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Technical Note: Patient‐morphed mesh‐type phantoms to support personalized nuclear medicine dosimetry — a proof of concept study
Author(s) -
Carter Lukas M.,
Camilo Ocampo Ramos Juan,
Bolch Wesley E.,
Lewis Jason S.,
Kesner Adam L.
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14784
Subject(s) - imaging phantom , dosimetry , voxel , dicom , monte carlo method , computer science , medical imaging , artificial intelligence , nuclear medicine , medical physics , computer vision , mathematics , physics , medicine , statistics
Purpose Current standard practice for clinical radionuclide dosimetry utilizes reference phantoms, where defined organ dimensions represent population averages for a given sex and age. Greater phantom personalization would support more accurate dose estimations and personalized dosimetry. Tailoring phantoms is traditionally accomplished using operator‐intensive organ‐level segmentation of anatomic images. Modern mesh phantoms provide enhanced anatomical realism, which has motivated their integration within Monte Carlo codes. Here, we present an automatable strategy for generating patient‐specific phantoms/dosimetry using intensity‐based deformable image registration between mesh reference phantoms and patient CT images. This work demonstrates a proof‐of‐concept personalized dosimetry workflow, presented in comparison to the manual segmentation approach. Methods A linear attenuation coefficient phantom was generated by resampling the PSRK‐Man reference phantom onto a voxel grid and defining organ regions with corresponding Hounsfield unit (HU) reference values. The HU phantom was co‐registered with a patient CT scan using Plastimatch B‐spline deformable registration. In parallel, major organs were manually contoured to generate a “ground truth” patient‐specific phantom for comparisons. Monte Carlo derived S‐values, which support nuclear medicine dosimetry, were calculated using both approaches and compared. Results Application of the derived B‐spline transform to the polygon vertices comprising the PSRK‐Man yielded a deformed variant more closely matching the patient’s body contour and most organ volumes as‐evaluated by Hausdorff distance and Dice metrics. S‐values computed for fluorine‐18 for the deformed phantom using the Particle and Heavy Ion Transport code System showed improved agreement with those derived from the patient‐specific analog. Conclusions Deformable registration techniques can be used to create a personalized phantom and better support patient‐specific dosimetry. This method is shown to be easier and faster than manual segmentation. Our study is limited to a proof‐of‐concept scope, but demonstrates that integration of personalized phantoms into clinical dosimetry workflows can reasonably be achieved when anatomical images (CT) are available.