Relevance of artefacts in 99m Tc-MAA SPECT scans on pre-therapy patient-specific 90 Y TARE internal dosimetry: a GATE Monte Carlo study.
Daniele PistoneAntonio ItalianoLucrezia AuditoreGiuseppe MandaglioAlfredo CampenníSergio BaldariErnesto AmatoPublished in: Physics in medicine and biology (2022)
Objective. The direct Monte Carlo (MC) simulation of radiation transport exploiting morphological and functional tomographic imaging as input data is considered the gold standard for internal dosimetry in nuclear medicine, and it is increasingly used in studies regarding trans-arterial radio-embolization (TARE). However, artefacts affecting the functional scans, such as reconstruction artefacts and motion blurring, decrease the accuracy in defining the radionuclide distribution in the simulations and consequently lead to errors in absorbed dose estimations. In this study, the relevance of such artefacts in patient-specific three-dimensional MC dosimetry was investigated in three cases of 90 Y TARE. Approach. The pre-therapy 99m Tc MacroAggregate Albumin (Tc-MAA) SPECTs and CTs of patients were used as input for simulations performed with the GEANT4-based toolkit GATE. Several pre-simulation SPECT-masking techniques were implemented, with the aim of zeroing the decay probability in air, in lungs, or in the whole volume outside the liver. Main results. Increments in absorbed dose up to about +40% with respect to the native-SPECT simulations were found in liver-related volumes of interest (VOIs), depending on the masking procedure adopted. Regarding lungs-related VOIs, decrements in absorbed doses in right lung as high as -90% were retrieved. Significance. These results highlight the relevant influence of SPECT artefacts, if not properly treated, on dosimetric outcomes for 90 Y TARE cases. Well-designed SPECT-masking techniques appear to be a promising way to correct for such misestimations.
Keyphrases
- monte carlo
- pet ct
- newly diagnosed
- end stage renal disease
- computed tomography
- chronic kidney disease
- high resolution
- type diabetes
- stem cells
- radiation therapy
- emergency department
- prognostic factors
- magnetic resonance
- bone marrow
- weight loss
- machine learning
- metabolic syndrome
- atomic force microscopy
- smoking cessation
- single molecule
- virtual reality
- patient reported outcomes