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Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described., (Copyright © 2022 The Association of University Radiologists. All rights reserved.)

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis., (Published by Elsevier Inc.)

This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier. We follow with an end-to-end real-world example of development and validation of a classifier for atherosclerotic plaque phenotypes. We consider diagnostic accuracy and interchangeability to be clinically meaningful claims for a phenotype classification model informed by mp-QIB inputs, aiming to provide tools to demonstrate agreement between imaging-derived characteristics and clinically established phenotypes. Understanding that we are working in an evolving field, we close our manuscript with an acknowledgement of existing challenges and a discussion of where additional work is needed. In particular, we discuss the challenges involved with technical performance and analytical validation of mp-QIBs. We intend for this manuscript to further advance the robust and promising science of multiparametric biomarker development., (Published by Elsevier Inc.)

Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal change, or predict outcome. Multiple imaging parameters are sometimes considered separately but ideally are evaluated collectively. Often, they are transformed as Likert interpretations, ignoring the correlations of quantitative properties that may result in better reproducibility or outcome prediction. In this paper we present three use cases of multiparameter quantitative imaging: i) multidimensional descriptor, ii) phenotype classification, and iii) risk prediction. A fourth application based on data-driven markers from radiomics is also presented. We describe the technical performance characteristics and their metrics common to all use cases, and provide a structure for the development, estimation, and testing of multiparameter quantitative imaging. This paper serves as an overview for a series of individual articles on the four applications, providing the statistical framework for multiparameter imaging applications in medicine., (Copyright © 2022 The Association of University Radiologists. All rights reserved.)

Purpose: Many practices have implemented support services to assist radiologists with noninterpretive tasks; however, little research has been performed to assess the overall effect of these services. The purpose of this study was to evaluate the effect of a team of imaging service navigators (ISNs) incorporated into a practice on (1) number of communications, (2) time saved by radiologists, and (3) radiologist satisfaction with the service., Methods: The numbers and types of reports dictated by radiologists were captured for 6-month periods before and after ISN implementation. Communication rates before and after implementation were then calculated. The amount of perceived time savings using the ISN team and satisfaction with the service were assessed through pre- and postimplementation surveys of participating radiologists. Mean and median time savings and satisfaction rates were calculated., Results: The overall communication rate increased from 2.196% before ISNs to 3.278% after ISNs (49% increase; 95% confidence interval, 47%-52%). Communication rates increased among all communication subtypes (critical, urgent, routine, and actionable), with the highest increases in urgent (94%) and actionable (75%) findings. Before implementation, radiologists reported spending 39 min on average per day on communications tasks, with only 33% of radiologists indicating that the communication process was efficient. After implementation, radiologists reported mean time savings of 28 min (95% confidence interval, 19.9-35.1), and 82% of radiologists indicated a positive or highly positive view of the ISN service., Conclusions: After ISN implementation, communication rates increased and radiologists reported spending less time performing communications. Most radiologists were satisfied with the service., (Copyright © 2022 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

The design and analysis of multireader multicase (MRMC) studies are quite challenging. These studies differ from most medical studies because they need a reference standard and sampling from two populations (ie, reader and patient populations). They are quite expensive to conduct, requiring a good deal of readers' time for image interpretation. One common problem is the use of imperfect reference standards, often correlated with the test or tests being evaluated. Another common issue is oversimplification of the multidimensional MRMC data. In this study, the fundamentals of MRMC study design and analysis are reviewed. The goal is to provide investigators with a guide to the fundamentals of MRMC design and analysis, with references to more detailed discussions. In addition, readers are updated on newer areas of research, including correction for studies with multiple diagnostic accuracy end points and adjustment for location bias., (© RSNA, 2022.)

Rationale and Objectives: A critical performance metric for any quantitative imaging biomarker is its ability to reliably generate similar values on repeat testing. This is known as the repeatability of the biomarker, and it is used to determine the minimum detectable change needed in order to show that a change over time is real change and not just due to measurement error. Test-retest studies are the classic approach for estimating repeatability; however, these studies can be infeasible when the imaging is expensive, time-consuming, invasive, or requires contrast agents. The objective of this study was to develop and test a method for estimating repeatability without a test-retest study., Materials and Methods: We present a statistical method for estimating repeatability and testing whether an imaging method meets a specified criterion for repeatability in the absence of a test-retest study. The new method is applicable for the particular situation where a reference standard is available. A Monte Carlo simulation study was conducted to evaluate the performance of the new method., Results: The proposed estimator is unbiased, and hypothesis tests with the new estimator have nominal type I error rate and power similar to a test-retest study. We considered the situation where the reference standard provides the true value, as well as when the reference standard itself has various magnitudes of measurement error. An example from CT imaging biomarkers of atherosclerosis illustrates the new method., Conclusion: Precision of a QIB can be measured without a test-retest study in the situation where a reference standard is available., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background/aims: Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected., Methods: Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease., Results: Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect., Conclusion: Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

As imaging technologies and treatment options continue to advance, imaging outcome measures are becoming increasingly utilized as the basis of making major decisions in new drug development and clinical practice. Quantitative imaging biomarkers (QIBs) are now commonly used for subject selection, response assessment, and safety monitoring. Although quantitative measurements can have many advantages compared with subjective, qualitative endpoints, it is important to recognize that QIBs are measured with error. This study uses Monte Carlo simulation to examine the impact of measurement error on a variety of clinical trial designs as well as to test proposed adjustments for measurement error. The focus is on some of the QIBs currently being studied by the Quantitative Imaging Biomarkers Alliance. The results show that the ability of QIBs to discriminate between health states and predict patient outcome is attenuated by measurement error; however, the known technical performance characteristics of QIBs can be used to adjust study sample size, control the misinterpretation rate of imaging findings, and establish statistically valid decision thresholds. We conclude that estimates of the precision and bias of a QIB are important for properly designing clinical trials and establishing the level of imaging standardization required.

Rationale and Objectives: Quantitative imaging biomarkers (QIBs) are becoming increasingly adopted into clinical practice to monitor changes in patients' conditions. The repeatability coefficient (RC) is the clinical cut-point used to discern between changes in a biomarker's measurements due to measurement error and changes that exceed measurement error, thus indicating real change in the patient. Imaging biomarkers have characteristics that make them difficult for estimating the repeatability coefficient, including nonconstant error, non-Gaussian distributions, and measurement error that must be estimated from small studies., Methods: We conducted a Monte Carlo simulation study to investigate how well three statistical methods for estimating the repeatability coefficient perform under five settings common for QIBs., Results: When the measurement error is constant and replicates are normally distributed, all of the statistical methods perform well. When the measurement error is proportional to the true value, approaches that use the log transformation or coefficient of variation perform similarly. For other common settings, none of the methods for estimating the repeatability coefficient perform adequately., Conclusion: Many of the common approaches to estimating the repeatability coefficient perform well for only limited scenarios. The optimal approach depends strongly on the pattern of the within-subject variability; thus, a precision profile is critical in evaluating the technical performance of QIBs. Asymmetric bounds for detecting regression vs progression can be implemented and should be used when clinically appropriate., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

A major initiative of the Quantitative Imaging Biomarker Alliance is to develop standards-based documents called "Profiles," which describe one or more technical performance claims for a given imaging modality. The term "actor" denotes any entity (device, software, or person) whose performance must meet certain specifications for the claim to be met. The objective of this paper is to present the statistical issues in testing actors' conformance with the specifications. In particular, we present the general rationale and interpretation of the claims, the minimum requirements for testing whether an actor achieves the performance requirements, the study designs used for testing conformity, and the statistical analysis plan. We use three examples to illustrate the process: apparent diffusion coefficient in solid tumors measured by MRI, change in Perc 15 as a biomarker for the progression of emphysema, and percent change in solid tumor volume by computed tomography as a biomarker for lung cancer progression., (Copyright © 2016 The Association of University Radiologists. All rights reserved.)

Although investigators in the imaging community have been active in developing and evaluating quantitative imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo imaging studies.

Quantitative imaging biomarkers are being used increasingly in medicine to diagnose and monitor patients' disease. The computer algorithms that measure quantitative imaging biomarkers have different technical performance characteristics. In this paper we illustrate the appropriate statistical methods for assessing and comparing the bias, precision, and agreement of computer algorithms. We use data from three studies of pulmonary nodules. The first study is a small phantom study used to illustrate metrics for assessing repeatability. The second study is a large phantom study allowing assessment of four algorithms' bias and reproducibility for measuring tumor volume and the change in tumor volume. The third study is a small clinical study of patients whose tumors were measured on two occasions. This study allows a direct assessment of six algorithms' performance for measuring tumor change. With these three examples we compare and contrast study designs and performance metrics, and we illustrate the advantages and limitations of various common statistical methods for quantitative imaging biomarker studies., (© The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.)

Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research., (© The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.)

Objective: Calculating the sample size for a multireader, multicase study of readers' diagnostic accuracy is complicated. Studies in which patients can have multiple findings, as is common in many computer-aided detection (CAD) studies, are particularly challenging to design., Materials and Methods: We modified existing methods for sample size estimation for multireader, multicase studies to accommodate multiple findings on the same case. We use data from two large multireader, multicase CAD studies as ballpark estimates of parameter values., Results: Sample size tables are presented to provide an estimate of the number of patients and readers required for a multireader, multicase study with multiple findings per case; these estimates may be conservative for many CAD studies. Two figures can be used to adjust the number of readers when there is some data on the between-reader variability., Conclusion: The sample size tables are useful in determining whether a proposed study is feasible with the available resources; however, it is important that investigators compute sample size for their particular study using any available pilot data.

Purpose: Sensitivity and the false positive rate are usually defined with the patient as the unit of observation, i.e., the diagnostic test detects or does not detect disease in a patient. For tests designed to find and diagnose lesions, e.g., lung nodules, the usual definitions of sensitivity and specificity may be misleading. In this paper we describe and compare five measures of accuracy of lesion detection., Methods: The five levels of evaluation considered were patient level without localization, patient level with localization, region of interest (ROI) level without localization, ROI level with localization, and lesion level., Results: We found that estimators of sensitivity that do not require the reader to correctly locate the lesion overstate sensitivity. Patient-level estimators of sensitivity can be misleading when there is more than one lesion per patient and they reduce study power. Patient-level estimators of the false positive rate can conceal important differences between techniques. Referring clinicians rely on a test's reported accuracy to both choose the appropriate test and plan management for their patients. If reported sensitivity is overstated, the clinician could choose the test for disease screening, and have false confidence that a negative test represents the true absence of lesions. Similarly, the lower false positive rate associated with patient-level estimators can mislead clinicians about the diagnostic value of the test and consequently that a positive finding is real., Conclusion: We present clear recommendations for studies assessing and comparing the accuracy of tests tasked with the detection and interpretation of lesions...

Rationale and Objectives: Investigators often need to assess the accuracies of diagnostic tests when the gold standard is not binary-scale. The objective of this article is to describe nonparametric estimators of diagnostic test accuracy when the gold standard is continuous, ordinal, and nominal scale., Materials and Methods: A nonparametric method of estimating and comparing the area under receiver operating characteristic (ROC) curves, proposed by DeLong et al, is extended to situations in which the gold standard is not binary. Two examples illustrate the methods., Results: Measures of diagnostic test accuracy, their variance, and tests for comparing two diagnostic tests' accuracies in paired designs are presented for situations in which the gold standard is continuous, ordinal, and nominal scale. These summary measures of diagnostic test accuracy are analogous in form and interpretation to the area under the ROC curve., Conclusion: Dichotomizing the outcomes of a gold standard so that traditional ROC methods can be applied can lead to bias. The methods described here are useful for assessing and comparing summary test accuracy when the gold standard is not binary scale. They have limitations similar to other summary indices.

Sensitivity and specificity are the basic measures of accuracy of a diagnostic test; however, they depend on the cut point used to define "positive" and "negative" test results. As the cut point shifts, sensitivity and specificity shift. The receiver operating characteristic (ROC) curve is a plot of the sensitivity of a test versus its false-positive rate for all possible cut points. The advantages of the ROC curve as a means of defining the accuracy of a test, construction of the ROC, and identification of the optimal cut point on the ROC curve are discussed. Several summary measures of the accuracy of a test, including the commonly used percentage of correct diagnoses and area under the ROC curve, are described and compared. Two examples of ROC curve application in radiologic research are presented., (Copyright RSNA, 2003)

Objective: To determine whether a simulated low-dose metal artifact reduction (MAR) CT technique is comparable with a clinical dose MAR technique for shoulder arthroplasty evaluation. Materials and methods: Two shoulder arthroplasties in cadavers and 25 shoulder arthroplasties in patients were scanned using a clinical dose (140 kVp, 300 qrmAs); cadavers were also scanned at half dose (140 kVp, 150 qrmAs). Images were reconstructed using a MAR CT algorithm at full dose and a noise-insertion algorithm simulating 50% dose reduction. For the actual and simulated half-dose cadaver scans, differences in SD for regions of interest were assessed, and streak artifact near the arthroplasty was graded by 3 blinded readers. Simulated half-dose scans were compared with full-dose scans in patients by measuring differences in implant position and by comparing readers' grades of periprosthetic osteolysis and muscle atrophy. Results: The mean difference in SD between actual and simulated half-dose methods was 2.42 HU (95% CI [1.4, 3.4]). No differences in streak artifact grades were seen in 13/18 (72.2%) comparisons in cadavers. In patients, differences in implant position measurements were within 1° or 1 mm in 149/150 (99.3%) measurements. The inter-reader agreement rates were nearly identical when readers were using full-dose (77.3% [232/300] for osteolysis and 76.9% [173/225] for muscle atrophy) and simulated half-dose (76.7% [920/1200] for osteolysis and 74.0% [666/900] for muscle atrophy) scans. Conclusion: A simulated half-dose MAR CT technique is comparable both quantitatively and qualitatively with a standard-dose technique for shoulder arthroplasty evaluation, demonstrating that this technique could be used to reduce dose in arthroplasty imaging. [ABSTRACT FROM AUTHOR]

As imaging technologies and treatment options continue to advance, imaging outcome measures are becoming increasingly utilized as the basis of making major decisions in new drug development and clinical practice. Quantitative imaging biomarkers (QIBs) are now commonly used for subject selection, response assessment, and safety monitoring. Although quantitative measurements can have many advantages compared with subjective, qualitative endpoints, it is important to recognize that QIBs are measured with error. This study uses Monte Carlo simulation to examine the impact of measurement error on a variety of clinical trial designs as well as to test proposed adjustments for measurement error. The focus is on some of the QIBs currently being studied by the Quantitative Imaging Biomarkers Alliance. The results show that the ability of QIBs to discriminate between health states and predict patient outcome is attenuated by measurement error; however, the known technical performance characteristics of QIBs can be used to adjust study sample size, control the misinterpretation rate of imaging findings, and establish statistically valid decision thresholds. We conclude that estimates of the precision and bias of a QIB are important for properly designing clinical trials and establishing the level of imaging standardization required. [ABSTRACT FROM AUTHOR]

Biostatistics is an essential component in most original research studies in imaging. In this article we discuss five key statistical concepts for study design and analyses in modern imaging research: statistical hypothesis testing, particularly focusing on noninferiority studies; imaging outcomes especially when there is no reference standard; dealing with the multiplicity problem without spending all your study power; relevance of confidence intervals in reporting and interpreting study results; and finally tools for assessing quantitative imaging biomarkers. These concepts are presented first as examples of conversations between investigator and biostatistician, and then more detailed discussions of the statistical concepts follow. Three skeletal radiology examples are used to illustrate the concepts. [ABSTRACT FROM AUTHOR]

Objectives: To assess the value of secretin during magnetic resonance cholangiopancreatography (MRCP) in demonstrating communication between cystic lesions and the pancreatic duct to help determine the diagnosis of side-branch intraductal papillary mucinous neoplasm (SB-IPMN). Methods: This is an IRB-approved, HIPAA-compliant retrospective study of 29 SB-IPMN patients and 13 non-IPMN subjects (control) who underwent secretin-enhanced MRCP (s-MRCP). Two readers blinded to the final diagnosis reviewed three randomised image sets: (1) pre-secretin HASTE, (2) dynamic s-MRCP and (3) post-secretin HASTE. Logistic regression, generalised linear models and ROC analyses were used to compare pre- and post-secretin results. Results: There was no significant difference in median scores for the pre-secretin [reader 1: 1; reader 2: 2 (range -2 to 2)] and post-secretin HASTE [reader 1: 1; reader 2: 1 (range -2 to 2)] in the SB-IPMN group ( P = 0.14), while the scores were lower for s-MRCP [reader 1: 0.5 (range -2 to 2); reader 2: 0 (range -1 to 2); P = 0.016]. There was no significant difference in mean maximum diameter of SB-IPMN on pre- and post-secretin HASTE, and s-MRCP ( P > 0.05). Conclusion: Secretin stimulation did not add to MRCP in characterising pancreatic cystic lesions as SB-IPMN. Key Points: • Magnetic resonance cholangiopancreatography (MRCP) is used to evaluate pancreatic cystic lesions. • Intraductal papillary mucinous neoplasm (IPMN) is a type of pancreatic cystic neoplasm. • Secretin administration does not facilitate the diagnosis of IPMN on MRCP. [ABSTRACT FROM AUTHOR]

Introduction: The sensitivity of CT based lung cancer screening for the detection of early lung cancer is balanced by the high number of benign lung nodules identified, the unknown consequences of radiation from the test, and the potential costs of a CT based screening program. CAD chest radiography may improve the sensitivity of standard chest radiography while minimizing the risks of CT based screening. Methods: Study subjects were age 40–75 years with 10+ pack-years of smoking and/or an additional risk for developing lung cancer. Subjects were randomized to receive a PA view chest radiograph or placebo control (went through the process of being imaged but were not imaged). Images were reviewed first without then with the assistance of CAD. Actionable nodules were reported and additional evaluation was tracked. The primary outcome was the rate of developing symptomatic advanced stage lung cancer. Results: 1,424 subjects were enrolled. 710 received a CAD chest radiograph, 29 of whom were found to have an actionable lung nodule on prevalence screening. Of the 15 subjects who had a chest CT performed for additional evaluation, a lung nodule was confirmed in 4, 2 of which represented lung cancer. Both of the cancers were seen by the radiologist unaided and were identified by the CAD chest radiograph. The cumulative incidence of symptomatic advanced lung cancer was 0.42 cases per 100 person-years in the control arm; there were no events in the screening arm. Conclusions: Further evaluation is necessary to determine if CAD chest radiography has a role as a lung cancer screening tool. ClinicalTrials.gov identifier NCT01663155 [ABSTRACT FROM AUTHOR]

Background: Dual-source computed tomography (DSCT) was introduced with significant hardware and software changes compared with single-source CT (SSCT), resulting in improved temporal resolution (83 ms) and the potential for improved image quality. The effect of these changes on radiation dose requirements for coronary CT angiography in clinical practice has not been investigated. Objective: We evaluated patient radiation dose and image quality of electrocardiogram (ECG)–gated helical techniques, using DSCT compared with SSCT for clinical imaging of the coronary arteries. Methods: DSCT data from 160 patients were evaluated; 82 patients (DSCT group 1) were imaged with early software, and 78 patients (DSCT group 2) were imaged with a later software version. Patients imaged with SSCT (n = 124) were the control group. Effective radiation dose values were estimated for all patients. Image noise was measured, and image quality was evaluated on a 5-point scale. Results: Effective dose values for DSCT group 2 (11.7 ± 4.0 mSv) were not different from those for SSCT group (10.9 ± 2.9 mSv); the highest doses, 13.2 ± 3.2 mSv, were recorded for DSCT group 1 (P < 0.001). A decrease in image noise was observed for DSCT compared with SSCT (P ≤ 0.001) as was an increase in image quality (P < 0.01). With optimized DSCT imaging, lower dose values were associated with (1) shorter scan range, (2) lower maximum tube current, and (3) lower fraction of R-R interval receiving maximum tube current. Conclusion: ECG-gated helical DSCT can provide images of the coronary arteries with improved image quality and decreased noise without an increase in radiation dose compared with SSCT in clinical patient groups. [Copyright &y& Elsevier]