagated to the anatomical space for each sCT, allowing for out of distribution intensities to be detected at a voxel level by computing local z-scores. Probability maps of organs were used to weight the resulting z-scores, reducing the bias induced by the registration around structures. Two sCT generation methods were chosen as examples to illustrate this methodology: an atlas-based method (ABM) and a deep-learning approach based on a Generative Adversarial Network (GAN) architecture. 39 patients treated with external beam radiotherapy for prostate cancer, with co-registered CT and MR pairs, were used for sCT generation. 26 of these patients were selected as reference CT, and sCT of the remaining 13 patients were assessed. Accurate inter-individual registration was achieved, with mean Dice scores higher than 0.91 for all organs. The average volume of error represented 0.29% of the image for the ABM, 0.37% for the GAN. The proposed methodology produced 3D volumes which identify significant local sCT errors. Depending on their size and location, these errors could lead to inaccurate tissue density computation during radiation therapy. This work provides an automated QA method aimed at preventing incorrect radiation dose delivery to patients.