Evaluation and comparison of synthetic computed tomography algorithms with 3T MRI for prostate radiotherapy: AI-based versus bulk density method.

Abstract

Purpose: Synthetic computed tomography (sCT)-algorithms, which generate computed tomography images from magnetic resonance imaging data, are becoming part of the clinical radiotherapy workflow. The aim of this retrospective study was to evaluate and compare commercial bulk-density-method (BM)-based and AI (artificial intelligence)-based-algorithms using 3T magnetic resonance imaging (MRI) with patient data as part of the local clinical commissioning process.

Methods: 44 prostate radiotherapy patients were subjected to MRI and treatment planning CT (TPCT) scans. From the MRI images, sCT images with two different sCT algorithms were generated. The sCT images were evaluated by visual inspection of artifacts. Both sCT methods were compared to TPCT, with Dice similarity score(DSC) of bone and body contours, DVH parameters for CTV, bladder and rectum, and gamma-analysis. Accuracy for treatment alignment using sCT images was also tested. Various limits were used to define whether the differences between sCT methods to TPCT were clinically relevant (DVH parameters <2%, gamma-analysis passing rates 90%, 95%, and 98%, and the DSC 0.98 for body and 0.7 for bone).

Results: Our results show that, differences in CTV-dose coverage values were <2% in most of the patients with both sCT algorithms when compared to reference dose coverage. While AI-sCT had mean dose coverage difference <0,5% and BM-sCT <1%. Gamma-analysis showed that the AI-sCT mean passing rates were 95.4%, 98.6%, and 99.4% with 1mm1%, 2mm2%, and 3mm3% criteria, respectively. Similarly for BM-sCT the mean passing rates were 93.4%, 98.2%, and 99.2%. For the treatment alignment accuracy, the mean difference in magnitude of the translational shifts was 1.43 mm for BM-sCT and 1.57 mm for AI-sCT. Even though AI-sCT showed statistically better correspondence to TPCT, the differences were not clinically relevant with any of the limits. Visual evaluation showed artifacts in the AI-sCT especially in the bowel area and fiducial markers were not generated with either of the sCT algorithms.

Conclusions: In conclusion, sCT-algorithms were clinically usable on prostate treatments using 3T MR-only workflow. While AI-sCT showed better correspondence to TPCT than BM-sCT, it generated characteristic artifacts. As sCT algorithms perform well, we still recommend testing the sCT-algorithms with retrospective analyses from patient data prior to implementing sCT into the routine workflow to better understand the specific limitations and capabilities of these algorithms.