Organ Doses from CT Localizer Radiographs: Development, Validation, and Application of a Monte Carlo Estimation Technique.
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PURPOSE:The purpose of this study was to simulate and validate organ doses from different CT localizer radiograph geometries using Monte Carlo methods for a population of patients. METHODS:A Monte Carlo method was developed to estimate organ doses from CT localizer radiographs using PENELOPE. The method was validated by comparing dosimetry estimates with measurements using an anthropomorphic phantom imbedded with thermoluminescent dosimeters (TLDs) scanned on a commercial CT system (Siemens SOMATOM Flash). The Monte Carlo simulation platform was then applied to conduct a population study with fifty-seven adult computational phantoms (XCAT). In the population study, clinically relevant chest localizer protocols were simulated with the x-ray tube in anterior-posterior (AP), right lateral, and PA positions. Mean organ doses and associated standard deviations (in mGy) were then estimated for all simulations. The obtained organ doses were studied as a function of patient chest diameter. Organ doses for breast and lung were compared across different views and represented as a percentage of organ doses from rotational CT scans. RESULTS:The validation study showed an agreement between the Monte Carlo and physical TLD measurements with a maximum percent difference of 15.5% and a mean difference of 3.5% across all organs. The XCAT population study showed that breast dose from AP localizers was the highest with a mean value of 0.24 mGy across patients, while the lung dose was relatively consistent across different localizer geometries. The organ dose estimates were found to vary across the patient population, partially explained by the changes in the patient chest diameter. The average effective dose was 0.18 mGy for AP, 0.09 mGy for lateral, and 0.08 mGy for PA localizer. CONCLUSION:A platform to estimate organ doses in CT localizer scans using Monte Carlo methods was implemented and validated based on comparison with physical dose measurements. The simulation platform was applied to a virtual patient population, where the localizer organ doses were found to range within 0.4-8.6% of corresponding organ doses for a typical CT scan, 0.2-3.3% of organ doses for a CT pulmonary angiography scan, and 1.1-20.8% of organ doses for a low dose lung cancer screening scan.
Published Version (Please cite this version)
Hoye, Jocelyn, Shobhit Sharma, Yakun Zhang, Wanyi Fu, Francesco Ria, Anuj Kapadia, W Paul Segars, Joshua Wilson, et al. (2019). Organ Doses from CT Localizer Radiographs: Development, Validation, and Application of a Monte Carlo Estimation Technique. Medical physics. 10.1002/mp.13781 Retrieved from https://hdl.handle.net/10161/19242.
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My research focuses on developing an innovative imaging modality - Neutron Stimulated Emission Computed Tomography (NSECT), that uses inelastic scattering through fast neutrons to generate tomographic images of the body's element composition. Such information is vital in diagnosing a variety of disorders ranging from iron and copper overload in the liver to several cancers. Specifically, there are two ongoing projects:
1) Experimental Implementation of NSECT
Neutron spectroscopy techniques are showing significant promise in determining element concentrations in the human body. We have developed a tomographic imaging system capable of generating tomographic images of the element concentration within a body through a single non-invasive in-vivo scan. This system has been implemented using a Van-de-Graaf accelerator fast neutron source and high-purity germanium gamma detectors at the Triangle Universities Nuclear Laboratory. This setup has been used to obtain NSECT scans for several samples such as bovine liver, mouse specimens and human breast tissue. In order to extract maximum information about a target sample with the lowest possible levels of dose, it is essential to maximize the sensitivity of the scanning system. In other words, the signal to noise ratio for the experimental setup must be maximized. This project aims at increasing the sensitivity of the NSECT system by understanding the various sources of noise and implementing techniques to reduce their effect. Noise in the system may originate from several factors such as the radiative background in the scanning room, and neutron scatter off of components of the system other than the target. Some of these effects can be reduced by using Time-of-Flight background reduction, while others can be reduced by acquiring a separate sample-out scan. Post processing background reduction techniques are also being developed for removing detector efficiency dependent noise. At this point we have acquired element information from whole mouse specimens and iron-overloaded liver models made of bovine liver tissue artificially injected with iron. Tomographic images have been generated from a solid iron and copper phantom. Our final goal is to implement a low-dose non-invasive scanning system for diagnosis of iron overload and breast cancer.
2) Monte-Carlo simulations in GEANT4
For each tomographic scan of a sample using NSECT, there are several acquisition parameters that can be varied. These parameters can broadly be classified into three categories: (i) Neutron Beam parameters: neutron flux, energy and beam width, (ii) Detector parameters: detector type, size, efficiency and location; (iii) Scanning Geometry: spatial and angular sampling rates. Due to the enormous number of combinations possible using these parameters, it is not feasible to investigate the effects of each parameter on the reconstructed image using a real neutron beam in the limited beam time available. A feasible alternative to this is to use Monte-Carlo simulations to reproduce the entire experiment in a virtual world. The effect of each individual parameter can then be studied using only computer processing time and resources. We use the high energy physics Monte-Carlo software package GEANT4, developed by CERN, which incorporates numerous tools required for building particle sources and detectors, and tracking particle interactions within them. The simulations built so far include the neutron source, HPGE and BGO gamma detectors, and several target materials such as iron, liver and breast tissue.
Our current research involves the use of computer-generated phantoms and simulation techniques to investigate and optimize medical imaging systems and methods. Medical imaging simulation involves virtual experiments carried out entirely on the computer using computational models for the patients as well as the imaging devices. Simulation is a powerful tool for characterizing, evaluating, and optimizing medical imaging systems. A vital aspect of simulation is to have realistic models of the subject's anatomy as well as accurate models for the physics of the imaging process. Without this, the results of the simulation may not be indicative of what would occur in actual clinical studies and would, therefore, have limited practical value. We are leading the development of realistic simulation tools for use toward human and small animal imaging research.
These tools have a wide variety of applications in many different imaging modalities to investigate the effects of anatomical, physiological, physical, and instrumentational factors on medical imaging and to research new image acquisition strategies, image processing and reconstruction methods, and image visualization and interpretation techniques. We are currently applying them to the field of x-ray CT. The motivation for this work is the lack of sufficiently rigorous methods for optimizing the image quality and radiation dose in x-ray CT to the clinical needs of a given procedure. The danger of unnecessary radiation exposure from CT applications, especially for pediatrics, is just now being addressed. Optimization is essential in order for new and emerging CT applications to be truly useful and not represent a danger to the patient. Given the relatively high radiation doses required of current CT systems, thorough optimization is unlikely to ever be done in live patients. It would be prohibitively expensive to fabricate physical phantoms to simulate a realistic range of patient sizes and clinical needs especially when physiologic motion needs to be considered. The only practical approach to the optimization problem is through the use of realistic computer simulation tools developed in our work.
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