KEYWORDS: Lung, Monte Carlo methods, Image quality, Image resolution, Phase modulation, Computed tomography, Tissues, 3D modeling, Natural surfaces, Breast
Using numerical simulations, the influence of various imaging parameters on the resulting image can be determined for various imaging technologies. To achieve this, visualization of fine tissue structures needed to evaluate the image quality with different radiation quality and dose is essential. The present work examines a method that employs simulations of the imaging process using Monte Carlo methods and a combination of a standard and higher resolution voxel models. A hybrid model, based on nonlinear uniform rational B-spline and polygon mesh surfaces, was constructed from an existing voxel model of a female patient of a resolution in the range of millimeters. The resolution of the hybrid model was 500 μm, i.e., substantially finer than that of the original model. Furthermore, a high resolution lung voxel model [(0.11 mm)3 voxel volume, slice thickness: 114 μm] was developed from the specimen of a left lung lobe. This has been inserted into the hybrid model, substituting its left lung lobe and resulting in a dual-lattice geometry model. “Dual lattice” means, in this context, the combination of voxel models with different resolutions. Monte Carlo simulations of radiographic imaging were performed and the fine structure of the lung was easily recognizable.
In radiography there is generally a conflict between the best image quality and the lowest possible patient dose. A proven
method of dosimetry is the simulation of radiation transport in virtual human models (i.e. phantoms). However, while the
resolution of these voxel models is adequate for most dosimetric purposes, they cannot provide the required organ fine
structures necessary for the assessment of the imaging quality.
The aim of this work is to develop hybrid/dual-lattice voxel models (called also phantoms) as well as simulation methods
by which patient dose and image quality for typical radiographic procedures can be determined. The results will provide
a basis to investigate by means of simulations the relationships between patient dose and image quality for various
imaging parameters and develop methods for their optimization.
A hybrid model, based on NURBS (Non Linear Uniform Rational B-Spline) and PM (Polygon Mesh) surfaces, was
constructed from an existing voxel model of a female patient. The organs of the hybrid model can be then scaled and
deformed in a non-uniform way i.e. organ by organ; they can be, thus, adapted to patient characteristics without losing
their anatomical realism. Furthermore, the left lobe of the lung was substituted by a high resolution lung voxel model,
resulting in a dual-lattice geometry model. “Dual lattice” means in this context the combination of voxel models with
different resolution.
Monte Carlo simulations of radiographic imaging were performed with the code EGS4nrc, modified such as to perform
dual lattice transport. Results are presented for a thorax examination.
Reliable estimates for patient doses for CT examinations are desirable for the patients themselves as well as for new
epidemiological studies. It has been shown that dose conversion coefficients normalized to CTDIvol provide rather scanner
independent quantities. In this work, it is demonstrated that this normalization provides also tube voltage independent
values by simulating axial CT scans of a seven-year old infant and an adult. The differences in the effective dose conversion
coefficients per CTDIvol between 80 and 120 kV is for most body heights below 5%. Only at the height of the testes and
the thyroid the difference can be as large as 15%. This results in differences of the effective dose conversion coefficient per
CTDIvol between 80 and 120 kV of less than 6-7% for typical CT examinations.
The dramatic increase of diagnostic imaging capabilities over the past decade has contributed to increased radiation
exposure to patient populations. Several factors have contributed to the increase in imaging procedures: wider
availability of imaging modalities, increase in technical capabilities, rise in demand by patients and clinicians,
favorable reimbursement, and lack of guidelines to control utilization. The primary focus of this research is to
provide in depth information about radiation doses that patients receive as a result of CT exams, with the initial
investigation involving abdominal CT exams. Current dose measurement methods (i.e. CTDIvol Computed
Tomography Dose Index) do not provide direct information about a patient's organ dose. We have developed a
method to determine CTDIvol normalized organ doses using a set of organ specific exponential regression
equations. These exponential equations along with measured CTDIvol are used to calculate organ dose estimates
from abdominal CT scans for eight different patient models. For each patient, organ dose and CTDIvol were
estimated for an abdominal CT scan. We then modified the DICOM Radiation Dose Structured Report (RDSR) to
store the pertinent patient information on radiation dose to their abdominal organs.
KEYWORDS: Medical imaging, Ionizing radiation, Computed tomography, Tissues, Data modeling, Medical diagnostics, Monte Carlo methods, Sensors, X-ray computed tomography, Particles
Radiation exposure due to medical imaging is a topic of emerging importance. In Europe this topic has been dealt with
for a long time and in other countries it is getting more and more important and it gets an aspect of public interest in the
latest years. This is mainly true due to the fact that the average dose per person in developed countries is increasing
rapidly since threedimensional imaging is getting more and more available and useful for diagnosis. This paper
introduces the most common dose quantities used in medical radiation exposure characterization, discusses usual ways
for determination of such quantities as well as some considerations how these values are linked to radiation risk
estimation. For this last aspect the paper will refer to the linear non threshold theory for an imaging application.
Radiation doses of radiopharmaceuticals to patients in nuclear medicine are, as the standard method, estimated by the
administered activity, medical imaging (e.g. PET imaging), compartmental modeling and Monte Carlo simulation of
radiation with reference digital human phantoms. However, in each of the contributing terms, individual uncertainty due
to measurement techniques, patient variability and computation methods may propagate to the uncertainties of the
calculated organ doses to the individual patient. To evaluate the overall uncertainties and the quality assurance of internal
absorbed doses, a method was developed within the framework of the MADEIRA Project (Minimizing Activity and
Dose with Enhanced Image quality by Radiopharmaceutical Administrations) to quantitatively analyze the uncertainties
in each component of the organ absorbed doses after administration of 18F-choline to prostate cancer patients undergoing
nuclear medicine diagnostics.
First, on the basis of the organ PET and CT images of the patients as well as blood and urine samples, a model structure
of 18F-choline was developed and the uncertainties of the model parameters were determined. Second, the model
parameter values were sampled and biokinetic modeling using these sampled parameter values were performed. Third,
the uncertainties of the new specific absorbed fraction (SAF) values derived with different phantoms representing
individual patients were presented. Finally, the uncertainties of absorbed doses to the patients were calculated by
applying the ICRP/ICRU adult male reference computational phantom. In addition to the uncertainty analysis, the
sensitivity of the model parameters on the organ PET images and absorbed doses was indicated by coupling the model
input and output using regression and partial correlation analysis.
The results showed that the uncertainty factors of absorbed dose to patients are in most cases less than a factor of 2
without taking into account the uncertainties caused by the variability and uncertainty of individual human phantoms.
The sensitivity study showed that the metabolic transfer parameter from the blood to soft tissues has a strong influence
on blood sample collection from the beginning until 500 min. post administration; the transfer pathways between blood
and liver impact strongly the liver imaging during the time course. The results of this study suggest that organ image
acquisition of liver and kidneys after 100 min. as well as blood and urine sample collection are necessary for the
reduction of uncertainties of absorbed dose estimates to patients.
The MADEIRA Project (Minimizing Activity and Dose with Enhanced Image quality by Radiopharmaceutical
Administrations), aims to improve the efficacy and safety of 3D functional imaging by optimizing, among others, the
knowledge of the temporal variation of the radiopharmaceuticals' uptake in and clearance from tumor and healthy
tissues. With the help of compartmental modeling it is intended to optimize the time schedule for data collection and
improve the evaluation of the organ doses to the patients.
Administration of 18F-choline to screen for recurrence or the occurrence of metastases in prostate cancer patients is one
of the diagnostic applications under consideration in the frame of the project. PET and CT images have been acquired up
to four hours after injection of 18F-choline. Additionally blood and urine samples have been collected and measured in a
gamma counter.
The radioactivity concentration in different organs and data of plasma clearance and elimination into urine were used to
set-up a compartmental model of the biokinetics of the radiopharmaceutical. It features a central compartment (blood)
exchanging with organs. The structure describes explicitly liver, kidneys, spleen, plasma and bladder as separate units
with a forcing function approach. The model is presented together with an evaluation of the individual and population
kinetic parameters, and a revised time schedule for data collection is proposed. This optimized time schedule will be
validated in a further set of patient studies.
For optimisation in diagnostic medical imaging it is important to consider the relation between diagnostic image quality
and patient dose. In the past, schematic representations of the human body were commonly used for dosimetric
simulations together with Monte Carlo codes. During the last two decades, voxel models were introduced as an
improvement to these body models. Studies performed by various research groups have shown that the more realistic
organ topology of voxel models constructed from medical image data of real persons has an impact on calculated doses
for external as well as internal exposures. As a consequence of these findings, the ICRP decided to use voxel models for
the forthcoming update of organ dose conversion coefficients. These voxel models should be representative of an
average population, i.e. they should resemble the ICRP reference anatomical data with respect to their external
dimensions and their organ masses. To meet the ICRP requirements, our group at the Helmholtz Zentrum München
(formerly known as GSF-National Research Center for Environment and Health) constructed voxel models of a male
and female adult, based on the voxel models of two individuals whose body height and weight resembled those of the
male and female ICRP reference adult. The organ masses of both models were adjusted to the ICRP reference anatomical
data, without spoiling their realistic anatomy. The paper describes the method used for this process and the resulting
voxel models.
For the numerical simulation of the radiation transport in the female breast, it is necessary to generate a numerical model of it. Despite of the success achieved by creating such models, for a more precise numerical dosimetry of the radiation transport and optimization of the imaging process, one needs more detailed numerical models. Unfortunately it is not always possible to appropriately accomplish this task on the base of data available today from conventional tomographic devices. The reason is a low contrast in the reconstructed image. This kind of problem was faced when attempting to segment the glandular and interlobular tissues in the 3D data of a breast specimen reconstructed on the Siemens CT device in the University Hospital in Magdeburg. To solve this problem, we have created a set of different 2D x-ray images of the breast specimen on the digital flat panel detector. The 3D distribution of the absorption function of the specimen was then reconstructed from the grey values of these images.
The 'European Guidelines on Quality Criteria for Diagnostic Radiographic Images' do not address the choice of film characteristic (H/D) curve, which is an important parameter for the description of a radiographic screen-film system. Since it is not possible to investigate this influence by taking repeated exposures of the same patients on films with systematically varied H/D curves, patient images of lumbar spine were digitised in the current study. The image contrast was altered by digital image processing techniques, simulating images with H/D curves varying from flat over standard latitude to a film type steeper than a mammography film. The manipulated images were printed on film for evaluation. Seven European radiologists evaluated the clinical image quality of in total 224 images by analysing the fulfilment of the European Image Criteria and by visual grading analysis of the images. The results show that the local quality can be significantly improved by the application of films with a steeper film H/D curve compared to the standard latitude film. For images with an average optical density of about 1.25, the application of the steeper film results in a reduction of patient absorbed dose by about 10-15% without a loss of diagnostically relevant image information. The results also show that the patient absorbed dose reduction obtained by altering the tube voltage from 70 kV to 90 kV coincides with a loss of image information that cannot be compensated for by simply changing the shape of the H/D curve.
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