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.
Despite the significant clinical benefits of computed tomography (CT) in providing diagnostic information for a broad range of diseases, concerns have been raised regarding the potential cancer risk induced by CT radiation exposure. In that regard, optimizing CT protocols and minimizing radiation dose have become the core problem for the CT community. To develop strategies to optimize radiation dose, it is crucial to effectively characterize CT image quality. Such image quality estimates need to be prospective to ensure that optimization can be performed before the scan is initiated. The purpose of this study was to establish a phantombased methodology to predict quantum noise in CT images as a first step in our image quality prediction. Quantum noise was measured using a variable-sized phantom under clinical protocols. The mathematical relationship between noise and water-equivalent-diameter (Dw) was further established. The prediction was achieved by ascribing a noise value to a patient according to the patient’s water-equivalent-diameter. The prediction accuracy was evaluated in anthropomorphic phantoms across a broad range of sizes, anatomy, and reconstruction algorithms. The differences between the measured and predicted noise were within 10% for anthropomorphic phantoms across all sizes and anatomy. This study proposed a practically applicable technique to predict noise in CT images. With a prospective estimation of image quality level, the scanning parameters can then by adjusted to ensure optimized imaging performance.
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.
KEYWORDS: Computed tomography, Motion models, 3D modeling, Data modeling, Monte Carlo methods, Image segmentation, Image quality, Detection and tracking algorithms, 3D acquisition, 3D image processing
With the increased use of CT examinations, the associated radiation dose has become a large concern, especially for pediatrics. Much research has focused on reducing radiation dose through new scanning and reconstruction methods. Computational phantoms provide an effective and efficient means for evaluating image quality, patient-specific dose, and organ-specific dose in CT. We previously developed a set of highly-detailed 4D reference pediatric XCAT phantoms at ages of newborn, 1, 5, 10, and 15 years with organ and tissues masses matched to ICRP Publication 89 values. We now extend this reference set to a series of 64 pediatric phantoms of a variety of ages and height and weight percentiles, representative of the public at large. High resolution PET-CT data was reviewed by a practicing experienced radiologist for anatomic regularity and was then segmented with manual and semi-automatic methods to form a target model. A Multi-Channel Large Deformation Diffeomorphic Metric Mapping (MC-LDDMM) algorithm was used to calculate the transform from the best age matching pediatric reference phantom to the patient target. The transform was used to complete the target, filling in the non-segmented structures and defining models for the cardiac and respiratory motions. The complete phantoms, consisting of thousands of structures, were then manually inspected for anatomical accuracy. 3D CT data was simulated from the phantoms to demonstrate their ability to generate realistic, patient quality imaging data. The population of pediatric phantoms developed in this work provides a vital tool to investigate dose reduction techniques in 3D and 4D pediatric CT.
While great advances are made toward making highly realistic, surface models of the human
anatomy, very little has been done to fill these bounded surfaces with models of anatomical
texture. We propose a method whereby realistic anatomically-based computed tomography (CT)
texture can be incorporated into voxelized versions of the 4D extended cardiac-torso (XCAT)
phantom. Our source of texture comes from patient CT scans from the Duke CT imaging
database. These image-sets were de-noised using anisotropic diffusion. Two organs were
selected from which texture was obtained, liver and lungs. From each organ, multiple regions of
interest (ROIs) were taken and tiled side-by-side to create a larger image. Textures for the liver
and lungs were extrapolated using ImageQuilting, based on the tiled images. Next, a NURBSbased
XCAT phantom was voxelized at the same resolution as the textures. The texture was then
placed in the voxelized phantoms. Finally, CT simulations of the phantoms with and without the
textures were compared against each other, using the power spectral density. This work shows
that there is a way whereby the XCAT phantoms can be textured to give more realistic
appearance in CT simulations. It is anticipated that this method would find great use in making
projections of the XCAT phantom look more realistic and allow for the phantoms to not only be
utilized in dosimetrical evaluations, but in image quality studies as well.
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 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.
In order to discuss the cost-benefit ratio of CT examinations in children, one must be familiar with the reasons why CT can provide a high collective or individual dose. The reasons include increasing CT use as well as lack of attention to dose reduction strategies. While those have been substantial efforts for dose reduction, additional work is necessary to prevent unnecessary radiation exposure. This responsibility is shared between science and medicine, industry, regulatory agencies, and patients as well.
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.
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.
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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