Organ Localization: Moving Toward Patient Specific Prospective Organ Dosimetry for CT

Abstract

<p><bold>Purpose:</bold> Radiation doses from computed tomography (CT) examinations have come under public and governmental scrutiny because of several recent misadministrations of radiation across the country. Current CT dosimetry methods in the clinic use standardized cylindrical water phantoms to measure radiation dose across various scanning protocols and different scanner manufacturers. These methods and equipment are too generalized to provide accurate risk assessment for patients of varying ages, genders, and anatomies. The advent of computer models based on real CT imaged anatomy has made patient specific and organ specific dosimetry achievable.</p><p>With a population of both pediatric and adult patient models comprised of a wide range of anatomies, Monte Carlo based dose calculations can be cataloged. A patient can receive a prospective dose estimation from a phantom within our population that best exhibits the patient's age and anatomical characteristics. Knowledge of organ size and location is essential to finding a proper match between the patient and the computer model. To this end, very little information is currently available regarding organ size and location across a diverse human population. The purpose of this study was to develop a predictive model to ascertain organ locations and volumes for pediatric and adult patients. </p><p><bold>Methods:</bold> This study included 51 adults and 40 pediatrics from which Extended NURBS-based Cardiac-Torso (XCAT) phantoms were generated. Large organs were manually segmented from clinical CT data. The remaining organs and other anatomical structures were created by transforming an existing human model template to fit the framework of the segmented structures. The maximum and minimum points of the organs were recorded with respect to the axial distance from the tip of the sacrum. The axial width and midpoint for each organ were then determined. The organ volumes were also calculated. All three organ parameters were plotted as functions of patient age and weight for adults and patient age for pediatrics.</p><p><bold>Results:</bold> The adult patients showed no statistically significant correlation between organ parameters and age and BMI. There were slight, positive linear trends with organ midpoint (max r<super>2</super>=0.365, mean r<super>2</super>=0.185) and organ volume (max r<super>2</super>=0.510, mean r<super>2</super>=0.183) versus adult patient weight. The height correlations were also positive for midpoint (r<super>2</super>=0.485, mean r<super>2</super>=0.271). Gaussian fits performed on probability density functions of adult organs resulted in r<super>2</super>-values ranging from 0.945 to 0.996. Pediatric patients demonstrated strong cube root relationships with organ midpoints (max r<super>2</super>=0.857, mean r<super>2</super>=0.790) and organ widths (max r<super>2</super>=0.905 , mean r<super>2</super>=0.564) versus age. Pediatric organ volumes showed positive linear relationships versus age (max r<super>2</super>=0.983, mean r<super>2</super>=0.701).</p><p><bold>Conclusions:</bold> Adult patients exhibited small variations in organ volume and location with respect to weight, but no meaningful correlation existed between these parameters and age. Once adulthood is reached, organ morphology and positioning seems to remain static; however, clear trends are evident between pediatric age and organ volumes and locations. Such information can aid in the selection of an appropriate computer model that has the highest probability of mirroring the anatomy of a patient undergoing a clinical exam. Applications could also extend into comparing PET versus CT determination of organ volume and location.</p>