Multi-Institutional Evaluation of Digital Tomosynthesis, Dual-Energy Radiography, and Conventional Chest Radiography for the Detection and Management of Pulmonary Nodules.

Purpose To conduct a multi-institutional, multireader study to compare the performance of digital tomosynthesis, dual-energy (DE) imaging, and conventional chest radiography for pulmonary nodule detection and management. Materials and Methods In this binational, institutional review board-approved, HIPAA-compliant prospective study, 158 subjects (43 subjects with normal findings) were enrolled at four institutions. Informed consent was obtained prior to enrollment. Subjects underwent chest computed tomography (CT) and imaging with conventional chest radiography (posteroanterior and lateral), DE imaging, and tomosynthesis with a flat-panel imaging device. Three experienced thoracic radiologists identified true locations of nodules (n = 516, 3-20-mm diameters) with CT and recommended case management by using Fleischner Society guidelines. Five other radiologists marked nodules and indicated case management by using images from conventional chest radiography, conventional chest radiography plus DE imaging, tomosynthesis, and tomosynthesis plus DE imaging. Sensitivity, specificity, and overall accuracy were measured by using the free-response receiver operating characteristic method and the receiver operating characteristic method for nodule detection and case management, respectively. Results were further analyzed according to nodule diameter categories (3-4 mm, >4 mm to 6 mm, >6 mm to 8 mm, and >8 mm to 20 mm). Results Maximum lesion localization fraction was higher for tomosynthesis than for conventional chest radiography in all nodule size categories (3.55-fold for all nodules, P < .001; 95% confidence interval [CI]: 2.96, 4.15). Case-level sensitivity was higher with tomosynthesis than with conventional chest radiography for all nodules (1.49-fold, P < .001; 95% CI: 1.25, 1.73). Case management decisions showed better overall accuracy with tomosynthesis than with conventional chest radiography, as given by the area under the receiver operating characteristic curve (1.23-fold, P < .001; 95% CI: 1.15, 1.32). There were no differences in any specificity measures. DE imaging did not significantly affect nodule detection when paired with either conventional chest radiography or tomosynthesis. Conclusion Tomosynthesis outperformed conventional chest radiography for lung nodule detection and determination of case management; DE imaging did not show significant differences over conventional chest radiography or tomosynthesis alone. These findings indicate performance likely achievable with a range of reader expertise. © RSNA, 2016 Online supplemental material is available for this article.

Population of 224 realistic human subject-based computational breast phantoms.

PURPOSE: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. METHODS: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. RESULTS: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. CONCLUSIONS: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.

Development and application of a suite of 4-D virtual breast phantoms for optimization and evaluation of breast imaging systems.

Mammography is currently the most widely utilized tool for detection and diagnosis of breast cancer. However, in women with dense breast tissue, tissue overlap may obscure lesions. Digital breast tomosynthesis can reduce tissue overlap. Furthermore, imaging with contrast enhancement can provide additional functional information about lesions, such as morphology and kinetics, which in turn may improve lesion identification and characterization. The performance of these imaging techniques is strongly dependent on the structural composition of the breast, which varies significantly among patients. Therefore, imaging system and imaging technique optimization should take patient variability into consideration. Furthermore, optimization of imaging techniques that employ contrast agents should include the temporally varying breast composition with respect to the contrast agent uptake kinetics. To these ends, we have developed a suite of 4-D virtual breast phantoms, which are incorporated with the kinetics of contrast agent propagation in different tissues and can realistically model normal breast parenchyma as well as benign and malignant lesions. This development presents a new approach in performing simulation studies using truly anthropomorphic models. To demonstrate the utility of the proposed 4-D phantoms, we present a simplified example study to compare the performance of 14 imaging paradigms qualitatively and quantitatively.

Frequency response and distortion properties of nonlinear image processing algorithms and the importance of imaging context.

PURPOSE: The most common metrics for resolution analysis in medical imaging are valid only for (approximately) linear systems. While analogues to these metrics have been used in attempts to describe resolution performance in nonlinear systems, the analysis is incomplete since distortion effects are often ignored. The authors have developed a methodology to analyze the amplitude modulation and waveform distortion properties of nonlinear systems with specific application to medical image processing algorithms. METHODS: Using sinusoidal basis functions, two metrics were derived which distinguish amplitude modulation from nonlinear waveform distortion: principle frequency response and distortion power spectrum, respectively. Additionally, two figures of merit were developed to describe the relative impact of nonlinear distortion as a result of image processing: distortion index (DI) and ΣDI. Three nonlinear denoising algorithms, the median, bilateral, and wavelet denoising filters, were selected as example functions to demonstrate the utility of the metrics derived in this study. RESULTS: Each filter showed very different resolution and waveform distortion properties. In particular, the amplitude and extent of nonlinear distortion depended strongly on image context and the type of nonlinear mechanism employed. Nonlinear waveform distortion constituted up to 30% of the median filter output signal power in high contrast-to-noise ratio (CNR) scenarios. Conversely, nonlinear distortion never exceeded 1% of the bilateral filter output signal power. The wavelet denoising response contained between 1% and 9% distortion which varied weakly as a function of CNR. CONCLUSIONS: The analytical metrics described in the paper demonstrate the importance of considering both resolution- and distortion-related effects in characterizing the performance of nonlinear imaging systems with specific application to image processing algorithms. Findings with three common nonlinear algorithms demonstrate a range of CNR values over which it is important to consider the impact of the nonlinear nature of each algorithm. Background context is also shown to influence the degree to which the nonlinear nature of the algorithm influences resolution and distortion. While no single metric can yet anticipate observer performance in nonlinear systems, the approach described can demonstrate the range of imaging contexts over which such nonlinear effects are important to consider.

Generation of a suite of 3D computer-generated breast phantoms from a limited set of human subject data.

PURPOSE: The authors previously reported on a three-dimensional computer-generated breast phantom, based on empirical human image data, including a realistic finite-element based compression model that was capable of simulating multimodality imaging data. The computerized breast phantoms are a hybrid of two phantom generation techniques, combining empirical breast CT (bCT) data with flexible computer graphics techniques. However, to date, these phantoms have been based on single human subjects. In this paper, the authors report on a new method to generate multiple phantoms, simulating additional subjects from the limited set of original dedicated breast CT data. The authors developed an image morphing technique to construct new phantoms by gradually transitioning between two human subject datasets, with the potential to generate hundreds of additional pseudoindependent phantoms from the limited bCT cases. The authors conducted a preliminary subjective assessment with a limited number of observers (n = 4) to illustrate how realistic the simulated images generated with the pseudoindependent phantoms appeared. METHODS: Several mesh-based geometric transformations were developed to generate distorted breast datasets from the original human subject data. Segmented bCT data from two different human subjects were used as the "base" and "target" for morphing. Several combinations of transformations were applied to morph between the "base' and "target" datasets such as changing the breast shape, rotating the glandular data, and changing the distribution of the glandular tissue. Following the morphing, regions of skin and fat were assigned to the morphed dataset in order to appropriately assign mechanical properties during the compression simulation. The resulting morphed breast was compressed using a finite element algorithm and simulated mammograms were generated using techniques described previously. Sixty-two simulated mammograms, generated from morphing three human subject datasets, were used in a preliminary observer evaluation where four board certified breast radiologists with varying amounts of experience ranked the level of realism (from 1 = "fake" to 10 = "real") of the simulated images. RESULTS: The morphing technique was able to successfully generate new and unique morphed datasets from the original human subject data. The radiologists evaluated the realism of simulated mammograms generated from the morphed and unmorphed human subject datasets and scored the realism with an average ranking of 5.87 ± 1.99, confirming that overall the phantom image datasets appeared more "real" than "fake." Moreover, there was not a significant difference (p > 0.1) between the realism of the unmorphed datasets (6.0 ± 1.95) compared to the morphed datasets (5.86 ± 1.99). Three of the four observers had overall average rankings of 6.89 ± 0.89, 6.9 ± 1.24, 6.76 ± 1.22, whereas the fourth observer ranked them noticeably lower at 2.94 ± 0.7. CONCLUSIONS: This work presents a technique that can be used to generate a suite of realistic computerized breast phantoms from a limited number of human subjects. This suite of flexible breast phantoms can be used for multimodality imaging research to provide a known truth while concurrently producing realistic simulated imaging data.

The effect of averaging adjacent planes for artifact reduction in matrix inversion tomosynthesis.

PURPOSE: Matrix inversion tomosynthesis (MITS) uses linear systems theory and knowledge of the imaging geometry to remove tomographic blur that is present in conventional backprojection tomosynthesis reconstructions, leaving in-plane detail rendered clearly. The use of partial-pixel interpolation during the backprojection process introduces imprecision in the MITS modeling of tomographic blur, and creates low-contrast artifacts in some MITS planes. This paper examines the use of MITS slabs, created by averaging several adjacent MITS planes, as a method for suppressing partial-pixel artifacts. METHODS: Human chest tomosynthesis projection data, acquired as part of an IRB-approved pilot study, were used to generate MITS planes, three-plane MITS slabs (MITSa3), five-plane MITS slabs (MITSa5), and seven-plane MITS slabs (MITSa7). These were qualitatively examined for partial-pixel artifacts and the visibility of normal and abnormal anatomy. Additionally, small (5 mm) subtle pulmonary nodules were simulated and digitally superimposed upon human chest tomosynthesis projection images, and their visibility was qualitatively assessed in the different reconstruction techniques. Simulated images of a thin wire were used to generate modulation transfer function (MTF) and slice-sensitivity profile curves for the different MITS and MITS slab techniques, and these were examined for indications of partial-pixel artifacts and frequency response uniformity. Finally, mean-subtracted, exposure-normalized noise power spectra (ENNPS) estimates were computed and compared for MITS and MITS slab reconstructions, generated from 10 sets of tomosynthesis projection data of an acrylic slab. The simulated in-plane MTF response of each technique was also combined with the square root of the ENNPS estimate to yield stochastic signal-to-noise ratio (SNR) information about the different reconstruction techniques. RESULTS: For scan angles of 20° and 5 mm plane separation, seven MITS planes must be averaged to sufficiently remove partial-pixel artifacts. MITSa7 does appear to subtly reduce the contrast of high-frequency "edge" information, but the removal of partial-pixel artifacts makes the appearance of low-contrast, fine-detail anatomy even more conspicuous in MITSa7 slices. MITSa7 also appears to render simulated subtle 5 mm pulmonary nodules with greater visibility than MITS alone, in both the open lung and regions overlying the mediastinum. Finally, the MITSa7 technique reduces stochastic image variance, though the in-plane stochastic SNR (for very thin objects which do not span multiple MITS planes) is only improved at spatial frequencies between 0.05 and 0.20 cycles∕mm. CONCLUSIONS: The MITSa7 method is an improvement over traditional single-plane MITS for thoracic imaging and the pulmonary nodule detection task, and thus the authors plan to use the MITSa7 approach for all future MITS research at the authors' institution.

An image-based technique to assess the perceptual quality of clinical chest radiographs.

PURPOSE: Current clinical image quality assessment techniques mainly analyze image quality for the imaging system in terms of factors such as the capture system modulation transfer function, noise power spectrum, detective quantum efficiency, and the exposure technique. While these elements form the basic underlying components of image quality, when assessing a clinical image, radiologists seldom refer to these factors, but rather examine several specific regions of the displayed patient images, further impacted by a particular image processing method applied, to see whether the image is suitable for diagnosis. In this paper, the authors developed a novel strategy to simulate radiologists' perceptual evaluation process on actual clinical chest images. METHODS: Ten regional based perceptual attributes of chest radiographs were determined through an observer study. Those included lung grey level, lung detail, lung noise, rib-lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm-lung contrast, and subdiaphragm area. Each attribute was characterized in terms of a physical quantity measured from the image algorithmically using an automated process. A pilot observer study was performed on 333 digital chest radiographs, which included 179 PA images with 10:1 ratio grids (set 1) and 154 AP images without grids (set 2), to ascertain the correlation between image perceptual attributes and physical quantitative measurements. To determine the acceptable range of each perceptual attribute, a preliminary quality consistency range was defined based on the preferred 80% of images in set 1. Mean value difference (µ(1) - µ(2)) and variance ratio (σ(1) (2)/σ(2) (2)) were investigated to further quantify the differences between the selected two image sets. RESULTS: The pilot observer study demonstrated that our regional based physical quantity metrics of chest radiographs correlated very well with their corresponding perceptual attributes. The distribution comparisons, mean value difference estimations, and variance ratio estimations of each physical quantity between sets of images from two different techniques matched our expectation that the image quality of set 1 should be better than that of set 2. CONCLUSIONS: The measured physical quantities provide a robust reflection of perceptual image quality in clinical images. The methodology can be readily applied for automated evaluation of perceptual image quality in clinical chest radiographs.

Estimation of the two-dimensional presampled modulation transfer function of digital radiography devices using one-dimensional test objects.

PURPOSE: The modulation transfer function (MTF) of medical imaging devices is commonly reported in the form of orthogonal one-dimensional (1D) measurements made near the vertical and horizontal axes with a slit or edge test device. A more complete description is found by measuring the two-dimensional (2D) MTF. Some 2D test devices have been proposed, but there are some issues associated with their use: (1) they are not generally available; (2) they may require many images; (3) the results may have diminished accuracy; and (4) their implementation may be particularly cumbersome. This current work proposes the application of commonly available 1D test devices for practical and accurate estimation of the 2D presampled MTF of digital imaging systems. METHODS: Theory was developed and applied to ensure adequate fine sampling of the system line spread function for 1D test devices at orientations other than approximately vertical and horizontal. Methods were also derived and tested for slit nonuniformity correction at arbitrary angle. Techniques were validated with experimental measurements at ten angles using an edge test object and three angles using a slit test device on an indirect-detection flat-panel system [GE Revolution XQ∕i (GE Healthcare, Waukesha, WI)]. The 2D MTF was estimated through a simple surface fit with interpolation based on Delaunay triangulation of the 1D edge-based MTF measurements. Validation by synthesis was also performed with simulated images from a hypothetical direct-detection flat-panel device. RESULTS: The 2D MTF derived from physical measurements yielded an average relative precision error of 0.26% for frequencies below the cutoff (2.5 mm(-1)) and approximate circular symmetry at frequencies below 4 mm(-1). While slit analysis generally agreed with the results of edge analysis, the two showed subtle differences at frequencies above 4 mm(-1). Slit measurement near 45° revealed radial asymmetry in the MTF resulting from the square pixel aperture (0.2 mm × 0.2 mm), a characteristic which was not necessarily appreciated with the orthogonal 1D MTF measurements. In simulation experiments, both slit- and edge-based measurements resolved the radial asymmetries in the 2D MTF. The average absolute relative accuracy error in the 2D MTF between the DC and cutoff (2.5 mm(-1)) frequencies was 0.13% with average relative precision error of 0.11%. Other simulation results were similar to those derived from physical data. CONCLUSIONS: Overall, the general availability, acceptance, accuracy, and ease of implementation of 1D test devices for MTF assessment make this a valuable technique for 2D MTF estimation.

Plate-specific gain map correction for the improvement of detective quantum efficiency in computed radiography.

PURPOSE: The purpose of this work is to improve the noise power spectrum (NPS), and thus the detective quantum efficiency (DQE), of computed radiography (CR) images by correcting for spatial gain variations specific to individual imaging plates. CR devices have not traditionally employed gain-map corrections, unlike the case with flat-panel detectors, because of the multiplicity of plates used with each reader. The lack of gain-map correction has limited the DQE(f) at higher exposures with CR. This current work describes a feasible solution to generating plate-specific gain maps. METHODS: Ten high-exposure open field images were taken with an RQA5 spectrum, using a sixth generation CR plate suspended in air without a cassette. Image values were converted to exposure, the plates registered using fiducial dots on the plate, the ten images averaged, and then high-pass filtered to remove low frequency contributions from field inhomogeneity. A gain-map was then produced by converting all pixel values in the average into fractions with mean of one. The resultant gain-map of the plate was used to normalize subsequent single images to correct for spatial gain fluctuation. To validate performance, the normalized NPS (NNPS) for all images was calculated both with and without the gain-map correction. Variations in the quality of correction due to exposure levels, beam voltage/spectrum, CR reader used, and registration were investigated. RESULTS: The NNPS with plate-specific gain-map correction showed improvement over the noncorrected case over the range of frequencies from 0.15 to 2.5 mm(-1). At high exposure (40 mR), NNPS was 50%-90% better with gain-map correction than without. A small further improvement in NNPS was seen from carefully registering the gain-map with subsequent images using small fiducial dots, because of slight misregistration during scanning. Further improvement was seen in the NNPS from scaling the gain map about the mean to account for different beam spectra. CONCLUSIONS: This study demonstrates that a simple gain-map can be used to correct for the fixed-pattern noise in a given plate and thus improve the DQE of CR imaging. Such a method could easily be implemented by manufacturers because each plate has a unique bar code and the gain-map for all plates associated with a reader could be stored for future retrieval. These experiments indicated that an improvement in NPS (and hence, DQE) is possible, depending on exposure level, over a wide range of frequencies with this technique.

Automated lung segmentation in digital chest tomosynthesis.

PURPOSE: The purpose of this study was to develop an automated lung segmentation method for computerized detection of lung nodules in digital chest tomosynthesis. METHODS: The authors collected 45 digital tomosynthesis scans and manually segmented reference lung regions in each scan to assess the performance of the method. The authors automated the technique by calculating the edge gradient in an original image for enhancing lung outline and transforming the edge gradient image to polar coordinate space. The authors then employed a dynamic programming technique to delineate outlines of the unobscured lungs in the transformed edge gradient image. The lung outlines were converted back to the original image to provide the final segmentation result. The above lung segmentation algorithm was first applied to the central reconstructed tomosynthesis slice because of the absence of ribs overlapping lung structures. The segmented lung in the central slice was then used to guide lung segmentation in noncentral slices. The authors evaluated the segmentation method by using (1) an overlap rate of lung regions, (2) a mean absolute distance (MAD) of lung borders, (3) a Hausdorff distance of lung borders between the automatically segmented lungs and manually segmented reference lungs, and (4) the fraction of nodules included in the automatically segmented lungs. RESULTS: The segmentation method achieved mean overlap rates of 85.7%, 88.3%, and 87.0% for left lungs, right lungs, and entire lungs, respectively; mean MAD of 4.8, 3.9, and 4.4 mm for left lungs, right lungs, and entire lungs, respectively; and mean Hausdorrf distance of 25.0 mm, 25.5 mm, and 30.1 mm for left lungs, right lungs, and entire lungs, respectively. All of the nodules inside the reference lungs were correctly included in the segmented lungs obtained with the lung segmentation method. CONCLUSIONS: The method achieved relatively high accuracy for lung segmentation and will be useful for computer-aided detection of lung nodules in digital tomosynthesis.

Biplane correlation imaging: a feasibility study based on phantom and human data.

The objective of this study was to implement and evaluate the performance of a biplane correlation imaging (BCI) technique aimed to reduce the effect of anatomic noise and improve the detection of lung nodules in chest radiographs. Seventy-one low-dose posterior-anterior images were acquired from an anthropomorphic chest phantom with 0.28° angular separations over a range of ±10° along the vertical axis within an 11 s interval. Similar data were acquired from 19 human subjects with institutional review board approval and informed consent. The data were incorporated into a computer-aided detection (CAD) algorithm in which suspect lesions were identified by examining the geometrical correlation of the detected signals that remained relatively constant against variable anatomic backgrounds. The data were analyzed to determine the effect of angular separation, and the overall sensitivity and false-positives for lung nodule detection. The best performance was achieved for angular separations of the projection pairs greater than 5°. Within that range, the technique provided an order of magnitude decrease in the number of false-positive reports when compared with CAD analysis of single-view images. Overall, the technique yielded ~1.1 false-positive per patient with an average sensitivity of 75%. The results indicated that the incorporation of angular information can offer a reduction in the number of false-positives without a notable reduction in sensitivity. The findings suggest that the BCI technique has the potential for clinical implementation as a cost-effective technique to improve the detection of subtle lung nodules with lowered rate of false-positives.

An analysis of the mechanical parameters used for finite element compression of a high-resolution 3D breast phantom.

PURPOSE: The authors previously introduced a methodology to generate a realistic three-dimensional (3D), high-resolution, computer-simulated breast phantom based on empirical data. One of the key components of such a phantom is that it provides a means to produce a realistic simulation of clinical breast compression. In the current study, they have evaluated a finite element (FE) model of compression and have demonstrated the effect of a variety of mechanical properties on the model using a dense mesh generated from empirical breast data. While several groups have demonstrated an effective compression simulation with lower density finite element meshes, the presented study offers a mesh density that is able to model the morphology of the inner breast structures more realistically than lower density meshes. This approach may prove beneficial for multimodality breast imaging research, since it provides a high level of anatomical detail throughout the simulation study. METHODS: In this paper, the authors describe methods to improve the high-resolution performance of a FE compression model. In order to create the compressible breast phantom, dedicated breast CT data was segmented and a mesh was generated with 4-noded tetrahedral elements. Using an explicit FE solver to simulate breast compression, several properties were analyzed to evaluate their effect on the compression model including: mesh density, element type, density, and stiffness of various tissue types, friction between the skin and the compression plates, and breast density. Following compression, a simulated projection was generated to demonstrate the ability of the compressible breast phantom to produce realistic simulated mammographic images. RESULTS: Small alterations in the properties of the breast model can change the final distribution of the tissue under compression by more than 1 cm; which ultimately results in different representations of the breast model in the simulated images. The model properties that impact displacement the most are mesh density, friction between the skin and the plates, and the relative stiffness of the different tissue types. CONCLUSIONS: The authors have developed a 3D, FE breast model that can yield high spatial resolution breast deformations under uniaxial compression for imaging research purposes and demonstrated that small changes in the mechanical properties can affect images generated using the phantom.

Effective dose efficiency: an application-specific metric of quality and dose for digital radiography.

The detective quantum efficiency (DQE) and the effective DQE (eDQE) are relevant metrics of image quality for digital radiography detectors and systems, respectively. The current study further extends the eDQE methodology to technique optimization using a new metric of the effective dose efficiency (eDE), reflecting both the image quality as well as the effective dose (ED) attributes of the imaging system. Using phantoms representing pediatric, adult and large adult body habitus, image quality measurements were made at 80, 100, 120 and 140 kVp using the standard eDQE protocol and exposures. ED was computed using Monte Carlo methods. The eDE was then computed as a ratio of image quality to ED for each of the phantom/spectral conditions. The eDQE and eDE results showed the same trends across tube potential with 80 kVp yielding the highest values and 120 kVp yielding the lowest. The eDE results for the pediatric phantom were markedly lower than the results for the adult phantom at spatial frequencies lower than 1.2-1.7 mm(-1), primarily due to a correspondingly higher value of ED per entrance exposure. The relative performance for the adult and large adult phantoms was generally comparable but affected by kVps. The eDE results for the large adult configuration were lower than the eDE results for the adult phantom, across all spatial frequencies (120 and 140 kVp) and at spatial frequencies greater than 1.0 mm(-1) (80 and 100 kVp). Demonstrated for chest radiography, the eDE shows promise as an application-specific metric of imaging performance, reflective of body habitus and radiographic technique, with utility for radiography protocol assessment and optimization.

Effective DQE (eDQE) and speed of digital radiographic systems: an experimental methodology.

Prior studies on performance evaluation of digital radiographic systems have primarily focused on the assessment of the detector performance alone. However, the clinical performance of such systems is also substantially impacted by magnification, focal spot blur, the presence of scattered radiation, and the presence of an antiscatter grid. The purpose of this study is to evaluate an experimental methodology to assess the performance of a digital radiographic system, including those attributes, and to propose a new metric, effective detective quantum efficiency (eDQE), a candidate for defining the efficiency or speed of digital radiographic imaging systems. The study employed a geometric phantom simulating the attenuation and scatter properties of the adult human thorax and a representative indirect flat-panel-based clinical digital radiographic imaging system. The noise power spectrum (NPS) was derived from images of the phantom acquired at three exposure levels spanning the operating range of the clinical system. The modulation transfer function (MTF) was measured using an edge device positioned at the surface of the phantom, facing the x-ray source. Scatter measurements were made using a beam stop technique. The eDQE was then computed from these measurements, along with measures of phantom attenuation and x-ray flux. The MTF results showed notable impact from the focal spot blur, while the NPS depicted a large component of structured noise resulting from use of an antiscatter grid. The eDQE was found to be an order of magnitude lower than the conventional DQE. At 120 kVp, eDQE(0) was in the 8%-9% range, fivefold lower than DQE(0) at the same technique. The eDQE method yielded reproducible estimates of the system performance in a clinically relevant context by quantifying the inherent speed of the system, that is, the actual signal to noise ratio that would be measured under clinical operating conditions.

Methodology for generating a 3D computerized breast phantom from empirical data.

The initial process for creating a flexible three-dimensional computer-generated breast phantom based on empirical data is described. Dedicated breast computed-tomography data were processed to suppress noise and scatter artifacts in the reconstructed image set. An automated algorithm was developed to classify the breast into its primary components. A preliminary phantom defined using subdivision surfaces was generated from the segmented data. To demonstrate potential applications of the phantom, simulated mammographic image data were acquired of the phantom using a simplistic compression model and an analytic projection algorithm directly on the surface model. The simulated image was generated using a model for a polyenergetic cone-beam projection of the compressed phantom. The methods used to create the breast phantom generate resulting images that have a high level of tissue structure detail available and appear similar to actual mammograms. Fractal dimension measurements of simulated images of the phantom are comparatively similar to measurements from images of real human subjects. A realistic and geometrically defined breast phantom that can accurately simulate imaging data may have many applications in breast imaging research.

Chest tomosynthesis: technical principles and clinical update.

Digital tomosynthesis is a radiographic technique that can produce an arbitrary number of section images of a patient from a single pass of the X-ray tube. It utilizes a conventional X-ray tube, a flat-panel detector, a computer-controlled tube mover, and special reconstruction algorithms to produce section images. While it does not have the depth resolution of computed tomography (CT), tomosynthesis provides some of the tomographic benefits of CT but at lower cost and radiation dose than CT. Compared to conventional chest radiography, chest tomosynthesis results in improved visibility of normal structures such as vessels, airway and spine. By reducing visual clutter from overlying normal anatomy, it also enhances detection of small lung nodules. This review article outlines the components of a tomosynthesis system, discusses results regarding improved lung nodule detection from the recent literature, and presents examples of nodule detection from a clinical trial in human subjects. Possible implementation strategies for use in clinical chest imaging are discussed.

Tomosynthesis imaging: at a translational crossroads.

Tomosynthesis is a decades-old technique for section imaging that has seen a recent upsurge in interest due to its promise to provide three-dimensional information at lower dose and potentially lower cost than CT in certain clinical imaging situations. This renewed interest in tomosynthesis began in the late 1990s as a new generation of flat-panel detectors became available; these detectors were the one missing piece of the picture that had kept tomosynthesis from enjoying significant utilization earlier. In the past decade, tomosynthesis imaging has been investigated in a variety of clinical imaging situations, but the two most prominent have been in breast and chest imaging. Tomosynthesis has the potential to substantially change the way in which breast cancer and pulmonary nodules are detected and managed. Commercial tomosynthesis devices are now available or on the horizon. Many of the remaining research activities with tomosynthesis will be translational in nature and will involve physicist and clinician alike. This overview article provides a forward-looking assessment of the translational questions facing tomosynthesis imaging and anticipates some of the likely research and clinical activities in the next five years.

Stochastic noise characteristics in matrix inversion tomosynthesis (MITS).

Matrix inversion tomosynthesis (MITS) uses known imaging geometry and linear systems theory to deterministically separate in-plane detail from residual tomographic blur in a set of conventional tomosynthesis ("shift-and-add") planes. A previous investigation explored the effect of scan angle (ANG), number of projections (N), and number of reconstructed planes (NP) on the MITS impulse response and modulation transfer function characteristics, and concluded that ANG = 20 degrees, N = 71, and NP = 69 is the optimal MITS imaging technique for chest imaging on our prototype tomosynthesis system. This article examines the effect of ANG, N, and NP on the MITS exposure-normalized noise power spectra (ENNPS) and seeks to confirm that the imaging parameters selected previously by an analysis of the MITS impulse response also yield reasonable stochastic properties in MITS reconstructed planes. ENNPS curves were generated for experimentally acquired mean-subtracted projection images, conventional tomosynthesis planes, and MITS planes with varying combinations of the parameters ANG, N, and NP. Image data were collected using a prototype tomosynthesis system, with 11.4 cm acrylic placed near the image receptor to produce lung-equivalent beam hardening and scattered radiation. Ten identically acquired tomosynthesis data sets (realizations) were collected for each selected technique and used to generate ensemble mean images that were subtracted from individual image realizations prior to noise power spectra (NPS) estimation. NPS curves were normalized to account for differences in entrance exposure (as measured with an ion chamber), yielding estimates of the ENNPS for each technique. Results suggest that mid- and high-frequency noise in MITS planes is fairly equivalent in magnitude to noise in conventional tomosynthesis planes, but low-frequency noise is amplified in the most anterior and posterior reconstruction planes. Selecting the largest available number of projections (N = 71) does not incur any appreciable additive electronic noise penalty compared to using fewer projections for roughly equivalent cumulative exposure. Stochastic noise is minimized by maximizing N and NP but increases with increasing ANG. The noise trend results for NP and ANG are contrary to what would be predicted by simply considering the MITS matrix conditioning and likely result from the interplay between noise correlation and the polarity of the MITS filters. From this study, the authors conclude that the previously determined optimal MITS imaging strategy based on impulse response considerations produces somewhat suboptimal stochastic noise characteristics, but is probably still the best technique for MITS imaging of the chest.

Detector or system? Extending the concept of detective quantum efficiency to characterize the performance of digital radiographic imaging systems.

PURPOSE: To develop an experimental method for measuring the effective detective quantum efficiency (eDQE) of digital radiographic imaging systems and evaluate its use in select imaging systems. MATERIALS AND METHODS: A geometric phantom emulating the attenuation and scatter properties of the adult human thorax was employed to assess eight imaging systems in a total of nine configurations. The noise power spectrum (NPS) was derived from images of the phantom acquired at three exposure levels spanning the operating range of the system. The modulation transfer function (MTF) was measured by using an edge device positioned at the anterior surface of the phantom. Scatter measurements were made by using a beam-stop technique. All measurements, including those of phantom attenuation and estimates of x-ray flux, were used to compute the eDQE. RESULTS: The MTF results showed notable degradation owing to focal spot blur. Scatter fractions ranged between 11% and 56%, depending on the system. The eDQE(0) results ranged from 1%-17%, indicating a reduction of up to one order of magnitude and different rank ordering and performance among systems, compared with that implied in reported conventional detective quantum efficiency results from the same systems. CONCLUSION: The eDQE method was easy to implement, yielded reproducible results, and provided a meaningful reflection of system performance by quantifying image quality in a clinically relevant context. The difference in the magnitude of the measured eDQE and the ideal eDQE of 100% provides a great opportunity for improving the image quality of radiographic and mammographic systems while reducing patient dose.