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Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Credible models of the correlations in longitudinal imaging require two or more pattern components. Valid inference requires enough flexibility of the correlation model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the correlation structure. For many one-dimensional spatial or temporal arrays, the linear exponent autoregressive (LEAR) correlation structure meets these two opposing goals in one model. The LEAR structure is a flexible two-parameter correlation model that applies to situations in which the within-subject correlation decreases exponentially in time or space. It allows for an attenuation or acceleration of the exponential decay rate imposed by the commonly used continuous-time AR(1) structure. We propose the Kronecker product LEAR correlation structure for multivariate repeated measures data in which the correlation between measurements for a given subject is induced by two factors (e.g., spatial and temporal dependence). Excellent analytic and numerical properties make the Kronecker product LEAR model a valuable addition to the suite of parsimonious correlation structures for multivariate repeated measures data. Longitudinal medical imaging data of caudate morphology in schizophrenia illustrates the appeal of the Kronecker product LEAR correlation structure.

Background: Using covariance or mean estimates from previous data introduces randomness into each power value in a power curve. Creating confidence intervals about the power estimates improves study planning by allowing scientists to account for the uncertainty in the power estimates. Driving examples arise in many imaging applications., Methods: We use both analytical and Monte Carlo simulation methods. Our analytical derivations apply to power for tests with the univariate approach to repeated measures (UNIREP). Approximate confidence intervals and regions for power based on an estimated covariance matrix and fixed means are described. Extensive simulations are used to examine the properties of the approximations., Results: Closed-form expressions are given for approximate power and confidence intervals and regions. Monte Carlo simulations support the accuracy of the approximations for practical ranges of sample size, rank of the design matrix, error degrees of freedom, and the amount of deviation from sphericity. The new methods provide accurate coverage probabilities for all four UNIREP tests, even for small sample sizes. Accuracy is higher for higher power values than for lower power values, making the methods especially useful in practical research conditions. The new techniques allow the plotting of power confidence regions around an estimated power curve, an approach that has been well received by researchers. Free software makes the new methods readily available., Conclusions: The new techniques allow a convenient way to account for the uncertainty of using an estimated covariance matrix in choosing a sample size for a repeated measures ANOVA design. Medical imaging and many other types of healthcare research often use repeated measures ANOVA.

Purpose: To determine effects of lesion type (calcification vs mass) and image processing on radiologist's performance for area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for detection of masses and calcifications with digital mammography in women with mammographically dense breasts., Materials and Methods: This study included 201 women who underwent digital mammography at seven U.S. and Canadian medical centers. Three image-processing algorithms were applied to the digital images, which were acquired with Fischer, General Electric, and Lorad digital mammography units. Eighteen readers participated in the reader study (six readers per algorithm). Baseline values for reader performance with screen-film mammograms were obtained through the additional interpretation of 179 screen-film mammograms. A repeated-measures analysis of covariance allowing unequal slopes was used in each of the nine analyses (AUC, sensitivity, and specificity for each of three machines). Bonferroni correction was used., Results: Although lesion type did not affect the AUC or sensitivity for Fischer digital images, it did affect specificity (P =.0004). For the General Electric digital images, AUC, sensitivity, and specificity were not affected by lesion type. For Lorad digital images, the results strongly suggested that lesion type affected AUC and sensitivity (P <.0001). None of the three image-processing methods tested affected the AUC, sensitivity, or specificity for the Fischer, General Electric, or Lorad digital images., Conclusion: Findings in this study indicate that radiologist's interpretation accuracy in interpreting digital mammograms depends on lesion type. Interpretation accuracy was not influenced by the image-processing method.

Purpose: Computer-assisted methods to analyze electronic portal images for the presence of treatment setup errors should be studied in controlled experiments before use in the clinical setting. Validation experiments using images that contain known errors usually report the smallest errors that can be detected by the image analysis algorithm. This paper offers human error-detection thresholds as one benchmark for evaluating the smallest errors detected by algorithms. Unfortunately, reliable data are lacking describing human performance. The most rigorous benchmarks for human performance are obtained under conditions that favor error detection. To establish such benchmarks, controlled observer studies were carried out to determine the thresholds of detectability for in-plane and out-of-plane translation and rotation setup errors introduced into digitally reconstructed portal radiographs (DRPRs) of prostate fields., Methods and Materials: Seventeen observers comprising radiation oncologists, radiation oncology residents, physicists, and therapy students participated in a two-alternative forced choice experiment involving 378 DRPRs computed using the National Library of Medicine Visible Human data sets. An observer viewed three images at a time displayed on adjacent computer monitors. Each image triplet included a reference digitally reconstructed radiograph displayed on the central monitor and two DRPRs displayed on the flanking monitors. One DRPR was error free. The other DRPR contained a known in-plane or out-of-plane error in the placement of the treatment field over a target region in the pelvis. The range for each type of error was determined from pilot observer studies based on a Probit model for error detection. The smallest errors approached the limit of human visual capability. The observer was told what kind of error was introduced, and was asked to choose the DRPR that contained the error. Observer decisions were recorded and analyzed using repeated-measures analysis of variance., Results: The thresholds of detectability averaged over all observers were approximately 2.5 mm for in-plane translations, 1.6 degrees for in-plane rotations, 1 degrees for out-of-plane rotations, and 8% change in magnification for out-of-plane translations along the central axis. When one inexperienced observer is excluded, the average threshold for change in magnification is 5%. Experienced observers tended to perform better, but differences between groups were not statistically significant. Thresholds were computed as averages over all observers. Because of the broad range of observer capabilities, some detection tasks were too difficult for some observers, leading to missing threshold values in our data analysis. The missing values were excluded from computation of the average thresholds reported above. The effect of the missing values is to bias the average values toward the best human performance., Conclusions: Under favorable conditions, humans can detect small errors in setup geometry. The thresholds for error detection reported in this study are believed to represent rigorous but reasonable benchmarks that can be incorporated into studies evaluating algorithms for computer-assisted detection of setup errors in electronic portal images.

Rationale and Objectives: The purpose of this study was to evaluate the accuracy and speed of a new, semiautomatic method of three-dimensional (3D)-two-dimensional (2D) vascular registration. This method should help guide endovascular procedures by allowing interpretation of each digital subtraction angiographic (DSA) image in terms of precreated, 3D vessel trees that contain "parent-child" connectivity information., Materials and Methods: Connected, 3D vessel trees were created from segmented magnetic resonance (MR) angiograms. Eleven total DSA images were registered with such trees by using both our method and the current standard (manual registration). The accuracy of each method was compared by using repeated-measures analysis of variance with correction for heterogeneity of variance to evaluate separation of curve pairs on the view plane. Subjective clinical comparisons of the two registration methods were evaluated with the sign test. Registration times were evaluated for both methods and also as a function of the error in the initial estimate of MR angiographic position., Results: The new registration method produced results that were numerically superior to those of manual registration (P < .001) and was subjectively judged to be as good as or better by clinical reviewers. Registration time with the new method was faster (P < .001). If the rotational error in the initial estimate of MR angiographic position is less than 10 degrees around each axis, the registration itself took only 1-2 minutes., Conclusion: This method is quicker than and produces results as good as or better than those of manual registration. This method should be able to calculate an initial registration matrix during endovascular embolization and adjust that matrix intermittently with registration updates provided by automatic tracking systems.

Purpose: Observer studies were conducted to test the hypothesis that radiation oncologists using a computer workstation for portal image analysis can detect setup errors at least as accurately as when following standard clinical practice of inspecting portal films on a light box., Methods and Materials: In a controlled observer study, nine radiation oncologists used a computer workstation, called PortFolio, to detect setup errors in 40 realistic digitally reconstructed portal radiograph (DRPR) images. PortFolio is a prototype workstation for radiation oncologists to display and inspect digital portal images for setup errors. PortFolio includes tools for image enhancement; alignment of crosshairs, field edges, and anatomic structures on reference and acquired images; measurement of distances and angles; and viewing registered images superimposed on one another. The test DRPRs contained known in-plane translation or rotation errors in the placement of the fields over target regions in the pelvis and head. Test images used in the study were also printed on film for observers to view on a light box and interpret using standard clinical practice. The mean accuracy for error detection for each approach was measured and the results were compared using repeated measures analysis of variance (ANOVA) with the Geisser-Greenhouse test statistic., Results: The results indicate that radiation oncologists participating in this study could detect and quantify in-plane rotation and translation errors more accurately with PortFolio compared to standard clinical practice., Conclusions: Based on the results of this limited study, it is reasonable to conclude that workstations similar to PortFolio can be used efficaciously in clinical practice.

Purpose: To determine the ability of in vitro one-dimensional and two-dimensional proton MR spectroscopy to help differentiate squamous cell carcinoma of the extracranial head and neck from normal tissues and to correlate the in vitro observations with clinical studies., Methods: In vitro 1-D and 2-D correlated proton MR spectroscopy (11 T) was performed in tissue specimens of squamous cell carcinoma of the head and neck (n = 19), in normal tissue (n = 13), in metastatic cervical lymph nodes (n = 3), and in a squamous cell carcinoma cell line. In vivo 1-D proton MR spectroscopy (1.5 T) was performed in patients with squamous cell carcinoma (n = 7) and in healthy volunteers (n = 7). The ratio of the areas under the choline (Cho) and creatine (Cr) resonances were calculated for 1-D proton MR spectra for the in vitro tissue studies and correlated with the in vivo studies. Data from in vitro 2-D correlated spectroscopy were analyzed for differences in the presence or absence of various metabolites in samples of tumor and normal tissue. Statistical analysis consisted of 2 x 2 factorial repeated measures analysis of variance (ANOVA), discriminate analysis, and chi2 test., Results: The mean in vitro 1-D proton MR spectroscopic Cho/Cr ratio was significantly higher in tumor than in normal tissue. The difference between the mean ratios appeared to increase with increasing echo time. All in vivo tumor Cho/Cr ratios were greater than the calculated mean in vitro tumor ratio, whereas six of the seven volunteers had no detectable Cho and Cr resonances. Two-dimensional correlated MR spectroscopic data revealed that a variety of amino acids have a significantly greater likelihood of being detected in tumor than in normal tissues., Conclusions: One-dimensional and 2-D proton MR spectroscopy can help differentiate primary squamous cell carcinoma and nodal metastases containing squamous cell carcinoma from normal tissue both in vitro and in vivo. In addition, 2-D spectroscopy can help identify the presence of certain amino acids in squamous cell carcinoma that are not detected in normal tissue.