BACKGROUND:Computed tomography (CT) is an essential imaging modality for disease diagnosis, treatment efficacy, and image-based guidance of various medical procedures. The locally deposited radiation dose in tissues, as estimated by the computed tomography dose index (CTDI), can vary considerably across exposures delivered by CT scanners from different vendors, even if the scans are performed using similar technique factors, such as tube potential and tube current. The volumetric CTDI (CTDIvol) is a common dose metric that reports an average radiation dose (in mGy) delivered to a specific volume within a test phantom. The CTDIvol is important in dosimetry applications as the organ absorbed dose within the patient has been shown to scale in near-linear proportion, creating a basis for comparing organ doses across different scan protocols and scanner models. PURPOSE:To develop a database of tube current-time product (mAs) normalized CTDIvol values for currently utilized CT scanner models for each of the four primary CT vendors for use in the MIRDct organ dosimetry software available at MIRDsoft.org. This data forms the basis of the MIRDct code, which reports organ doses across a range of computational phantoms based upon axial organ dose coefficient libraries generated through Monte Carlo radiation transport for a reference CT scanner. Organ doses delivered by alternate CT scanner vendors and models may then be reported using ratios of normalized CTDIvol values under similar technique factors. METHODS:Scanners were selected from four major CT manufacturers: Philips Healthcare, GE Healthcare, Canon Medical Systems, and Siemens Healthineers. Technique parameters were also selected for each scanner that closely matched values used in the generation of an equivalent CT source term (small to large bowtie filters; 80-140-kVp tube voltage; and 10-mm to 40-mm beam collimation). For each scanner chosen, the appropriate technique factors and protocols were selected, and the console-reported CTDIvol values were recorded and normalized to a set value of 100 mAs. The normalized CTDIvol data collected for use within the MIRDct code were analyzed for noticeable patterns, features, and trends, and were compared to similar normalized CTDIvol datasets used within the National Cancer Institute NCICT software and the Virtual Phantoms, Inc. VirtualDose software. RESULTS:For all given CT scanner and technique factor combinations, there was strong agreement in normalized CTDIvol values across all three codes: between 0% and 12% difference for the compared scanners. Ratios of CTDIvol values for various CT scanner vendors and models to the corresponding CTDIvol values for the MIRDct reference scanner (Cannon Aquilion One Genesis) were also compared on the basis of either the 16-cm head PMMA phantom or the 32-cm body PMMA phantom. The mean quotient of these normalized CTDIvol ratios (head ratios to body ratios) was found to be approximately 1.06, and thus either ratio may be applied in reporting patient organ dose by MIRDct. CONCLUSIONS:A database of normalized CTDIvol (mGy/100 mAs) was created for 17 models of CT scanners from four manufacturers at varying tube potentials, collimations, x-ray bowtie filters, and phantom sizes for use in the MIRDct software.

Introduction: The objective of this investigation is to examine a wide array of commonly accessible 3D print filaments and assess their radiographic fidelity in vertebral models.Material and methods: Solid cylinders were 3D printed on an Ultimaker S5 (Ultimaker B.V., Utrecht, Netherlands) in 12 commonly accessible filaments: ABS (Acrylonitrile butadiene styrene), PLA (Polylactic acid), Tough PLA, PP (Poly propylene), Carbon Fiber ABS, Wood fill, PETG (Polyethylene terephthalate glycol), Nylon, PC (Polycarbonate), Bronze fill, TPU 95A (Thermoplastic polyurethane), and CPE (Chlorinated polyethylene). Cylinders were imaged in a CT phantom with anatomic standards. Next, 11 identical L4 human vertebral models were 3D printed in the same materials (omission of TPU 95A). AP and lateral fluoroscopic images were taken of each of the vertebrae and sent to board-certified/board-eligible neurosurgeons, neuroradiologists, and orthopedic spine surgeons for evaluation.Results: CT imaging of the materials yielded a range of Hounsfield values from –120.6 HU (PP) to 167.76 HU (PETG). The polled experts rated CF ABS as the highest fidelity model (mean 4.069) and Bronze fill as lowest (mean 2.000). All simulated vertebrae in this study ranked higher than Bronze fill (p<0.05). Notably, CF ABS (p=0.0029), ABS (p=0.0075), and CPE (p=0.0182) ranked significantly higher than Tough PLA.Discussion: It was determined that CT values of examined filaments were not comparable to cortical bone standard but similar to other bone standards. Our results suggest that apart from Bronze fill, educators can create high fidelity fluoroscopic models with print materials such as ABS, CF ABS, and CPE.

Purpose: To investigate the potential for decreasing radiation dose when utilizing a third-generation vs second-generation dual-source dual-energy CT (dsDECT) scanner, while maintaining diagnostic image quality and acceptable image noise. Materials and Methods: Retrospective analysis of patients who underwent dsDECT for clinical suspicion of urolithiasis from October 2, 2017, to September 5, 2018. Patient demographics, body mass index, abdominal diameter, scanning parameters, and CT dose index volume (CTDIvol) were recorded. Image quality was assessed by measuring the attenuation and standard deviation (SD) regions of interest in the aorta and in the bladder. Image noise was determined by averaging the SD at both levels. Patients were excluded if they had not undergone both third- and second-generation dual-energy CT (DECT), time between DECT was more than 2 years, or scan parameters were outside the standard protocol. Results: A total of 117 patients met the inclusion criteria. Examinations performed on a third-generation DECT had an average CTDIvol 12.3 mGy, while examinations performed on a second-generation DECT had an average CTDIvol 13.3 mGy (p < 0.001). Average image noise was significantly lower for the third-generation DECT (SD = 10.3) compared with the second-generation DECT (SD = 13.9) (p < 0.001). Conclusions: The third-generation dsDECT scanners can simultaneously decrease patient radiation dose and decrease image noise compared with second-generation DECT. These reductions in radiation exposure can be particularly important in patients with urinary stone disease who often require repeated imaging to evaluate for stone development and recurrence as well as treatment assessment.

There are currently four different organizations recognized by the Centers for Medicare and Medicaid Services (CMS) as accreditors for CT imaging, including the American College of Radiology (ACR), The Joint Commission (TJC), the Intersocietal Accreditation Commission (IAC, formerly the Intersocietal Commission for the Accreditation of Vascular Laboratories (ICAVL)), and RadSite ( https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Accreditation-of-Advanced-Diagnostic-Imaging-Suppliers.html . Accessed 27 Oct 2016). In addition to general CT imaging accreditation, the ACR has a specific cardiac module, and the IAC has accreditation programs for coronary calcium scoring CT and coronary CTA exams. TJC and RadSite have no specific cardiac requirements for CT scanners, although RadSite requires submission of a sample cardiac case if cardiac CT is part of the clinical practice. The commonality of all four accreditors is that the CT equipment specifications and performance meet all state and federal requirements. Beyond this requirement, the criterion for equipment accreditation and the technologist credentials differs among the accreditors.

Objectives: Both projection and dual-energy (DE)-based methods have been used for metal artifact reduction (MAR) in CT. The two methods can also be combined. The purpose of this work was to evaluate these three MAR methods using phantom experiments for five types of metal implants. Materials and Methods: Five phantoms representing spine, dental, hip, shoulder, and knee were constructed with metal implants. These phantoms were scanned using both single-energy (SE) and DE protocols with matched radiation output. The SE data were processed using a projection-based MAR (iMAR, Siemens) algorithm, while the DE data were processed to generate virtual monochromatic images at high keV (Mono+, Siemens). In addition, the DE images after iMAR were used to generate Mono+ images (DE iMAR Mono+). Artifacts were quantitatively evaluated using CT numbers at different regions of interest. Iodine contrast-to-noise ratio (CNR) was evaluated in the spine phantom. Three musculoskeletal radiologists and two neuro-radiologists independently ranked the artifact reduction. Results: The DE Mono+ at high keV resulted in reduced artifacts but also lower iodine CNR. The iMAR method alone caused missing tissue artifacts in dental phantom. DE iMAR Mono+ caused wrong CT numbers in close proximity to the metal prostheses in knee and hip phantoms. All musculoskeletal radiologists ranked SE iMAR > DE iMAR Mono+ > DE Mono+ for knee and hip, while DE iMAR Mono+ > SE iMAR > DE Mono+ for shoulder. Both neuro-radiologists ranked DE iMAR Mono+ > DE Mono+ > SE iMAR for spine and DE Mono+ > DE iMAR Mono+ > SE iMAR for dental. Conclusions: The SE iMAR was the best choice for the hip and knee prostheses, while DE Mono+ at high keV was best for dental implants and DE iMAR Mono+ was best for spine and shoulder prostheses. Artifacts were also introduced by MAR algorithms.

PurposeModel observers have been successfully developed and used to assess the quality of static 2D CT images. However, radiologists typically read images by paging through multiple 2D slices (i.e., multislice reading). The purpose of this study was to correlate human and model observer performance in a low‐contrast detection task performed using both 2D and multislice reading, and to determine if the 2D model observer still correlate well with human observer performance in multislice reading.MethodsA phantom containing 18 low‐contrast spheres (6 sizes × 3 contrast levels) was scanned on a 192‐slice CT scanner at five dose levels (CTDIvol = 27, 13.5, 6.8, 3.4, and 1.7 mGy), each repeated 100 times. Images were reconstructed using both filtered‐backprojection (FBP) and an iterative reconstruction (IR) method (ADMIRE, Siemens). A 3D volume of interest (VOI) around each sphere was extracted and placed side‐by‐side with a signal‐absent VOI to create a 2‐alternative forced choice (2AFC) trial. Sixteen 2AFC studies were generated, each with 100 trials, to evaluate the impact of radiation dose, lesion size and contrast, and reconstruction methods on object detection. In total, 1600 trials were presented to both model and human observers. Three medical physicists acted as human observers and were allowed to page through the 3D volumes to make a decision for each 2AFC trial. The human observer performance was compared with the performance of a multislice channelized Hotelling observer (CHO_MS), which integrates multislice image data, and with the performance of previously validated CHO, which operates on static 2D images (CHO_2D). For comparison, the same 16 2AFC studies were also performed in a 2D viewing mode by the human observers and compared with the multislice viewing performance and the two CHO models.ResultsHuman observer performance was well correlated with the CHO_2D performance in the 2D viewing mode [Pearson product‐moment correlation coefficient R = 0.972, 95% confidence interval (CI): 0.919 to 0.990] and with the CHO_MS performance in the multislice viewing mode (R = 0.952, 95% CI: 0.865 to 0.984). The CHO_2D performance, calculated from the 2D viewing mode, also had a strong correlation with human observer performance in the multislice viewing mode (R = 0.957, 95% CI: 879 to 0.985). Human observer performance varied between the multislice and 2D modes. One reader performed better in the multislice mode (P = 0.013); whereas the other two readers showed no significant difference between the two viewing modes (P = 0.057 and P = 0.38).ConclusionsA 2D CHO model is highly correlated with human observer performance in detecting spherical low contrast objects in multislice viewing of CT images. This finding provides some evidence for the use of a simpler, 2D CHO to assess image quality in clinically relevant CT tasks where multislice viewing is used.

PURPOSE:This study aimed to investigate the influence of display window setting on technologist performance detecting subtle but clinically relevant artifacts in daily computed tomography (CT) quality control (dQC) images.METHODS:Fifty three sets of dQC images were retrospectively selected, including 30 sets without artifacts, and 23 with subtle but clinically relevant artifacts. They were randomized and shown to six CT technologists (two new and four experienced). Each technologist reviewed all images in each of two sessions, one with a display window width (WW) of 100 HU, which is currently recommended by the American College of Radiology, and the other with a narrow WW of 40 HU, both at a window level of 0 HU. For each case, technologists rated the presence of image artifacts based on a five point scale. The area under the receiver operating characteristic curve (AUC) was used to evaluate the artifact detection performance.RESULTS:At a WW of 100 HU, the AUC (95% confidence interval) was 0.658 (0.576, 0.740), 0.532 (0.429, 0.635), and 0.616 (0.543, 0.619) for the experienced, new, and all technologists, respectively. At a WW of 40 HU, the AUC was 0.768 (0.687, 0.850), 0.546 (0.433, 0.658), and 0.694 (0.619, 0.769), respectively. The performance significantly improved at WW of 40 HU for experienced technologists (p = 0.009) and for all technologists (p = 0.040).CONCLUSIONS:Use of a narrow display WW significantly improved technologists' performance in dQC for detecting subtle but clinically relevant artifacts as compared to that using a 100 HU display WW.

The use of Fourier domain model observer is challenged by iterative reconstruction (IR), because IR algorithms are nonlinear and IR images have noise texture different from that of FBP. A modified Fourier domain model observer, which incorporates nonlinear noise and resolution properties, has been proposed for IR and needs to be validated with human detection performance. On the other hand, the spatial domain model observer is theoretically applicable to IR, but more computationally intensive than the Fourier domain method. The purpose of this study is to compare the modified Fourier domain model observer to the spatial domain model observer with both FBP and IR images, using human detection performance as the gold standard. A phantom with inserts of various low contrast levels and sizes was repeatedly scanned 100 times on a third-generation, dual-source CT scanner at 5 dose levels and reconstructed using FBP and IR algorithms. The human detection performance of the inserts was measured via a 2-alternative-forced-choice (2AFC) test. In addition, two model observer performances were calculated, including a Fourier domain non-prewhitening model observer and a spatial domain channelized Hotelling observer. The performance of these two mode observers was compared in terms of how well they correlated with human observer performance. Our results demonstrated that the spatial domain model observer correlated well with human observers across various dose levels, object contrast levels, and object sizes. The Fourier domain observer correlated well with human observers using FBP images, but overestimated the detection performance using IR images.

OBJECTIVE:To compare computed tomography dose and noise arising from use of an automatic exposure control (AEC) system designed to maintain constant image noise as patient size varies with clinically accepted technique charts and AEC systems designed to vary image noise.MATERIALS AND METHODS:A model was developed to describe tube current modulation as a function of patient thickness. Relative dose and noise values were calculated as patient width varied for AEC settings designed to yield constant or variable noise levels and were compared to empirically derived values used by our clinical practice. Phantom experiments were performed in which tube current was measured as a function of thickness using a constant-noise-based AEC system and the results were compared with clinical technique charts.RESULTS:For 12-, 20-, 28-, 44-, and 50-cm patient widths, the requirement of constant noise across patient size yielded relative doses of 5%, 14%, 38%, 260%, and 549% and relative noises of 435%, 267%, 163%, 61%, and 42%, respectively, as compared with our clinically used technique chart settings at each respective width. Experimental measurements showed that a constant noise-based AEC system yielded 175% relative noise for a 30-cm phantom and 206% relative dose for a 40-cm phantom compared with our clinical technique chart.CONCLUSIONS:Automatic exposure control systems that prescribe constant noise as patient size varies can yield excessive noise in small patients and excessive dose in obese patients compared with clinically accepted technique charts. Use of noise-level technique charts and tube current limits can mitigate these effects.

Thoracic computed tomography (CT) is considered the gold standard for detection lung pathology, yet its efficacy as a screening tool in regards to cost and radiation dose continues to evolve. Chest radiography (CXR) remains a useful and ubiquitous tool for detection and characterization of pulmonary pathology, but reduced sensitivity and specificity compared to CT. This prospective, blinded study compares the sensitivity of digital tomosynthesis (DTS), to that of CT and CXR for the identification and characterization of lung nodules. Ninety-five outpatients received a posteroanterior (PA) and lateral CXR, DTS, and chest CT at one care episode. The CXR and DTS studies were independently interpreted by three thoracic radiologists. The CT studies were used as the gold standard and read by a fourth thoracic radiologist. Nodules were characterized by presence, location, size, and composition. The agreement between observers and the effective radiation dose for each modality was objectively calculated. One hundred forty-five nodules of greatest diameter larger than 4 mm and 215 nodules less than 4 mm were identified by CT. DTS identified significantly more >4 mm nodules than CXR (DTS 32 % vs. CXR 17 %). CXR and DTS showed no significant difference in the ability to identify the smaller nodules or central nodules within 3 cm of the hilum. DTS outperformed CXR in identifying pleural nodules and those nodules located greater than 3 cm from the hilum. Average radiation dose for CXR, DTS, and CT were 0.10, 0.21, and 6.8 mSv, respectively. Thoracic digital tomosynthesis requires significantly less radiation dose than CT and nearly doubles the sensitivity of that of CXR for the identification of lung nodules greater than 4 mm. However, sensitivity and specificity for detection and characterization of lung nodules remains substantially less than CT. The apparent benefits over CXR, low cost, rapid acquisition, and minimal radiation dose of thoracic DTS suggest that it may be a useful procedure. Work-up of a newly diagnosed nodule will likely require CT, given its superior cross-sectional characterization. Further investigation of DTS as a diagnostic, screening, and surveillance tool is warranted.

OBJECTIVE:To compare contrast-to-noise ratio (CNR) thresholds with visual assessment of low-contrast resolution (LCR) in filtered back projection (FBP) and iteratively reconstructed (IR) computed tomographic (CT) images.METHODS:American College of Radiology (ACR) CT accreditation phantom LCR images were acquired at CTDIvol levels of 8, 12, and 16 mGy using 2 scanner models and reconstructed using one FBP and 2 IR kernels. Acquisitions were repeated 100 times. Three board-certified medical physicists blindly reviewed the LCR section images. Pass-percentage rates (PPRs) using previous and current ACR CT accreditation criteria were compared.RESULTS:Observer PPRs for FBP images were less than 32%. For IR images, 5 of 18 settings/dose/model configurations had PPRs greater than 32% (maximum 76.3%). For CNR evaluation of FBP images, PPRs for 15 configurations were greater than 70%. For IR images, all PPRs were at least 96%.CONCLUSIONS:The CNR threshold used by the ACR CT accreditation program yields higher PPRs than visual assessment of LCR, potentially resulting in lower-quality images passing the ACR CNR criteria.

To compare computed tomography dose and noise arising from use of an automatic exposure control (AEC) system designed to maintain constant image noise as patient size varies with clinically accepted technique charts and AEC systems designed to vary image noise.A model was developed to describe tube current modulation as a function of patient thickness. Relative dose and noise values were calculated as patient width varied for AEC settings designed to yield constant or variable noise levels and were compared to empirically derived values used by our clinical practice. Phantom experiments were performed in which tube current was measured as a function of thickness using a constant-noise-based AEC system and the results were compared with clinical technique charts.For 12-, 20-, 28-, 44-, and 50-cm patient widths, the requirement of constant noise across patient size yielded relative doses of 5%, 14%, 38%, 260%, and 549% and relative noises of 435%, 267%, 163%, 61%, and 42%, respectively, as compared with our clinically used technique chart settings at each respective width. Experimental measurements showed that a constant noise-based AEC system yielded 175% relative noise for a 30-cm phantom and 206% relative dose for a 40-cm phantom compared with our clinical technique chart.Automatic exposure control systems that prescribe constant noise as patient size varies can yield excessive noise in small patients and excessive dose in obese patients compared with clinically accepted technique charts. Use of noise-level technique charts and tube current limits can mitigate these effects.

PURPOSE:To determine the dose reduction that could be achieved without degrading low-contrast spatial resolution (LCR) performance for two commercial iterative reconstruction (IR) techniques, each evaluated at two strengths with many repeated scans.MATERIALS AND METHODS:Two scanner models were used to image the American College of Radiology (ACR) CT accreditation phantom LCR section at volume CT dose indexes of 8, 12, and 16 mGy. Images were reconstructed by using filtered back projection (FBP) and two manufacturers' IR techniques, each at two strengths (moderate and strong). Data acquisition and reconstruction were repeated 100 times for each, yielding 1800 images. Three diagnostic medical physicists reviewed the LCR images in a blinded fashion and graded the visibility of four 6-mm rods with a six-point scale. Noninferiority and inferiority-superiority analyses were used to interpret the differences in LCR relative to FBP images acquired at 16 mGy.RESULTS:LCR decreased with decreasing dose for all reconstructions. Relative to FBP and full dose, 25%-50% dose reductions resulted in inferior LCR for vendors 1 and 2 for FBP and 25% dose reductions resulted in inferior and equivalent performance for vendor 1 and equivalent and superior performance for vendor 2 at moderate and strong IR settings, respectively. When dose was reduced by 50%, both IR techniques resulted in inferior LCR at both strength settings.CONCLUSION:For radiation dose reductions of 25% or more, the ability to resolve the four 6-mm rods in the ACR CT accreditation phantom can be lost.

Through this investigation we developed a methodology to evaluate and standardize CT image quality from routine abdomen protocols across different manufacturers and models. The influence of manufacturer-specific automated exposure control systems on image quality was directly assessed to standardize performance across a range of patient sizes. We evaluated 16 CT scanners across our health system, including Siemens, GE, and Toshiba models. Using each practice’s routine abdomen protocol, we measured spatial resolution, image noise, and scanner radiation output (CTDIvol). Axial and in-plane spatial resolutions were assessed through slice sensitivity profile (SSP) and modulation transfer function (MTF) measurements, respectively. Image noise and CTDIvol values were obtained for three different phantom sizes. SSP measurements demonstrated a bimodal distribution in slice widths: an average of 6.2  ±  0.2 mm using GE’s ‘Plus’ mode reconstruction setting and 5.0  ±  0.1 mm for all other scanners. MTF curves were similar for all scanners. Average spatial frequencies at 50%, 10%, and 2% MTF values were 3.24  ±  0.37, 6.20  ±  0.34, and 7.84  ±  0.70 lp cm−1, respectively. For all phantom sizes, image noise and CTDIvol varied considerably: 6.5–13.3 HU (noise) and 4.8–13.3 mGy (CTDIvol) for the smallest phantom; 9.1–18.4 HU and 9.3–28.8 mGy for the medium phantom; and 7.8–23.4 HU and 16.0–48.1 mGy for the largest phantom. Using these measurements and benchmark SSP, MTF, and image noise targets, CT image quality can be standardized across a range of patient sizes.

To reduce the radiation dose associated with CT scans, much attention is focused on CT protocol review and improvement. In fact, annual protocol reviews will soon be required for ACR CT accreditation. A major challenge in the protocol review process is determining whether a current protocol is optimal and deciding what steps to take to improve it. In this paper, the authors describe methods for pinpointing deficiencies in CT protocols and provide a systematic approach for optimizing them. Emphasis is placed on a team approach, with a team consisting of at least one radiologist, one physicist, and one technologist. This core team completes a critical review of all aspects of a CT protocol and carefully evaluates proposed improvements. Changes to protocols are implemented only with consensus of the core team, with consideration of all aspects of the CT examination, including image quality, radiation dose, patient care and safety, and workflow.

The increase in radiation exposure due to CT scans has been of growing concern in recent years. CT scanners differ in their capabilities, and various indications require unique protocols, but there remains room for standardization and optimization. In this paper, the authors summarize approaches to reduce dose, as discussed in lectures constituting the first session of the 2013 UCSF Virtual Symposium on Radiation Safety and Computed Tomography. The experience of scanning at low dose in different body regions, for both diagnostic and interventional CT procedures, is addressed. An essential primary step is justifying the medical need for each scan. General guiding principles for reducing dose include tailoring a scan to a patient, minimizing scan length, use of tube current modulation and minimizing tube current, minimizing tube potential, iterative reconstruction, and periodic review of CT studies. Organized efforts for standardization have been spearheaded by professional societies such as the American Association of Physicists in Medicine. Finally, all team members should demonstrate an awareness of the importance of minimizing dose.

Awareness of and communication about issues related to radiation dose are beneficial for patients, clinicians, and radiology departments. Initiating and facilitating discussions of the net benefit of CT by enlisting comparisons to more familiar activities, or by conveying that the anticipated radiation dose to an exam is similar to or much less than annual background levels help resolve the concerns of many patients and providers. While radiation risk estimates at the low doses associated with CT contain considerable uncertainty, we choose to err on the side of safety by assuming a small risk exists, even though the risk at these dose levels may be zero. Thus, radiologists should individualize CT scans according to patient size and diagnostic task to ensure that maximum benefit and minimum risk is achieved. However, because the magnitude of net benefit is driven by the potential benefit of a positive exam, radiation dose should not be reduced if doing so may compromise making an accurate diagnosis. The benefits and risks of CT are also highly individualized, and require consideration of many factors by patients, clinicians, and radiologists. Radiologists can assist clinicians and patients with understanding many of these factors, including test performance, potential patient benefit, and estimates of potential risk.

PURPOSE:Efficient optimization of CT protocols demands a quantitative approach to predicting human observer performance on specific tasks at various scan and reconstruction settings. The goal of this work was to investigate how well a channelized Hotelling observer (CHO) can predict human observer performance on 2-alternative forced choice (2AFC) lesion-detection tasks at various dose levels and two different reconstruction algorithms: a filtered-backprojection (FBP) and an iterative reconstruction (IR) method.METHODS:A 35 × 26 cm(2) torso-shaped phantom filled with water was used to simulate an average-sized patient. Three rods with different diameters (small: 3 mm; medium: 5 mm; large: 9 mm) were placed in the center region of the phantom to simulate small, medium, and large lesions. The contrast relative to background was -15 HU at 120 kV. The phantom was scanned 100 times using automatic exposure control each at 60, 120, 240, 360, and 480 quality reference mAs on a 128-slice scanner. After removing the three rods, the water phantom was again scanned 100 times to provide signal-absent background images at the exact same locations. By extracting regions of interest around the three rods and on the signal-absent images, the authors generated 21 2AFC studies. Each 2AFC study had 100 trials, with each trial consisting of a signal-present image and a signal-absent image side-by-side in randomized order. In total, 2100 trials were presented to both the model and human observers. Four medical physicists acted as human observers. For the model observer, the authors used a CHO with Gabor channels, which involves six channel passbands, five orientations, and two phases, leading to a total of 60 channels. The performance predicted by the CHO was compared with that obtained by four medical physicists at each 2AFC study.RESULTS:The human and model observers were highly correlated at each dose level for each lesion size for both FBP and IR. The Pearson's product-moment correlation coefficients were 0.986 [95% confidence interval (CI): 0.958-0.996] for FBP and 0.985 (95% CI: 0.863-0.998) for IR. Bland-Altman plots showed excellent agreement for all dose levels and lesions sizes with a mean absolute difference of 1.0% ± 1.1% for FBP and 2.1% ± 3.3% for IR.CONCLUSIONS:Human observer performance on a 2AFC lesion detection task in CT with a uniform background can be accurately predicted by a CHO model observer at different radiation dose levels and for both FBP and IR methods.

Arguing against the Proposition is David W. Jordan, Ph.D. Dr. Jordan is Clinical Assistant Professor in the Department of Radiology, University Hospitals Case Medical Center, Cleveland, OH. He obtained his Ph.D. in Nuclear Engineering and Radiological Sciences from the University of Michigan in 2005, and is certified by the American Board of Radiology in Diagnostic Radiological Physics and Medical Nuclear Physics, by the American Board of Medical Physics in MRI Physics, and by the American Board of Science in Nuclear Medicine in NM Physics & Instrumentation. He has served on many AAPM committees and is currently Chairman of the Insurance Subcommittee. Exposure or dose metric tracking for x-ray imaging is a disservice to our patients. The information gathered is incomplete and meaningful interpretation is problematic, both in the imaging and broader medical community. Imaging decisions must be based on each individual patient's clinical needs, regardless of the amount of previously delivered medical exposure. In regard to data available in DICOM image headers and structured reports, there is great potential for obtaining information that could benefit our patients and our practices. One example of beneficial use of such data collection is the ACR CT Dose Index Registry.1 With over 800 facilities contributing over 10 × 106 scans, this registry has immediate value in terms of quality improvement initiatives, allowing sites to benchmark their dose levels against regional and national data. One component of exposure tracking in x-ray imaging that is problematic, however, is tracking the exposure or dose metrics for specific patients for use in making clinical decisions about future medical exposures. Such tracking raises concerns regarding patient perception of radiation risk, and it does not provide meaningful data to health care providers. Patients typically have little understanding of radiation units or effects, except generally that radiation is bad and more radiation is worse. Patients may compare their exposure, dose, or dose metric values to data available on the Internet while not understanding the important differences between radiological units (e.g., mrad vs mGy) or quantities (e.g., absorbed dose, effective dose, or CT dose index). Further, there is a wealth of misinformation on radiation risk on the Internet; I have counseled numerous patients who have read alarmist articles and were considering foregoing necessary exams. Others are emotionally distressed about dying from cancer. Not only is this not beneficial to our patients, it is harmful. The effects of radiation at diagnostic levels on humans are not currently understood well enough to allow health care professionals to draw meaningful conclusions regarding radiation risks from diagnostic exams. Within the imaging community there is a renewed realization that we cannot speak with confidence regarding risk at diagnostic dose levels, even when relatively higher cumulative levels are delivered in small quantities over time.2–4 Additionally, the quantities available for tracking (e.g., CTDI-vol and DLP in CT) are not measures of individual patient dose.5 In particular, even though effective dose can be estimated from such quantities, effective dose is not defined for individuals.6 This lack of patient-specific dose information, coupled with the uncertainties associated with risk estimates at diagnostic dose levels, makes any record of previous doses clinically meaningless. Knowledge of prior exams and imaging findings are extremely relevant for making current imaging decisions, but an estimate of the cumulative exposures associated with those exams is not. If a radiological procedure is medically justified, then it should be performed—regardless of prior exposure amounts.7 What physician would withhold needed imaging of a trauma victim simply because the patient is a cancer survivor and has a high cumulative dose value? It is a very slippery slope to start applying dose thresholds to patient care, and in fact is counter to a basic premise of radiological protection in medicine.8 In conclusion, dose metric tracking for individual patients is a bad idea. It gives the illusion of providing meaningful data that can augment individual healthcare decisions. In fact, these data may lead to the withholding of needed imaging. Individual patient dose tracking simply attributes more significance to diagnostic dose levels than is scientifically justified. Medical physicists should understand that the use of radiation exposure tracking data is not appropriate for making prospective decisions about patient imaging.9 It is easy to appreciate that many physicists are uncomfortable with the marketing of commercially available systems that promote such uses. Even more disconcerting are suggestions by radiation dose tracking vendors that imaging providers should give patients “score cards” to track their own personal exposure history, adding to the difficulty of confronting the sunk cost bias10 in rational discussions of patient risk from imaging doses. Nevertheless, radiation exposure tracking in imaging has useful applications, and medical physicists currently have a fleeting opportunity to play a central and essential role in the inevitable deployment of the technology. Patient radiation exposure tracking tools can be very powerful in the hands of a physicist or a quality control/improvement specialist or committee. Most imaging equipment produces and stores information about radiation exposure, but not always in a format that can be readily analyzed without manual data entry or formatting. Exposure tracking tools collect such data automatically for every study and provide various reporting and analysis tools that can be used to easily identify protocols, scanners, operators, and referring physicians or departments that deviate from institutional norms. While this function could be performed without exposure tracking software, much more time and effort would be required. Such reviews are a logical extension of protocol review and optimization committee efforts to evaluate whether protocol updates have achieved the desired dose reductions and been implemented correctly across departments and facilities. For researchers studying patient dose in imaging, exposure tracking provides an efficient method to collect large, accurate data sets without the time and expense of manually reviewing dose information embedded in image data. For retrospective studies, the exposure tracking databases are invaluable because in many cases, dose-related information is only stored locally on the imaging device and not transferred to archival storage with the images. Commercial exposure tracking products have a head start in convincing physicians, administrators, and the popular media that cumulative patient exposure tracking is the right thing to do and that dose histories are of vital importance to patient care. The IAEA, in concert with several other prominent health and radiological organizations, has an initiative for individual patient dose histories using “smart card” technology.11 It is tempting to dismiss exposure tracking as a bad idea, but that will not stop the technology from being deployed and misused by well-meaning individuals. Therefore, physicists should not eliminate themselves or discourage their colleagues from engaging with it, using it, and educating physicians and administrators about its proper and improper uses. There is an understandable concern about “scope creep” in the duties expected of the medical physicist, but exposure tracking is not the only contributor to this trend,12 which will continue with or without electronic exposure databases and reporting. Dr. Jordan and I agree on many of the valuable uses of radiation exposure tracking, particularly for population-based studies and quality improvement projects. However, for physicists to engage or embrace dose tracking for individual patients simply under the assumption that the use of the technology is inevitable is shortsighted. We are the experts regarding diagnostic radiation and to support individual dose tracking is equivalent to endorsing the concept as being meaningful to patient care. Allowing decisions on the use of individual patient radiation exposure tracking to be manipulated or dictated by those with commercial interests or by well-intentioned, yet uninformed, individuals—be they administrators, physicians, legislators, or media personnel—is neglecting our responsibilities as medical physicists. Dr. Jordan is correct in that physicists have a fleeting opportunity to play a central role in this technology, but a primary focus of that role should be to assure that it is used in a manner that is consistent with the scientific data and not let it inappropriately gain acceptance as a management tool for individual patients. This ball is already starting to roll down the hill—we cannot expect it will be easier to control once it gains more momentum. There are potentially very serious long term consequences regarding patient care if it continues unchecked. We must remain diligent in our responsibilities to our patients. In considering the pros and cons of exposure tracking, we should not equate collection and analysis of data with use of that data to influence future imaging procedures. Dr. Kofler has correctly pointed out that such uses can be outright harmful. Also, there is clearly much work to do to educate patients and physicians and counter widespread misinformation about radiation. Exposure tracking did not create this problem, and eschewing exposure tracking will not fix it. The ACR CT Dose Index Registry is an illustration of a beneficial application of exposure tracking in practice. Such a registry would not be possible without individual participants tracking and reporting their patients’ exposures. This information is used appropriately for monitoring and quality improvement efforts. While the data reported are dose metrics and not true patient doses, they represent trends in patient dose that are useful to examine and analyze. Also, eventually we can expect scanners and dose tracking platforms to be able to report patient doses more accurately than they do today. While it does not make sense to withhold beneficial imaging from patients in whom it is medically appropriate, there are scenarios where detailed knowledge of a patient's prior exposure could be useful to a clinician. First, consider a patient who has undergone a recent lengthy interventional fluoroscopy procedure. A tool that could automatically and accurately alert the physician could help them to be more aware of possible skin injury in a subsequent procedure. Second, consider the large amounts of repetitive imaging performed on radiation oncology patients during the course of treatment. Some physicists and radiation oncologists may feel that it is worth knowing how much dose these imaging procedures have delivered. Exposure tracking is not a decision support tool, but used appropriately, it can provide many benefits. Medical physicists need to provide highly visible leadership to make sure that these tools and practices are not misused.

Purpose: To determine relationships among patient size, scanner radiation output, and size-specific dose estimates (SSDEs) for adults who underwent computed tomography (CT) of the torso.Materials and Methods: Informed consent was waived for this institutional review board-approved study of existing data from 545 adult patients (322 men, 223 women) who underwent clinically indicated CT of the torso between April 1, 2007, and May 13, 2007. Automatic exposure control was used to adjust scanner output for each patient according to the measured CT attenuation. The volume CT dose index (CTDIvol) was used with measurements of patient size (anterioposterior plus lateral dimensions) and the conversion factors from the American Association of Physicists in Medicine Report 204 to determine SSDE. Linear regression models were used to assess the dependence of CTDIvol and SSDE on patient size.Results: Patient sizes ranged from 42 to 84 cm. In this range, CTDIvol was significantly correlated with size (slope = 0.34 mGy/cm; 95% confidence interval [CI]: 0.31, 0.37 mGy/cm; R-2 = 0.48; P < .001), but SSDE was independent of size (slope = 0.02 mGy/cm; 95% CI: -0.02, 0.07 mGy/cm; R-2 = 0.003; P = .3). These R-2 values indicated that patient size explained 48% of the observed variability in CTDIvol but less than 1% of the observed variability in SSDE. The regression of CTDIvol versus patient size demonstrated that, in the 42-84-cm range, CTDIvol varied from 12 to 26 mGy. However, use of the evaluated automatic exposure control system to adjust scanner output for patient size resulted in SSDE values that were independent of size.Conclusion: For the evaluated automatic exposure control system, CTDIvol (scanner output) increased linearly with patient size; however, patient dose (as indicated by SSDE) was independent of size. (C) RSNA, 2012