The purpose of this study was to formulate a systematic, evidence-based method to relate quantitative diagnostic performance to radiation dose, enabling a multidimensional system to optimize computed tomography imaging across pediatric populations. Based on two prior foundational studies, radiation dose was assessed in terms of organ doses, effective dose (E), and risk index for 30 patients within nine color-coded pediatric age-size groups as a function of imaging parameters. The cases, supplemented with added noise and simulated lesions, were assessed in terms of nodule detection accuracy in an observer receiving operating characteristic study. The resulting continuous accuracy–dose relationships were used to optimize individual scan parameters. Before optimization, the nine protocols had a similar E of 2.2±0.2 mSv with accuracy decreasing from 0.89 for the youngest patients to 0.67 for the oldest. After optimization, a consistent target accuracy of 0.83 was established for all patient categories with E ranging from 1 to 10 mSv. Alternatively, isogradient operating points targeted a consistent ratio of accuracy–per-unit-dose across the patient categories. The developed model can be used to optimize individual scan parameters and provide for consistent diagnostic performance across the broad range of body sizes in children.
KEYWORDS: Monte Carlo methods, Computed tomography, Abdomen, Polymethylmethacrylate, Chest, X-ray computed tomography, Lithium, Modulation, Medical imaging, Medical physics
The purpose of this study was to extend the concept of weighted CT dose index (CTDIw) to the elliptical phantoms. Based on the published body dimension data, eight body aspect ratios were chosen between 1 (perfectly circular) and 1.72 (extremely elliptical). For each aspect ratio, two elliptical cylinders were created digitally to represent adult and pediatric bodies. Their cross-sectional areas were identical to the standard 32- and 16-cm CTDI phantoms. For each phantom, CTDI100 at center and periphery were simulated for tube voltages between 70 and 140 kVp using a validated Monte Carlo program. The simulations also provided the average dose over the cross-sectional area, CTDIxsec. Values of CTDIxsec and CTDI100 allowed linear systems of equations to be established, from which central and peripheral weighting coefficients were solved. Regardless of phantom shape, only two weighting coefficients were needed: w1 for the central CTDI100 and w2 for the average of the four peripheral CTDI100’s. Over the full range of aspect ratios, w1 increased linearly from 0.37 to 0.46, whereas w2 decreased linearly from 0.63 to 0.54, allowing the concept of CTDIw to be readily extended to the elliptical phantoms. When cross-sectional area (hence volume) was kept constant, all phantoms had the same CTDIxsec regardless of shape.
In Monte Carlo simulation of organ dose for a chest CT scan, many input parameters are required (e.g., half-value layer of the x-ray energy spectrum, effective beam width, and anatomical coverage of the scan). The input parameter values are provided by the manufacturer, measured experimentally, or determined based on typical clinical practices. The goal of this study was to assess the uncertainties in Monte Carlo simulated organ dose as a result of using input parameter values that deviate from the truth (clinical reality). Organ dose from a chest CT scan was simulated for a standard-size female phantom using a set of reference input parameter values (treated as the truth). To emulate the situation in which the input parameter values used by the researcher may deviate from the truth, additional simulations were performed in which errors were purposefully introduced into the input parameter values, the effects of which on organ dose per CTDIvol were analyzed. Our study showed that when errors in half value layer were within ± 0.5 mm Al, the errors in organ dose per CTDIvol were less than 6%. Errors in effective beam width of up to 3 mm had negligible effect (< 2.5%) on organ dose. In contrast, when the assumed anatomical center of the patient deviated from the true anatomical center by 5 cm, organ dose errors of up to 20% were introduced. Lastly, when the assumed extra scan length was longer by 4 cm than the true value, dose errors of up to 160% were found. The results answer the important question: to what level of accuracy each input parameter needs to be determined in order to obtain accurate organ dose results.
Portable x-ray examinations often account for a large percentage of all radiographic examinations. Currently, portable examinations do not employ automatic exposure control (AEC). To aid in the design of a size-specific technique chart, acrylic slabs of various thicknesses are often used to estimate x-ray transmission for patients of various body thicknesses. This approach, while simple, does not account for patient anatomy, tissue heterogeneity, and the attenuation properties of the human body. To better account for these factors, in this work, we determined x-ray transmission factors using computational patient models that are anatomically realistic. A Monte Carlo program was developed to model a portable x-ray system. Detailed modeling was done of the x-ray spectrum, detector positioning, collimation, and source-to-detector distance. Simulations were performed using 18 computational patient models from the extended cardiac-torso (XCAT) family (9 males, 9 females; age range: 2-58 years; weight range: 12-117 kg). The ratio of air kerma at the detector with and without a patient model was calculated as the transmission factor. Our study showed that the transmission factor decreased exponentially with increasing patient thickness. For the range of patient thicknesses examined (12-28 cm), the transmission factor ranged from approximately 21% to 1.9% when the air kerma used in the calculation represented an average over the entire imaging field of view. The transmission factor ranged from approximately 21% to 3.6% when the air kerma used in the calculation represented the average signals from two discrete AEC cells behind the lung fields. These exponential relationships may be used to optimize imaging techniques for patients of various body thicknesses to aid in the design of clinical technique charts.
KEYWORDS: Modulation, Computed tomography, Monte Carlo methods, Radiation effects, Scanners, 3D modeling, Diagnostics, Physics, Signal attenuation, Radiology
In an environment in which computed tomography (CT) has become an indispensable diagnostic tool employed with great frequency, dose concerns at the population level have become a subject of public attention. In that regard, optimizing radiation dose has become a core problem to the CT community. As a fundamental step to optimize radiation dose, it is crucial to effectively quantify radiation dose for a given CT exam. Such dose estimates need to be patient-specific to reflect individual radiation burden. It further needs to be prospective so that the scanning parameters can be dynamically adjusted before the scan is performed. The purpose of this study was to prospectively estimate organ dose in abdominopelvic CT exams under tube current modulation (TCM). CTDIvol-normalized-organ dose coefficients ( hfixed ) for fixed tube current were first estimated using a validated Monte Carlo simulation program and 58 computational phantoms. To account for the effect of TCM scheme, a weighted CTDIvol was computed for each organ based on the tube current modulation profile. The organ dose was predicted by multiplying the weighted CTDIvol with the organ dose coefficients ( hfixed ). To quantify prediction accuracy, each predicted organ dose was compared with organ dose simulated from Monte Carlo program with TCM profile explicitly modeled. The predicted organ dose showed good agreement with simulated organ dose across all organs and modulation strengths. For an average CTDIvol of a CT exam of 10 mGy, the absolute median error across all organs were 0.64 mGy (-0.21 and 0.97 for 25th and 75th percentiles, respectively). The percentage differences (normalized by CTDIvol of the exam) were within 15%. This study developed a quantitative model to predict organ dose under clinical abdominopelvic scans. Such information may aid in the optimization of CT protocols.
The purpose of this study was to evaluate how different implementations of the tube
current modulation (TCM) technology affect organ dose conversion factors in chest CT
and how organ dose can be accurately estimated for various modulation schemes.
Computational phantom of a normal-weight female patient was used. A method was
developed to generate tube current (mA) modulation profiles based on the attenuation of
the phantom, taking into account the geometry of the CT system as well as the x-ray
energy spectrum and bowtie filtration in a CT scan. The mA for a given projection angle
was calculated as a power-law function of the attenuation along this projection. The
exponent of this function, termed modulation control strength, was varied from 0 to 1 to
emulate the effects of different TCM schemes. Organ dose was estimated for a chest scan
for each modulation scheme and was subsequently normalized by volume-weighted CT
dose index (CTDIvol) to obtain conversion factors. The results showed that the conversion
factors are second-order polynomial functions of the modulation control strength. The
conversion factors established for a fixed-mA scan may be used to estimate organ dose in
a TCM scan. For organs on the periphery of the scan coverage, the best accuracy is
achieved when using CTDIvol computed from the average mA of the entire scan. For
organs inside the scan coverage, the best accuracy is achieved when using CTDIvol
computed from the volume-averaged mA values of all the axial slices containing the
organ.
There are three main x-ray based modalities for imaging the thorax: radiography, tomosynthesis, and CT. CT provides
perhaps the highest level of feature resolution but at notably higher radiation dose. To implement the ALARA (as low as
reasonable achievable) principle in making an appropriate choice between standard chest projection imaging,
tomosynthesis, and CT to achieve the lowest possible dose to patients, the effective doses and risk indices for each
modality should be accurately known. In this study, we employed 59 computational anthropomorphic male and female
extended cardiac-torso (XCAT) adult phantoms and a Monte Carlo simulation program (PENELOPE, version 2006,
Universitat de Barcelona, Spain). Effective dose and risk index was estimated for a clinical radiography system enabling
to conduct chest radiography and tomosynthesis sweep (Definium 8000, Volume RAD, GE Healthcare) and a clinical
CT system (LightSpeed VCT, GE Healthcare). It was found that the absolute effective dose and risk index increased
greatly with increasing patient size for CT, while these two dose metrics only increased slightly for radiography and
tomosynthesis. This suggests that it is important to specify patient size when comparing radiation dose across imaging
modalities.
KEYWORDS: Computed tomography, Scanners, Monte Carlo methods, Cancer, Medicine, Digital Light Processing, X-ray computed tomography, Tissues, 3D modeling, Physics
The purpose of this work was twofold: (a) to estimate patient- and cohort-specific radiation
dose and cancer risk index for abdominopelvic computer tomography (CT) scans; (b) to
evaluate the effects of patient anatomical characteristics (size, age, and gender) and CT
scanner model on dose and risk conversion coefficients. The study included 100 patient
models (42 pediatric models, 58 adult models) and multi-detector array CT scanners from two
commercial manufacturers (LightSpeed VCT, GE Healthcare; SOMATOM Definition Flash,
Siemens Healthcare). A previously-validated Monte Carlo program was used to simulate
organ dose for each patient model and each scanner, from which DLP-normalized-effective
dose (k factor) and DLP-normalized-risk index values (q factor) were derived. The k factor
showed exponential decrease with increasing patient size. For a given gender, q factor showed
exponential decrease with both increasing patient size and patient age. The discrepancies in k
and q factors across scanners were on average 8% and 15%, respectively. This study
demonstrates the feasibility of estimating patient-specific organ dose and cohort-specific
effective dose and risk index in abdominopelvic CT requiring only the knowledge of patient
size, gender, and age.
The relationship between theoretical descriptions of imaging performance (Fourier-based) and the
performance of real human observers was investigated for detection tasks in multi-slice CT. The detectability
index for the Fisher-Hotelling model observer and non-prewhitening model observer (with and without
internal noise and eye filter) was computed using: 1) the measured modulation transfer function (MTF) and
noise-power spectrum (NPS) for CT; and 2) a Fourier description of imaging task. Based upon CT images of
human patients with added simulated lesions, human observer performance was assessed via an observer
study in terms of the area under the ROC curve (Az). The degree to which the detectability index correlated
with human observer performance was investigated and results for the non-prewhitening model observer with
internal noise and eye filter (NPWE) were found to agree best with human performance over a broad range of
imaging conditions. Results provided initial validation that CT image acquisition and reconstruction
parameters can be optimized for observer performance rather than system performance (i.e., contrast-to-noise
ratio, MTF, and NPS). The NPWE model was further applied for the comparison of FBP with a novel modelbased
iterative reconstruction algorithm to assess its potential for dose reduction.
The effective dose associated with computed tomography (CT) examinations is often
estimated from dose-length product (DLP) using scanner-independent conversion
coefficients. Such conversion coefficients are available for a small number of
examinations, each covering an entire region of the body (e.g., head, neck, chest,
abdomen and/or pelvis). Similar conversion coefficients, however, do not exist for
examinations that cover a single organ or a sub-region of the body, as in the case of a
multi-phase liver examination. In this study, we extended the DLP-to-effective dose
conversion coefficient (k factor) to a wide range of body CT protocols and derived the
corresponding DLP-to-cancer risk conversion coefficient (q factor). An extended cardiactorso
(XCAT) computational model was used, which represented a reference adult male
patient. A range of body CT protocols used in clinical practice were categorized based on
anatomical regions examined into 10 protocol classes. A validated Monte Carlo program
was used to estimate the organ dose associated with each protocol class. Assuming the
reference model to be 20 years old, effective dose and risk index (an index of the total
risk for cancer incidence) were then calculated and normalized by DLP to obtain the k and q factors. The k and q factors varied across protocol classes; the coefficients of
variation were 28% and 9%, respectively. The small variation exhibited by the q factor
suggested the feasibility of universal q factors for a wide range of body CT protocols.
Radiation-dose awareness and optimization in CT can greatly benefit from a dosereporting
system that provides radiation dose and cancer risk estimates specific to each
patient and each CT examination. Recently, we reported a method for estimating patientspecific
dose from pediatric chest CT. The purpose of this study is to extend that effort to
patient-specific risk estimation and to a population of pediatric CT patients. Our study
included thirty pediatric CT patients (16 males and 14 females; 0-16 years old), for whom
full-body computer models were recently created based on the patients' clinical CT data.
Using a validated Monte Carlo program, organ dose received by the thirty patients from a
chest scan protocol (LightSpeed VCT, 120 kVp, 1.375 pitch, 40-mm collimation,
pediatric body scan field-of-view) was simulated and used to estimate patient-specific
effective dose. Risks of cancer incidence were calculated for radiosensitive organs using
gender-, age-, and tissue-specific risk coefficients and were used to derive patientspecific
effective risk. The thirty patients had normalized effective dose of 3.7-10.4 mSv/100 mAs and normalized effective risk of 0.5-5.8 cases/1000 exposed persons/100 mAs. Normalized lung dose and risk of lung cancer correlated strongly with average chest diameter (correlation coefficient: r = -0.98 to -0.99). Normalized effective risk also correlated strongly with average chest diameter (r = -0.97 to -0.98). These strong correlations can be used to estimate
patient-specific dose and risk prior to or after an imaging study to potentially guide healthcare providers in justifying CT examinations and to guide individualized protocol design and optimization.
The current estimations of risk associated with medical imaging procedures rely on assessing the organ dose
via direct measurements or simulation. Each organ dose is assumed to be homogeneous, a representative
sample or mean of which is weighted by a corresponding tissue weighting factor provided by ICRP
publication 103. The weighted values are summed to provide Effective Dose (ED), the most-widely accepted
surrogate for population radiation risk. For individual risk estimation, one may employ Effective Risk (ER),
which further incorporates gender- and age-specific risk factors. However, both the tissue-weighting factors
(as used by ED) and the risk factors (as used by ER) were derived (mostly from the atomic bomb survivor
data) under the assumption of a homogeneous dose distribution within each organ. That assumption is
significantly violated in most medical imaging procedures. In chest CT, for example, superficial organs (eg,
breasts) demonstrate a heterogeneous distribution while organs on the peripheries of the irradiation field (eg,
liver) possess a nearly discontinuous dose profile. Projection radiography and mammography involve an
even wider range of organ dose heterogeneity spanning up to two orders of magnitude. As such, mean dose
or point measured dose values do not reflect the maximum energy deposited per unit volume of the organ, and
therefore, effective dose or effective risk, as commonly computed, can misrepresent irradiation risk. In this
paper, we report the magnitude of the dose heterogeneity in both CT and projection x-ray imaging, provide an
assessment of its impact on irradiation risk, and explore an alternative model-based approach for risk
estimation for imaging techniques involving heterogeneous organ dose distributions.
KEYWORDS: Data modeling, Intestine, Computed tomography, Computer simulations, Tissues, Kidney, Monte Carlo methods, Image segmentation, 3D modeling, Chest
The purpose of this study is to develop a method for estimating patient-specific dose from
abdomen-pelvis CT examinations and to investigate dose variation across patients in the
same weight group. Our study consisted of seven pediatric patients in the same
weight/protocol group, for whom full-body computer models were previously created
based on the patients' CT data obtained for clinical indications. Organ and effective dose
of these patients from an abdomen-pelvis scan protocol (LightSpeed VCT scanner,
120-kVp, 85-90 mA, 0.4-s gantry rotation period, 1.375-pitch, 40-mm beam collimation, and
small body scan field-of-view) was calculated using a Monte Carlo program previously
developed and validated for the same CT system. The seven patients had effective dose
of 2.4-2.8 mSv, corresponding to normalized effective dose of
6.6-8.3 mSv/100mAs
(coefficient of variation: 7.6%). Dose variations across the patients were small for large
organs in the scan coverage (mean: 6.6%; range: 4.9%-9.2%), larger for small organs in
the scan coverage (mean: 10.3%; range: 1.4%-15.6%), and the largest for organs partially
or completely outside the scan coverage (mean: 14.8%; range:
5.7%-27.7%). Normalized
effective dose correlated strongly with body weight (correlation coefficient: r = -0.94).
Normalized dose to the kidney and the adrenal gland correlated strongly with mid-liver
equivalent diameter (kidney: r = -0.97; adrenal glands:
r = -0.98). Normalized dose to the
small intestine correlated strongly with mid-intestine equivalent diameter (r = -0.97).
These strong correlations suggest that patient-specific dose may be estimated for any
other child in the same size group who undergoes the abdomen-pelvis scan.
KEYWORDS: Computed tomography, 3D modeling, Bone, Image segmentation, Data modeling, Mathematical modeling, Natural surfaces, Medical imaging, Chest, Algorithm development
We create a series of detailed computerized phantoms to estimate patient organ and effective dose in pediatric CT and
investigate techniques for efficiently creating patient-specific phantoms based on imaging data. The initial anatomy of
each phantom was previously developed based on manual segmentation of pediatric CT data. Each phantom was
extended to include a more detailed anatomy based on morphing an existing adult phantom in our laboratory to match
the framework (based on segmentation) defined for the target pediatric model. By morphing a template anatomy to
match the patient data in the LDDMM framework, it was possible to create a patient specific phantom with many
anatomical structures, some not visible in the CT data. The adult models contain thousands of defined structures that
were transformed to define them in each pediatric anatomy. The accuracy of this method, under different conditions, was
tested using a known voxelized phantom as the target. Errors were measured in terms of a distance map between the
predicted organ surfaces and the known ones. We also compared calculated dose measurements to see the effect of
different magnitudes of errors in morphing. Despite some variations in organ geometry, dose measurements from
morphing predictions were found to agree with those calculated from the voxelized phantom thus demonstrating the
feasibility of our methods.
The purpose of this study is to evaluate the effect of reduced tube current, as a surrogate
for radiation dose, on lung nodule detection in pediatric chest multi-detector CT (MDCT).
Normal chest MDCT images of 13 patients aged 1 to 7 years old were used as templates
for this study. The original tube currents were between 70 mA and 180 mA. Using
proprietary noise addition software, noise was added to the images to create 13 cases at
the lowest common mA (i.e. 70 mA), 13 cases at 35 mA (50% reduction), and 13 cases at
17.5 mA (75% reduction). Three copies of each case were made for a total of 117 series
for simulated nodule insertion. A technique for three-dimensional simulation of small
lung nodules was developed, validated through an observer study, and used to add
nodules to the series. Care was taken to ensure that each of three lung zones (upper,
middle, lower) contained 0 or 1 nodule. The series were randomized and the presence of
a nodule in each lung zone was rated independently and blindly by three pediatric
radiologists on a continuous scale between 0 (definitely absent) and 100 (definitely
present). Receiver operating characteristic analysis of the data showed no general
significant difference in diagnostic accuracy between the reduced mA values and 70 mA,
suggesting a potential for dose reduction with preserved diagnostic quality. To our
knowledge, this study is the first controlled, systematic, and task-specific assessment of
the influence of dose reduction in pediatric chest CT.
In recent years, there has been a desire to reduce CT radiation dose to children because of their susceptibility and
prolonged risk for cancer induction. Concerns arise, however, as to the impact of dose reduction on image quality and
thus potentially on diagnostic accuracy. To study the dose and image quality relationship, we are developing a
simulation code to calculate organ dose in pediatric CT patients. To benchmark this code, a cylindrical phantom was
built to represent a pediatric torso, which allows measurements of dose distributions from its center to its periphery.
Dose distributions for axial CT scans were measured on a 64-slice multidetector CT (MDCT) scanner (GE Healthcare,
Chalfont St. Giles, UK). The same measurements were simulated using a Monte Carlo code (PENELOPE, Universitat de
Barcelona) with the applicable CT geometry including bowtie filter. The deviations between simulated and measured
dose values were generally within 5%. To our knowledge, this work is one of the first attempts to compare measured
radial dose distributions on a cylindrical phantom with Monte Carlo simulated results. It provides a simple and effective
method for benchmarking organ dose simulation codes and demonstrates the potential of Monte Carlo simulation for
investigating the relationship between dose and image quality for pediatric CT patients.
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