Assessment of different patient‐to‐phantom matching criteria applied in Monte Carlo–based computed tomography dosimetry. Issue 10 (1st September 2017) - Stepusin, Elliott J.; Long, Daniel J.; Marshall, Emily L.; Bolch, Wesley E.
Abstract : Purpose: To quantify differences in computationally estimated computed tomography (CT) organ doses for patient‐specific voxel phantoms to estimated organ doses in matched computational phantoms using different matching criteria. Materials and methods: Fifty‐two patient‐specific computational voxel phantoms were created through CT image segmentation. In addition, each patient‐specific phantom was matched to six computational phantoms of the same gender based, respectively, on age and gender (reference phantoms), height and weight, effective diameter (both central slice and exam range average), and water equivalent diameter (both central slice and exam range average). Each patient‐specific phantom and matched computational phantom were then used to simulate six different torso examinations using a previously validated Monte Carlo CT dosimetry methodology that accounts for tube current modulation. Organ doses for each patient‐specific phantom were then compared with the organ dose estimates of each of the matched phantoms. Results: Relative to the corresponding patient‐specific phantoms, the root mean square of the difference in organ dose was 39.1%, 20.3%, 22.7%, 21.6%, 20.5%, and 17.6%, for reference, height and weight, effective diameter (central slice and scan average), and water equivalent diameter (central slice and scan average), respectively. The average magnitude of difference in organ dose was 24%, 14%, 16.9%, 16.2%, 14%, and 11.9%, respectively. Conclusion: Overall, these data suggest that matching a patient to a computational phantom in a library is superior to matching to a reference phantom. Water equivalent diameter is the superior matching metric, but it is less feasible to implement in a clinical and retrospective setting. For these reasons, height‐and‐weight matching is an acceptable and reliable method for matching a patient to a member of a computational phantom library with regard to CT dosimetry.