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.

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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

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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

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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

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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.

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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.

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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.

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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

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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.

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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

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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
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

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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

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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.
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