Download Radiation Dose and Safety: Informatics Standards and Tools

Radiation Dose and Safety:
Informatics Standards and Tools
Richard L. Morin, PhDa, J. Anthony Seibert, PhDb, John M. Boone, PhDc
Quality and safety improvements in radiology and medical imaging are substantially affected by radiation dose
and its relationship to image quality and patient safety. Because radiation dose has many definitions and
meanings, familiarity with and understanding of the basic nuances are important; modalities in general use
radiation dose metrics that differ from patient radiation dose. Dose metric data differ among CT, interventional imaging, and digital radiography modalities. Informatics standards and tools assist in the extraction,
collation, and analysis of these data and are described here. An informatics infrastructure can provide a
pathway to automatically track and record dose metrics individually at the patient level and collectively
through a regional or national radiation dose registry. Comparison of reference dose benchmarks to local and
national practice values allows personnel at a given institution to objectively evaluate and optimize imaging
procedures in regard to radiation dose metrics. Ultimately, enhanced patient care and safety are achieved.
Key Words: Radiation dose, CT, interventional imaging, radiography, informatics tools, radiation dose
registry
J Am Coll Radiol 2014;11:1286-1297. © 2014 Published by Elsevier Inc. on behalf of American College of
Radiology
INTRODUCTION
Radiation dose for diagnostic medical imaging examinations has recently come under intense scrutiny, triggered by
media coverage of overexposures in CT by The New York
Times and other newspapers [1], and by recent epidemiological studies on radiation dose and risk in the literature
[2,3]. With public awareness piqued across the nation,
legislative activity, initially in California, culminated in a
dose-reporting law for CT that requires radiation dose
metrics to be noted in the interpreting physician’s report
[4]. Even though CT collectively represents about 40% of
the overall radiation dose delivered to patients for medical
imaging procedures [5], for about 10% of the overall volume, radiation dose and safety for all imaging modalities
using ionizing radiation is a concern (Fig. 1). Aside from
CT, these modalities include interventional radiology/
cardiology/vascular surgery, fluoroscopy (in many different
settings), radiography, mammography, and nuclear medicine planar/tomographic imaging.
Collectively, the health industry is moving toward safer
and more effective diagnostic imaging by using optimized
a
Department of Radiology, Mayo Clinic Jacksonville, Jacksonville, Florida.
b
Department of Radiology, University of California Davis Medical Center,
Sacramento, California.
c
Departments of Radiology and Biomedical Engineering, University of California Davis Medical Center, Sacramento, California.
Corresponding author and reprints: Richard L. Morin, PhD, BrooksHollern Professor, Department of Radiology, Mayo Clinic Jacksonville,4500 San Pablo Road, Jacksonville, FL 32224; e-mail: morin@mayo.
edu.
1286
acquisition protocols, implementing dose-reduction
technologies, measuring and reporting dose indices,
participating in dose registries, identifying reference dose
standards, and providing feedback to identify outliers and
optimize the utilization of ionizing radiation. The goals
are to reduce corresponding risks of radiation, choose the
most appropriate exam, render an accurate diagnosis, and
provide the best patient care as safely as possible. Part of
this effort involves raising awareness of imaging examination dose metrics on the part of the interpreting
physician, referring physicians, technologists, medical
physicists, caregivers, administrators, and others involved
in the oversight of medical imaging. Retrieving and using
this information is an informatics challenge, which requires knowledge of the DICOM standards [6]. In
addition, an understanding of the corresponding relevant
metadata and structured reporting objects is required, as
well as of efforts of the Integrating the Healthcare Enterprise (IHE) organization in describing the radiation
exposure monitoring (REM) profile [7] to identify the
relevant parameters and information to be extracted.
INFORMATICS TOOLS IN THE PURSUIT OF
PERSONALIZED DOSE TRACKING
Informatics plays a crucial role in providing information
on radiation dose delivered to patients during an exam,
the dose distributions for specific protocols, and identifying and comparing the current delivered dose to the
mean dose at an institution, local area, or in regional and
national registry databases. Many challenges arise in the
ª 2014 Published by Elsevier Inc. on behalf of American College of Radiology
1546-1440/14/$36.00 http://dx.doi.org/10.1016/j.jacr.2014.09.017
Morin, Seibert, Boone/Radiation Dose and Safety 1287
Fig 1. The charts show the medical imaging modalities contributing to the collective effective radiation dose to the population of the United States [5]. Left: The chart shows all exposure categories contributing to the annual collective dose
(percentage) in 2006; note the large fraction (24%) contributed by CT. Right: The chart shows the distribution of CT exams
performed in 2006 that contribute to the percentage estimated radiation dose burden (S in person-sievert), indicating that
the largest fraction is attributable to abdominal CT exams.
area of informatics, including (1) measuring/extracting
radiation dose metrics per exam; (2) using standards to
gather/store information (DICOM metadata and
DICOM Radiation Dose Structured Report [RDSR] and
template information [8,9]); (3) tracking patient radiation
dose histories; (4) accumulating dose per patient (whether
and how to do this is an area of debate); (5) decision
making for future imaging exams; and (6) sending information to regional and national databases as a method
of comparing protocols and imaging practices.
ESTIMATING RADIATION DOSE
As imaging technology advances, methods to estimate
and assess patient radiation dose advance as well, with
many capabilities being introduced in DICOM standards and IHE profiles on an ongoing basis. Each modality described in this article (CT, interventional
vascular imaging, and radiography) has unique dose
metrics and indices that can be captured to determine
radiation output from the machine or incident levels of
radiation to the detector for specific protocols and/or
exams. Radiation dose metrics are not patient dose;
however, the metrics can often be used to estimate the
radiation dose to the patient, if the metrics’ limitations
are taken into account. Unfortunately, such estimations
are not easy to make because the modalities provide
different dose metrics and units to different areas of the
anatomy over either short or extended time periods,
often from different clinics or institutions.
The ultimate goal is to achieve personalized dose
tracking for all ionizing radiation imaging exams over a
patient’s lifetime—ostensibly for the purpose of determining risk from ionizing radiation for the “next” exam.
However, the fact is that we live in a world with natural
background radiation, and we have repair mechanisms that
can potentially obviate the damage caused by previous
events; at low levels of radiation exposure (typical of most
diagnostic imaging procedures), demonstrating any deleterious impact or increase of risk, or a range of other biological, physical, societal, and political effects, is extremely
difficult. The ultimate goal requires a perspective that is
based primarily on safety and benefit for the patient.
Many methods are available to describe radiation dose,
including absorbed dose, peak skin dose, threshold (tissue
reaction) dose, cumulative dose, equivalent dose, and
effective dose. Each is used for specific purposes. Radiation
“dose effects” include stochastic effects and tissue reaction
(deterministic) effects. The reader is referred to an excellent
paper [10] for precise definitions of dose and dose effects.
Dose measurements are achieved by measuring X-ray
beam ionization events in a calibrated air-filled ionization
chamber, a calibrated scintillation detector, a thermoluminescent detector, or other storage detector; or by using
computer-simulation studies (Monte-Carlo) to track the
energy deposition in air or a tissue medium. The basic unit
of absorbed dose is the gray (Gy), equal to 1 joule of energy
deposited in 1 kilogram of medium. A common measurement is air kerma (kinetic energy released in a small
volume of air), made using an ionization chamber and
electrometer to measure the incident energy, usually in
mGy units. Other dose values are not directly measured,
but rather calculated, such as equivalent dose and effective
dose, and are expressed in sieverts (Sv). Equivalent dose
takes into account enhanced biological effects of particulate
ionizing radiation compared to X-rays for the same
deposited dose. Effective dose provides a method to estimate the whole-body dose risk from a partial irradiation of
the body [11] by considering the absorbed dose of critical
1288 Journal of the American College of Radiology/Vol. 11 No. 12PB December 2014
organs in the exam times a predetermined risk-weighting
factor for each critical organ; the individual products are
summed.
Effective dose is a concept that was developed by the
International Commission on Radiation Protection [12],
which seeks to prorate partial-body radiation exposures
such as those performed in most diagnostic imaging exams
to a whole-body exposure of radiation with the same risk.
The technical basis of computing effective dose requires
that a collection of organ doses be assessed (in absorbed
dose units, mGy); then, a series of tissue-weighting factors
are used for each organ to prorate the risk. The weighting
factors sum to unity. In general, measuring organ doses for
all examinations is not practical or easy; however, several
software packages are designed to estimate organ dose
using Monte Carloederived data, mathematical phantoms, and a number of simplifying assumptions. The
effective dose is not really a dose at all, but rather a measure
of risk reported in mSv. The concept of effective dose was
never devised with the intention of producing risk estimates for an individual patient, but rather for assessing
risks from larger populations of individuals (eg, all patients
having a head CT scan, interventional fluoroscopy procedure, or nuclear medicine exam). The tissue-weighting
factors for various organs are provided in Table 1.
Direct methods to estimate effective dose have been
established with the use of conversion factors experimentally determined for specific anatomy and image acquisitions from the dose metric values provided by CT,
fluoroscopy, and projection imaging, as described in the
following sections. Although the effective dose was originally proposed as a population metric based on a “standard man” model [12], its use has proliferated (perhaps
inappropriately) for personal “dose” measurements and
estimates of risk.
HOW ARE RADIATION DOSES ADDED?
Because effective dose is normalized to the whole body,
effective dose values can be added; however, adding
absorbed doses for different organs or body parts is
Table 1. International Commission on Radiation
Protection, IRCP-103 organ-weighting factors
Organ
Weighting factor wt
Gonads
Bone marrow
Colon
Lung
Stomach
Bladder
Esophagus
Thyroid
Skin
Bone surface
Brain
Salivary glands
Remainder
Note: Table is from Reference [12].
0.08
0.12
0.12
0.12
0.12
0.04
0.04
0.04
0.01
0.01
0.01
0.01
0.12
inappropriate, because of variable biological sensitivities
and outcomes. For instance, if the forefinger is irradiated
with 10,000 mGy, and the abdomen with 2 mGy, it
would not make sense to directly add the doses together
(10,002 mGy). However, normalizing the biological
impact of the finger dose and the abdominal dose relative to the whole body, which can be accomplished by
using the effective dose paradigm, would result in almost
zero impact from the finger dose and a larger impact
from the abdominal dose. The sum of the effective doses
would provide an overall assessment of risk.
If the same region of the body is irradiated, it is
usually appropriate to add the dose metric values, eg,
peak-skin dose mapping for an interventional examination or the radiation dose metrics generated for a precontrast and postcontrast CT scan of the abdomen. In
situations in which tissue doses and individual organ
doses can be estimated and tracked (eg, using a Monte
Carlo photon transport computer-simulation algorithm), doses for the same organ or tissue can be added.
Adding radiation dose metrics generated from a modality without regard to where the radiation dose is
deposited is inappropriate, and in many cases incorrect.
MEASURING RADIATION DOSE IN CT
CT generates the largest collective radiation dose to the
population undergoing medical imaging studies (Fig. 1
[left]) and has been under intense scrutiny over the past
several years, in part because of radiation overdose incidents
[1] and subsequent reporting requirements for CT dose
metrics [4]. This situation places added importance on how
CT doses can be appropriately optimized by considering
and employing dose-reduction tools, and by understanding
CT dose metrics, the importance of patient size in estimating patient dose with informatics tools, how to more
accurately estimate dose for a specific patient, and how to
extract and report radiation dose metrics in CT.
CT Dose-Reduction Technology
The dose-reduction tools that are available on CT systems are far more robust on modern scanners than those
available a decade ago. A brief overview of these tools is
provided below.
Automatic exposure control. Automatic exposure
control involves the use of a parameter that relates to a
preselected image-quality setting (for 1 vendor) or an Xray tube-current setting for a standard-sized patient (for
other vendors). Automatic exposure control generally
increases the milliamperes (mA) for larger patients and
decreases it for smaller patients. This tool brings to CT
what the radiology community has used in radiography
(“phototiming”) for several decades.
Tube-current modulation. The concept of modulation
here is to maintain the average X-ray transmission in CT
projections so that the noise is relatively constant for all
projections during the reconstruction; this constancy
Morin, Seibert, Boone/Radiation Dose and Safety 1289
allows the thinner or less-attenuating projections of the
body to be subjected to less radiation (by turning down
the mA) than the thicker or more-attenuating projections (eg, anteroposterior versus lateral in an
abdominal exam, less-attenuating lungs versus moreattenuating soft tissue in a chest/abdomen/pelvis exam).
z-dimension) that would otherwise be exposed and not
contribute to the image-formation process. Adaptive
collimator systems are particularly effective for highpitch helical exams using large collimator widths on
MDCT systems, such as 40 mm.
Iterative reconstruction methods. By analyzing and
accounting for the photon and electronic noise statistics
in the CT projections relative to a projection model of
the patient CT image, a reduction in the difference
between the estimated model and the measured image
data is used to improve successive iterative steps such
that noise is selectively reduced. A model-based method
that takes into account characteristics of the imageformation process (eg, detector configuration, focal
spot dimensions, scanning geometry) can reduce statistical variations much more than traditional filtered
backprojection reconstruction techniques used in CT,
but at the expense of greater computation time. Iterative
reconstruction techniques do not produce dose reduction on their own; however, when used in conjunction
with lower technique settings (ie, a reduction in mAs),
iterative reconstruction techniques do contribute to the
ability to scan patients using lower dose settings.
CT dose index (CTDI) as a CT dose metric was
described in the late 1970s [13] as a method of using a
single scan to estimate the multiple scan average dose of
a 10-cmelong contiguous CT scan. The CTDI family
of metrics (CTDI100, weighted CTDI [CTDIw], and
volume CTDI [CTDIvol]; these terms are explained in
the next paragraph) are surrogates for the relative dose
output of a CT scanner for a given set of technique
factors [14]. The most commonly used dose metrics
include CTDIvol and the dose-length product (DLP),
which are specifically dose metrics and not radiation dose
quantities, and they do not directly represent the radiation dose to patients [15].
The CTDIvol is derived from several air kerma measurements made on a CT scanner using a cylindrical polymethyl methacrylate phantom that is 15 cm wide and
either 16 cm or 32 cm in diameter. Measurements are made
using a 100-mm pencil ionization chamber placed in holes
at the center of the phantom and then again at the periphery
of the phantom. The measured air kerma data, specified in
mGy, are corrected for X-ray beam collimation width and
chamber calibration factor, giving the values of CTDI100
center and CTDI100 peripheral. The numerical results of
these measurements are combined mathematically (onethird center þ two-thirds peripheral) to compute CTDIw.
The CTDIvol is determined from the CTDIw to take into
account the pitch factor in CT studies, where CTDIvol ¼
CTDIw/pitch. When the CTDIvol metric is used, the size of
the phantom (16 or 32 cm) should be specified, or significant errors in dose assessment can result [16,17]. The DLP
is the average CTDIvol multiplied by the length (in cm) of
the CT scan along the long axis of the patient, with units of
mGy-cm. The DLP is a dose metric that is approximately
linearly proportional to the amount of X-ray energy
absorbed (“imparted”) by the patient from the CT scan.
Prospective cardiac gating. Early forms of cardiac
gating (ie, retrospective gating) scan the patient at fulldose levels during the entire cardiac cycle while
recording the electrocardiogram signal. The retrospective gating algorithm then selects the acquired data based
upon a time window during the cardiac cycle and uses
that data to reconstruct the CT image at that time point.
The problem with this method is that reconstructing
cardiac CT images at every time point in the cardiac
cycle is generally not necessary to make the diagnosis of
coronary artery disease; therefore, a large fraction of the
radiation is unnecessary. Prospective cardiac gating uses
the electrocardiogram signal to modulate the X-ray tube
so that X-rays are produced only during the desired
phase of the cardiac cycle. Prospective gating techniques
can reduce radiation dose for cardiac CT by a factor of
10 or more.
Overbeaming. Multidetector array CT systems
(MDCT) typically use z-axis collimation, which places
the edge of the X-ray beam just off of the active detector
array owing to penumbra effects, which can cause artifacts
in helical scanning. Some manufacturers have focal-spot
tracking software that can reduce the amount of overbeaming, which reduces the dose penalty. As the collimated beam width has increased for CT systems with
larger detector arrays (ie 64 slice), the overbeaming dose
penalty becomes almost negligible compared to earlier
MDCT systems with 4 detector arrays.
Overranging corrections. Adaptive collimator systems,
separate from the main X-ray beam collimator, shield
parts of the body at the edges of a CT scan (in the
CT Dose Metrics
Patient Size
The dose metrics CTDIvol and DLP provide information
about the radiation output of a CT scanner for a given
protocol to a given phantom size, but the numerical data
currently provided in the RDSR or in the CT image
DICOM header do not provide information about the
size of the patient. Patient size is an important consideration when an actual radiation dose is to be estimated; eg,
for a fixed set of CT acquisition parameters (kV, mA,
time, pitch, etc.), the dose for a smaller patient will be
larger than the dose for a larger patient [16]. The absorbed
dose is essentially the absorbed X-ray energy divided by
the mass of the patient. Thus, for the same amount of
energy emitted by the X-ray system, smaller patients have
less mass and consequently larger absorbed doses.
1290 Journal of the American College of Radiology/Vol. 11 No. 12PB December 2014
Although the RDSR and DICOM header data do not
provide information with respect to patient size, the CT
images do, since the dimensions of each pixel in each
CT image are accurately known. However, identification of the patient boundaries—separate from the surrounding air in the image—needs to be performed
automatically using segmentation algorithms to automate slice- by-slice patient size estimates. Segmentation
in CT is aided by the grayscale in a CT image because it
is quantitatively meaningful using the Hounsfield Unit
(HU) scale. HU values range from e1,000 to þ3,095,
and most patient anatomy spans the range from e400
to þ2,000. The air in the image has a theoretical value
of e1,000, but for various reasons, it can extend to
around e800 when associated with the patient.
Segmentation methods include the following:
1. A basic segmentation approach is to use a threshold,
such as HU ¼ e800, and count the number of pixels
in the image that exceed this threshold. The pixel
count (N) combined with the known area of the pixel
(A) can be used to compute the area of the patient
(Ap) as: Ap ¼ NA. The equivalent diameter Deq is the
diameter corresponding to the patient area Ap, such
that Deq ¼ 2 sqrt (Ap p).
2. An alternate approach is to compute the water
equivalent diameter Dw, where the segmentation
methods described above are used, coupled with a
density-weighted approach that considers the low
density of the lungs and the higher density of bones
and metal hardware in the patient. Methods are
described in more detail elsewhere [18].
3. A third approach uses the CT localizer when the
patient anatomy is visually cut off in the CT reconstructions, caused by either the scan field of view
being too small (extra-large patient) or the display
reconstruction field of view being zoomed in. In most
cases, the localizer field of view incorporates all of the
patient anatomy.
Dose Estimate Conversion Factors: Size-Specific
Dose Estimates
To the extent that the CTDI phantom diameter differs
from the patient diameter that is scanned, the indicated
CTDIvol (mGy) will be considerably underestimated or
overestimated compared to the actual dose to the patient
(mGy). This issue is recognized in the American Association of Physicists in Medicine report number 204
[16], which discusses methods to convert the scannerreported CTDIvol to a “size-specific dose estimate”
based on conversion tables published therein and on
determination of effective diameter (Fig. 2a).
The size-specific dose estimate (SSDE) methodology
adjusts the indicated CTDIvol by means of conversion
factors based on patient effective diameter to produce a
more accurate estimate of patient dose. This estimate is
particularly important for pediatric patients and when
manufacturers use different calibration phantoms to
determine CTDIvol for pediatric body studies (Fig. 2b).
When using CTDIvol as the basis of a dose estimate,
knowing the size of the CTDI phantom (16 cm or 32
cm in diameter) is imperative. The use of different
strategies by CT vendors causes confusion in this regard.
The phantom with the 16-cm diameter is used for all
pediatric head and all adult head protocols; the phantom
with the 32-cm diameter is used for all adult body CT
protocols. However, a discrepancy exists for the pediatric
body protocol: GE, Hitachi, and Toshiba use a phantom with either a 16-cm or a 32-cm diameter,
depending on the scan field of view; Philips and Siemens
use a phantom with a 32-cm diameter for all body scans,
regardless of patient size.
Fig 2. (a) Size-specific dose estimate methods require conversion of the patient dimensions to an effective diameter. (b)
Conversion factors based on effective diameter are shown for a 25-cm (effective diameter) patient and the corresponding
correction factors for a scanner that uses the 32-cm phantom (1.47), and for a scanner that uses the 16-cm diameter phantom
(0.71) [17]. AP ¼ anterior-posterior; CTDIvol ¼ volume CT dose index; Dia ¼ diameter; Dim ¼ dimension; Lat ¼ lateral.
Morin, Seibert, Boone/Radiation Dose and Safety 1291
The SSDE requires patient diameter (Dw is preferred
over Deq) to provide a more accurate estimate of patient
dose by using CTDIvol as an indicator of CT scanner Xray output. In some implementations, the Dw of the
patient at the center (along the z-axis) of the CT scan is
used; however, a more robust approach is to compute
the Dw for each CT image in the CT study, apply the
SSDE correction on an image-by-image basis (using the
corresponding image-specific CTDIvol value), and then
compute the average SSDE for all images along the zaxis (longitudinal axis) of the patient.
Estimating Effective Dose with DLP
The DLP has a high correlation with the estimated
effective dose based on Monte-Carlo studies that generate
effective dose as a function of DLP for various types of CT
scans, including head, chest, abdomen, and pelvis. These
data have a zero intercept; thus, the slopes of these relationships (not shown) are used as a shortcut procedure
for estimating effective dose in the field in some settings.
The slopes, which have become known as k-factors, are
slightly different for CT scans in different parts of the
body (Table 2). An estimate of effective dose E is a simple
multiplication of the CT-generated DLP dose metric and
the corresponding k-factor for a given region of the body
(x): Ex (mSv) ¼ kx (mSv mGy-1cm-1) DLPx (mGy cm).
CT DOSE REPORTING
A number of states have adopted legislation that requires
that dose metrics from the CT scanner be placed in the
interpretive report, or at a minimum, be available in the
electronic medical record. The Joint Commission has
also developed new standards [20] (not yet implemented) that could require the reporting of CT dose
metrics on a patient-by-patient basis. The most basic
(and earliest available) form of “dose reporting” on CT
systems was the production of bitmap images, which are
a part of the patient’s image data produced during a CT
examination. Because PACS are designed to handle
primarily image data, the CT scanners would produce a
text report, stored as a bitmap image (Figs. 3,4), which
was saved as an additional CT image series in the
DICOM data. These images could be viewed on the
PACS console and the values could be seen by the
interpreting physician.
Open source [21,22] and commercially available
dose-reporting software systems can use the bitmap
dose-summary page with optical character recognition
software to extract the text data on the dose-summary
page. In legacy CT systems, only the data provided on
the bitmap dose-summary page were available for CT
dose reporting. In current CT systems, the DICOM CT
RDSR [8,9] provides numerical data in a separate
DICOM object; when these data are combined with
data in the DICOM header associated with each CT
image, the system provides comprehensive information
with respect to each CT exam series. In addition to the
basic CTDIvol and DLP metrics, the CT RDSR information includes the acquisition protocol name, type of
acquisition, X-ray tube voltage, average tube current,
gantry rotation period, exposure time, pitch factor,
single/total collimation width, X-ray focal spot size,
procedure anatomic target region, and CTDI phantom
diameter used for calibration.
MEASURING RADIATION DOSE IN
INTERVENTIONAL IMAGING PROCEDURES
Interventional imaging procedures, chiefly performed in
radiology, cardiology, electrophysiology, and vascular
surgery, involve the use of continuous fluoroscopic and
fluorographic images with real-time, in-room feedback
for guidance, evaluation, diagnosis, and treatment of the
vascular or organ systems. Because these procedures can
be complex and very time consuming, acute radiationinduced tissue reactions can occur as a result of long
irradiation times on areas of the skin along the path of
the X-ray beam, or from overlapping X-ray beams that
occur with use of various positions and projections of
the X-ray system. Peak skin radiation doses of 10s of Gy
have occurred, causing outcomes ranging from simple
skin erythema (sunburn) to complete breakdown of the
underlying vasculature and tissue necrosis [23].
Historically, radiation dose indicators for fluoroscopically guided interventional (FGI) procedures were based
mainly on the recording of fluoroscopy time and
Table 2. Dose-length product and normalized effective
dose factors
Region of Body
k-factor (mSv mGy-1cm-1)
Head
Neck
Chest
Abdomen
Pelvis
Note: Table is from Reference [19].
0.0023
0.0054
0.017
0.015
0.014
Fig 3. The bitmap image of a dose summary page for a GE
VCT scanner. Two CT series are described along with the
localizer view (“Scout” is the GE moniker). Note phantom
size indication. CTDIvol ¼ volume CT dose index; DLP ¼
dose-length product; VCT ¼ volume CT.
1292 Journal of the American College of Radiology/Vol. 11 No. 12PB December 2014
Fig 4. The bitmap image of a dose
summary page for a Siemens Definition CT scanner. Two CT series are
reported along with two localizer
views (“Topogram” is the Siemens
moniker). Note phantom size indication, L ¼ large (32 cm), S ¼ small (16
cm) diameter CTDI phantom, listed
after the CTDIvol numerical values;
Abd ¼ abdomen; CTDIvol ¼ volume
CT dose index; DLP ¼ dose-length
product; PEL ¼ pelvis; TI ¼ Rotation
Time in seconds; cSL ¼ collimated
slice in millimeters; mAs ¼ average
mAs for the CT acquisition range;
ref. ¼ quality reference mAs for the
CT acquisition range.
secondarily on the number of higher-dose acquisitions
performed using spot film, cine (35-mm) film, or digital
image acquisition, collectively known as fluorography,
which can deliver a 10-100 times greater air kerma rate
than fluoroscopy [24]. Ultimately, these values do not
correlate well with the radiation dose delivered to the
patient because of factors such as operational mode,
technique, movement of the X-ray beam, and patient size.
Highly publicized radiation incidents in which significant
tissue reactions were documented for complex FGI procedures prompted the FDA in 2006 to publish changes in
the Code of Federal Regulations Title 21 to require
manufacturers to include dose metric indicators on
interventional and fluoroscopic equipment [25].
Interventional Imaging Dose-Reduction
Technologies
Modern FGI equipment provides several advances to
reduce the dose and the dose rate, which would otherwise reach very high levels for complex studies that
require long procedure times and large numbers of imaging sequences. The last sequence hold/last frame hold
function provides the user with the ability to play back a
fluoroscopic sequence of images or image without reirradiating the patient. The pulsed fluoroscopy feature
allows the user to adjust the frame rate from real time
(30 frames per second) to values commensurate with the
needed temporal resolution (eg, 15, 7.5, 3.75 frames per
second) for the specific interventional procedure. As the
pulse rate is reduced, the dose rate to the patient is
typically lower and decreases with frame-rate reduction.
The virtual collimation function allows the user to adjust
the collimation for the next sequence of images without
irradiating the patient, by indicating the area to be acquired on the display monitor. The X-ray tube filtration
can be changed by automatically adding extra filtration
from 0.1 to 0.9 mm Cu at the X-ray exit port in order to
reduce the fraction of lower energy radiation in the
bremsstrahlung spectrum, thus lowering the amount of
radiation absorbed in the body relative to that absorbed
in the detector. Together, these technologies can substantially reduce the radiation dose for a procedure,
without any major compromise of image quality or user
ability to complete the study.
Interventional Imaging Dose Metrics
The 2 specific dose metric indicators on FGI equipment
include the air kerma-area product (PKA) and air kerma at
the reference point (Ka,r) [24], which should both be
included in a patient-specific dose report. PKA represents
the integral of air kerma (essentially absorbed dose to air)
across the entire X-ray beam emitted from the X-ray tube
measured by an area-sensitive chamber positioned after
the beam-defining collimators in the X-ray tube assembly. This metric is sensitive to the volume of the body
irradiated and is proportional to the amount of energy
delivered to the patient by the beam, with units of
Gy$cm2. Similar to the DLP metric for CT, a rough
estimate of the effective dose E to the whole body from a
specific FGI procedure, x, can be estimated with a conversion factor kx, as E(mSv) ¼ kx(mSv Gy-1 cm-2) PKA
(Gy cm2) [26]. Using Monte-Carlo algorithms to track
X-ray photon dose deposition to specific organs for a
typical procedure, an effective dose estimate is determined based on the organ dose weighting factors per
International Commission on Radiation Protection
report number 103 methods [12]. Equating the calculated effective dose to the measured PKA value times a
constant (kx) allows the conversion factor to be determined. Tables are generated to provide specific kx values
for several interventional procedures [26].
For FGI systems with an isocenter of rotation, Ka,r is
the estimated air kerma (free-in-air) at a distance 15 cm
from the isocenter in the direction of the focal spot [27].
The intent of this calibration distance is to provide a
reasonable estimate of the dose rate and accumulated
dose to the skin for an acquisition sequence, assuming a
patient girth of ~30 cm. However, some circumstances,
such as attenuation of the X-ray beam by the table,
will reduce the dose estimate; some, such as radiation
Morin, Seibert, Boone/Radiation Dose and Safety 1293
backscatter from the patient, will increase the dose estimate; and some, such as variation in distance from the
focal spot, will either increase or decrease the skin dose
estimate. In addition, movement of the X-ray tube and
detector over many different projection angles spreads
the radiation dose to the patient over a large area. A
reasonable estimate of the dose to any specific region of
the skin, therefore, requires an analysis of the specific
acquisition sequences, location of the X-ray tube distance relative to the skin, projection angles, table
attenuation, and many other factors.
Fortunately, the DICOM Irradiation Event X-ray Data
RDSR [8,9], available on many FGI systems, contains
details of gantry position, kV, mA, time, table height, field
of view, Ka,r, PKA , and many other acquisition parameters
for each radiation event (hundreds of pages of details are
common for a complex exam), resulting in a complete
description of the procedure (an example of partial data
extraction of events recorded in the RDSR is shown in
Fig. 5). Reconstruction of peak skin dose areas is achieved
by binning the individual Ka,r values as a function of gantry
angle, field size, and X-ray beam locations (for more accurate estimates, corrections for distance variation, table
attenuation, and radiation backscatter can be applied).
Regions of high accumulated doses with possible tissue
reactions can be ascertained and identified as shown in a
map of radiation events (Fig. 6). Availability of the
DICOM RDSR is very helpful in estimating peak skin dose
and effective dose with FGI procedures; it should be
mandatory for all new fluoroscopy equipment. At the
minimum, recording of the accumulated Ka,r and PKA can
identify situations that require further evaluation for
possible tissue reactions and subsequent care of the patient.
MEASURING RADIATION DOSE IN DIGITAL
RADIOGRAPHY
Radiography represents the greatest fraction of encounters
for medical imaging, in which single or multiple projection static images are acquired in a study. In terms of radiation dose, these examinations are largely on the low end
of dose per encounter relative to other procedures such as
CT, interventional, or nuclear medicine imaging.
Nevertheless, tracking and recording of radiation doses for
these procedures are important, particularly for pediatric
and in-patient evaluations that require repeated imaging
on a daily or even more frequent basis to evaluate patient
status (eg, line placement, pulmonary perfusion).
Assessment of radiation dose is dependent on the
radiographic technique (kV, mA, exposure time), beam
quality and filtration, tube output (mGy/mAs),
geometrical setup (source-to-patient and source-todetector distance), and patient anatomy exposed. In
most devices using computed radiography detectors, the
technical parameters are not directly available and
require input by the technologist. For flat-panel detector
systems that are directly coupled to X-ray generator
system interfaces, in addition to the acquisition parameters mentioned, these systems can provide air kermaarea product (PKA,Gy-cm2) and reference point air
kerma (Ka,r, mGy), similar to that described for interventional and fluoroscopy systems in the previous section, with similar ways to estimate patient dose.
Fig 5. Example of dose-relevant information generated for fluoroscopy/Interventional Imaging encounters and fields
populated by the parameters contained in the DICOM Radiation Dose Structured Report.
1294 Journal of the American College of Radiology/Vol. 11 No. 12PB December 2014
Fig 6. Reference point air
kerma map of accumulated
dose as a function of the
gantry angle position, color
encoded with scale shown
on right of figure. X-axis is
the primary gantry angle
position from e180 to 180
degrees, and Y-axis is the
secondary gantry angle position from e90 to 90 degrees. This map provides the
locations of possibly high
skin doses and potential
tissue reaction sites.
Another important consideration with radiography,
however, is the amount of incident radiation transmitted
through the anatomy and converted by the detector to
an image of the necessary quality for a diagnosis. Unlike
fixed-speed screen-film detectors that require a specific
radiation intensity to create an image of acceptable optical density (brightness and contrast), digital radiography detectors have a variable speed and a wide
exposure range that produce an image with appropriate
brightness and contrast under a large range of acquisition parameters (kV, mAs, magnification). Although this
might sound like a great attribute, underexposures can
compromise image diagnosis because of insufficient
quantum statistics and an excess of image noise, and
overexposures deliver needless radiation to the patient;
in either situation, patient safety suffers. Examples of
these image acquisition situations are shown in Figure 7.
The de-linking of radiation dose and image appearance
is an issue that has been addressed by manufacturers of
digital detectors with the inclusion of an “exposure index”
value that provides a useful signal to cue the technologist
regarding the radiation dose. Each manufacturer has
developed proprietary exposure indicators to identify
underexposure, appropriate exposure, and overexposure
situations for a given diagnostic task. Although this feature
provides excellent feedback, there are, unfortunately, as
many different exposure indicator algorithms as there are
manufacturers [28]. This situation can be particularly
confounding when systems from multiple manufacturers
are used interchangeably at a single site.
Standardization of Digital Radiography Exposure
Indices
In 2008, the International Electrotechnical Commission
(IEC) published a standard—Exposure Index for Digital
Radiography, IEC 62494-1 [29]—to establish a common
feedback mechanism for all digital radiography manufacturers to adopt for their systems (complementary to
their own proprietary indicators). In addition, the standard was established to ensure placement of the indicators
into a DICOM RDSR specific for radiography, and inclusion of specific tags in the DICOM metadata of each
image. The international exposure index standard specifies 3 distinct parameters: the exposure index (EI); the
target exposure index (EIT); and the deviation index (DI).
EI is a unitless value that provides an estimate of the
amount of incident exposure on a detector for a given
calibration energy and filtration [29]. EIT is a user-defined
target value for the detector that is based on a specific body
part, view, procedure, and diagnostic task (eg, extremity
imaging that requires a lower noise level and a higher
incident exposure to allow for greater sensitivity in
detecting micro-fractures of bones, versus an oftenrepeated study such as scoliosis that does not require as
much exposure to determine the angles of the vertebral
bodies of the spine for evaluation of treatment). DI is a
value that indicates whether the calculated EI matches
EIT, through a logarithmic comparison, calculated as:
EI
DI ¼ 10 log10
:
EIT
When an “appropriate” exposure is made, then EI
equals EIT, and DI ¼ 0. For situations that result in an
overexposure or underexposure, DI will be positive or
negative, respectively. The equation generates values such
that a DI of 1 is one step on a standard generator mAs
control or auto exposure compensation (International
Standardization Organization [ISO] R5 scale), which
gives the technologist insight on how to compensate for
Morin, Seibert, Boone/Radiation Dose and Safety 1295
Fig 7. The left image is underexposed and exhibits a mottled
appearance, indicative of low
SNR. The image on the right exhibits a clean, smooth appearance, indicative of high SNR. The
question
regarding
“overexposure” cannot be directly
determined, unless a quantitative
indicator is available. SNR ¼
signal-to-noise ratio.
the technique factors if a radiographic image retake is
necessary. Given that the standard is relatively new,
implementation rules have yet to be identified, but a DI
target of e2.0 to þ2.0 would seem to be within an
“acceptable” exposure range for a given diagnostic task. A
DI of e2 (EI is 63% of target exposure) or of >þ2 (EI
is 160% of target exposure) would be cause for investigation and a possible retake of the underexposed image.
When DI is >þ3.0 (2-fold overexposure) or <e3.0 (2fold underexposure), consultation with a radiologist or
diagnostician is strongly advised.
Radiography Dose Metrics
Obtaining dose metrics for radiography is an informatics
challenge. For those systems conforming to the IEC
Exposure Index Standard, the first task is to ensure proper
input of EIT values for each detector type, examination, and
diagnostic question [28]. No doubt, this requires input of
medical knowledge and consensus from professional societies such as the ACR in their practice patterns and technical standards efforts. Radiography exposure indices and
deviation indices should be tracked by technologist, X-ray
system, detector/processing unit type (e.g., CR or DR)
anatomical view, longitudinal studies, performance over
time, and X-ray technique factors (if available). Analysis of
data should include mean and SD of EI and DI values to
identify potential situations of increased radiation dose over
time (dose creep) or acquisition factors that are too low with
negative DI values. This information can then be used to
adjust EIT values based on analysis and feedback from radiologists, who can identify studies with optimal signal-tonoise ratio. Procedure protocols and automatic exposure
control devices can be adjusted to achieve techniques for
variations in patient size and required examination
outcomes.
Although the implementation of a universal standard
for radiography dose metrics is an important step forward, neither the EI nor the DI describe patient dose, as
they are calculated values derived from the detector
signal. In addition, the EI is valid for one radiation
quality only. Thus, the EI does not indicate patient
dose; it is not a dose measurement tool; and images with
the same EI obtained on different digital systems might
not have similar image quality, owing to the influence of
detector sensitivity, scattered radiation, and beam quality differences, a subset of factors that affect patient dose.
To more accurately estimate patient dose, radiographic
acquisition parameters must be collected, including kVp,
mAs, beam filtration (half-value layer), and tube output
data per study. Also helpful, if available, are the air kermaarea product values and the reference point air kerma
values for patient dosimetry evaluation. Subsequent
calculation of effective dose can be performed, based on
use of these metrics, as explained earlier.
IHE: THE REM PROFILE
IHE is an initiative promoting the use of standards to
achieve interoperability of health IT systems and effective
use of electronic health records through the publication of
implementation guidelines. Under a well-defined process,
stakeholders, developers and volunteer committees in
clinical and operational domains reach consensus on
standards-based solutions, called IHE profiles, which go
through various levels of testing and real-world deployment. After the profile is sufficiently verified, it is incorporated into the IHE Technical Framework, which
provides a resource for developers and users of health IT
systems to address common interoperability challenges
[7]. Purchasers can specify conformance with appropriate
IHE profiles as a requirement in requests for proposals.
Pertinent to this article is the REM Profile.
The REM Profile [7] specifies communications between systems that generate reports of irradiation events
(the acquisition modalities) and systems that receive,
store, or process those reports. The profile defines how
DICOM Structured Reports for CT and projection Xray dose objects [30] are created, stored, queried,
retrieved, and deidentified, and how they may be processed and displayed. Use of the IHE REM Profile in
specifying requirements for radiation dose software and
database capabilities is a highly recommended step in
1296 Journal of the American College of Radiology/Vol. 11 No. 12PB December 2014
developing local informatics standards and tools for the
ultimate achievement of personalized dose tracking.
PARTICIPATION IN DOSE REGISTRIES
A dose registry provides a means to collect radiation dose
metrics across facilities, within a local, regional, national,
or multinational area. With the informatics tools and
standard methods of data collection/transmission discussed in this article, accurate and objective data on a
variety of parameters, including patient radiation dose
metrics, can be forwarded to the registry database.
Relevant comparative data can be extracted, and
collectively, diagnostic reference level (DRL) values can
be derived from distributions of dosimetric quantities
observed in practice, from a relevant region, country, or
multinational area. These DRLs can serve as benchmarks and indicators of radiation dose applicable to
groups of patients for what is achievable in a good
practice.
In general, the DRL for a specific exam represents the
75th percentile of the dose metric data collected from a
number of facilities. DRL values are not applicable to
individual patients, do not represent dose constraints or
dose limits, and are not regulatory; however, a facility
can use DRL benchmarks to compare with their own
mean dose (dose metric) levels. For instance, if a mean
dose level is greater than a corresponding DRL, then a
reasonable course of action would be to investigate the
equipment, protocols, and operators, with a strategy of
identifying high dose outliers, determining causes, and
implementing changes to optimize the procedure with
an “appropriate” dose. If the mean dose is lower than the
DRL, the procedure is not necessarily optimized, but it
is acceptable in terms of the dose metrics. If the mean
dose is significantly lower than the DRL, then the image
quality should be checked for diagnostic acceptability.
Indeed, peer review includes a directive to not only
lower radiation dose but also demonstrate requisite image quality at an appropriate dose.
The United Kingdom has had a long-term ongoing
DRL effort in place since the 1980s, and the use of
DRLs has been mandatory in the European Union since
1997 [31]. In the United States, the FDA-sponsored
Nationwide Evaluation of X-ray Trends (NEXT) has
been ongoing since 1973, providing comprehensive data
on radiation exposure, image quality, and quality
assurance practices for selected imaging exams [32], but
with decreasing frequency. The National Council on
Radiation Protection and Measurements (NCRP) has
released Report No. 172 [33], defining DRLs for
medical and dental imaging in the United States.
However, the report is based largely on the NEXT data,
all pre-2005, and therefore does not necessarily represent
current practice or medical imaging technology advances. A need for DRLs certainly exists, and a mechanism to meet that need is the establishment of a dose
registry.
ACR DOSE INDEX REGISTRY
The ACR has developed the infrastructure for several
clinically useful registries and databases collectively
known as the National Radiology Data Registry
(NRDR), one of which is the Dose Index Registry
(DIR) [34]. With automated streaming of relevant CT
dose metric data to the DIR from many sites across the
nation, evaluations of trends and comparisons of practices to national reference levels can be achieved on a
continually updated basis. To date, the ACR DIR
contains more than 20 million scans—from more than
11 million examseconducted in more than 1,000 facilities. Exam names are standardized to RadLex terminology. The registry automatically (with no human
intervention) captures CTDIvol, DLP, and SSDE dose
metrics. With the large pool of data, results can be
segmented by national, regional, and local areas. DRLs
for these metrics can be determined in straightforward
and objective ways. Participants can compare their data
with the nationwide collective benchmarks and identify
areas of improvement and dose optimization. Efforts to
expand the DIR for computed and digital radiography
are currently under way.
CONCLUSIONS
Medical imaging dose tracking and reporting are rapidly
evolving components of quality patient care. Adoption
and use of informatics tools are necessary first steps toward achieving the flexibility, accuracy, and efficiency of
these nontrivial tasks for estimating patient dose for
ionizing radiation imaging exams. Each modality
described, including CT, interventional imaging/fluoroscopy, and radiography, have their own unique dose
metrics and challenges when it comes to estimating
patient dose and associated risks. To compare or accumulate doses from various modalities, finding a “common denominator,” such as specific organ dose
estimation or calculation of individual effective doses is
proposed, but this approach is also fraught with issues/
errors and subject to criticism for inapplicability to
specific patient dose/risk estimates. Nevertheless,
personalized profiles of patient dose will soon be
required, and understanding the informatics challenges
of implementation is important. Certainly, the IHE
REM Profile provides a framework and logical step
forward in meeting these challenges. Finally, a dose index registry is a useful tool that can be used to optimize
radiation use in medical imaging by collecting and
evaluating dose metrics.
TAKE-HOME POINTS
Awareness and understanding of radiation dose and
radiation dose metrics are responsibilities of all
stakeholders involved in medical imaging.
Radiation dose metrics are not the same as patient
dose and therefore can be misleading; however, they
are helpful in estimating patient dose with appropriate
Morin, Seibert, Boone/Radiation Dose and Safety 1297
corrections and for comparing dose levels in X-ray
modalities among systems and institutions.
Informatics standards (DICOM RDSR) and tools
(IHE REM Profile) provide descriptions of how to
extract radiation dose metric values from the X-ray
generating systems in an institution; additional software is crucial to create databases for analyzing individual and collective patient dose records.
The ACR Dose Index Registry enables individual sites
to compare their radiation dose metrics with others to
ensure consistent protocols for delivering patient care
while maintaining safe practices.
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