Jason S Lewis1,2, Adam L Kesner3, Lukas M Carter3, Juan Camilo Ocampo Ramos3, Wesley E Bolch4. 1. Department of Radiology, Program in Pharmacology and the Radiochemistry and Molecular Imaging Probes Core, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 2. Department of Radiology and Department of Pharmacology, Weill Cornell Medical College, New York, NY, USA. 3. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 4. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Abstract
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.
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.
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