Implementation of a high-resolution, high-contrast magnetic resonance imaging protocol with extended delayed phases for peritoneal mesothelioma.

Background: Imaging of peritoneal malignancies using conventional cross-sectional imaging is challenging, but accurate assessment of peritoneal disease burden could guide better selection for definitive surgery. Here we demonstrate feasibility of high-resolution, high-contrast magnetic resonance imaging (MRI) of peritoneal mesothelioma and explore optimal timing for delayed post-contrast imaging. Methods: Prospective data from inpatients with malignant peritoneal mesothelioma (MPM), imaged with a novel MRI protocol, were analyzed. The new sequences augmenting the clinical protocol were (I) pre-contrast coronal high-resolution T2-weighted single-shot fast spin echo (COR hr T2w SSH FSE) of abdomen and pelvis; and (II) post-contrast coronal high-resolution three-dimensional (3D) T1-weighted modified Dixon (COR hr T1w mDIXON) of abdomen, acquired at five delay times, up to 20 min after administration of a double dose of contrast agent. Quantitative analysis of contrast enhancement was performed using linear regression applied to normalized signal in lesion regions of interest (ROIs). Qualitative analysis was performed by three blinded radiologists. Results: MRI exams from 14 participants (age: mean ± standard deviation, 60±12 years; 71% male) were analyzed. The rate of lesion contrast enhancement was strongly correlated with tumor grade (cumulative nuclear score) (r=-0.65, P<0.02), with 'early' delayed phase (12 min post-contrast) and 'late' delayed phase (19 min post-contrast) performing better for higher grade and lower grade tumors, respectively, in agreement with qualitative scoring of image contrast. Conclusions: High-resolution, high-contrast MRI with extended post-contrast imaging is a viable modality for imaging peritoneal mesothelioma. Multiple, extended (up to 20 min post-contrast) delayed phases are necessary for optimal imaging of peritoneal mesothelioma, depending on the grade of disease.

Lesion detection in digital breast tomosynthesis: human reader experiments indicate no benefit from the integration of information from multiple planes.

Purpose: In digital breast tomosynthesis (DBT), radiologists need to review a stack of 20 to 80 tomosynthesis images, depending upon breast size. This causes a significant increase in reading time. However, it is currently unknown whether there is a perceptual benefit to viewing a mass in the 3D tomosynthesis volume. To answer this question, this study investigated whether adjacent lesion-containing planes provide additional information that aids lesion detection for DBT-like and breast CT-like (bCT) images. Method: Human reader detection performance was determined for low-contrast targets shown in a single tomosynthesis image at the center of the target (2D) or shown in the entire tomosynthesis image stack (3D). Using simulations, targets embedded in simulated breast backgrounds, and images were generated using a DBT-like (50 deg angular range) and a bCT-like (180 deg angular range) imaging geometry. Experiments were conducted with spherical and capsule-shaped targets. Eleven readers reviewed 1600 images in two-alternative forced-choice experiments. The area under the receiver operating characteristic curve (AUC) and reading time were computed for the 2D and 3D reading modes for the DBT and bCT imaging geometries and for both target shapes. Results: Spherical lesion detection was higher in 2D mode than in 3D, for both DBT- and bCT-like images (DBT: AUC2D=0.790, AUC3D=0.735, P=0.03; bCT: AUC2D=0.869, AUC3D=0.716, P<0.05), but equivalent for capsule-shaped signals (DBT: AUC2D=0.891, AUC3D=0.915, P=0.19; bCT: AUC2D=0.854, AUC3D=0.847, P=0.88). Average reading time was up to 134% higher for 3D viewing (P<0.05). Conclusions: For the detection of low-contrast lesions, there is no inherent visual perception benefit to reviewing the entire DBT or bCT stack. The findings of this study could have implications for the development of 2D synthetic mammograms: a single synthesized 2D image designed to include all lesions present in the volume might allow readers to maintain detection performance at a significantly reduced reading time.

A Clinically Optimal Protocol for the Imaging of Enteric Tubes: On the Basis of Radiologist Interpreted Diagnostic Utility and Radiation Dose Reduction.

RATIONALE AND OBJECTIVES: The purpose of this study was to develop and evaluate a patient thickness-based protocol specifically for the confirmation of enteric tube placements in bedside abdominal radiographs. Protocol techniques were set to maintain image quality while minimizing patient dose. MATERIALS AND METHODS: A total of 226 pre-intervention radiographs were obtained to serve as a baseline cohort for comparison. After the implementation of a thickness-based protocol, a total of 229 radiographs were obtained as part of an intervention cohort. Radiographs were randomized and graded for diagnostic quality by seven expert radiologists based on a standardized conspicuity scale (grades: 0 non-diagnostic to 3+). Basic patient demographics, body mass index, ventilatory status, and enteric tube type were recorded and subgroup analyses were performed. Effective dose was estimated for both cohorts. RESULTS: The dedicated thickness-based protocol resulted in a significant reduction in effective dose of 80% (p-value < 0.01). There was no significant difference in diagnostic quality between the two cohorts with 209 (92.5%) diagnostic radiographs in the baseline and 221 (96.5%) diagnostic radiographs in the thickness-based protocol (p-value 0.06). CONCLUSION: A protocol optimized for the confirmation of enteric tube placements was developed. This protocol results in lower patient effective dose, without sacrificing diagnostic accuracy. The technique chart is provided for reference. The protocol development process outlined in this work could be readily generalized to other imaging clinical tasks.

High-Resolution Full-3D Specimen Imaging for Lumpectomy Margin Assessment in Breast Cancer.

BACKGROUND: Two-dimensional (2D) specimen radiography (SR) and tomosynthesis (DBT) for breast cancer yield data that lack high-depth resolution. A volumetric specimen imager (VSI) was developed to provide full-3D and thin-slice cross-sectional visualization at a 360° view angle. The purpose of this prospective trial was to compare VSI, 2D SR, and DBT interpretation of lumpectomy margin status with the final pathologic margin status of breast lumpectomy specimens. METHODS: The study enrolled 200 cases from two institutions. After standard imaging and interpretation was performed, the main lumpectomy specimen was imaged with the VSI device. Image interpretation was performed by three radiologists after surgery based on VSI, 2D SR, and DBT. A receiver operating characteristic (ROC) curve was created for each method. The area under the curve (AUC) was computed to characterize the performance of the imaging method interpreted by each user. RESULTS: From 200 lesions, 1200 margins were interpreted. The AUC values of VSI for the three radiologists were respectively 0.91, 0.90, and 0.94, showing relative improvement over the AUCs of 2D SR by 54%, 13%, and 40% and DBT by 32% and 11%, respectively. The VSI has sensitivity ranging from 91 to 94%, specificity ranging from 81 to 85%, a positive predictive value ranging from 25 to 30%, and a negative predicative value of 99%. CONCLUSIONS: The ROC curves of the VSI were higher than those of the other specimen imaging methods. Full-3D specimen imaging can improve the correlation between the main lumpectomy specimen margin status and surgical pathology. The findings from this study suggest that using the VSI device for intraoperative margin assessment could further reduce the re-excision rates for women with malignant disease.

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation.

The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density <±3%; dose <±20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.

Iodine quantification in limited angle tomography.

PURPOSE: To develop and test the feasibility of a two-pass iterative reconstruction algorithm with material decomposition designed to obtain quantitative iodine measurements in digital breast tomosynthesis. METHODS: Contrast-enhanced mammography has shown promise as a cost-effective alternative to magnetic resonance imaging for imaging breast cancer, especially in dense breasts. However, one limitation is the poor quantification of iodine contrast since the true three-dimensional lesion shape cannot be inferred from the two-dimensional (2D) projection. Use of limited angle tomography can potentially overcome this limitation by segmenting the iodine map generated by the first-pass reconstruction using a convolutional neural network, and using this segmentation to restrict the iodine distribution in the second pass of the reconstruction. To evaluate the performance of the algorithms, a set of 2D digital breast phantoms containing targets with varying iodine concentration was used. In each breast phantom, a single simulated lesion with a random size (4 to 8 mm) was placed in a random location within each phantom, with the iodine distribution defined as either homogeneous or rim-enhanced and blood iodine concentration set between 1.4 and 5.6 mg/mL. Limited angle projection data of these phantoms were simulated for wide and narrow angle geometries, and the proposed reconstruction and segmentation algorithms were applied. RESULTS: The median Dice similarity coefficient of the segmented masks was 0.975 for the wide angle data and 0.926 for the narrow angle data. Using these segmentations during the second reconstruction pass resulted in an improvement in the concentration estimates (mean estimated-to-true concentration ratio, before and after second pass: 48% to 73% for wide angle; 30% to 73% for narrow angle), and a reduction in the coefficient of variation of the estimates (55% to 27% for wide angle; 54% to 35% for narrow angle). CONCLUSION: We demonstrate that the proposed two-pass reconstruction can potentially improve accuracy and precision of iodine quantification in contrast-enhanced tomosynthesis.

Imaging of fiber-like structures in digital breast tomosynthesis.

Fiber-like features are an important aspect of breast imaging. Vessels and ducts are present in all breast images, and spiculations radiating from a mass can indicate malignancy. Accordingly, fiber objects are one of the three types of signals used in the American College of Radiology digital mammography (ACR-DM) accreditation phantom. Our work focuses on the image properties of fiber-like structures in digital breast tomosynthesis (DBT) and how image reconstruction can affect their appearance. The impact of DBT image reconstruction algorithm and regularization strength on the conspicuity of fiber-like signals of various orientations is investigated in simulation. A metric is developed to characterize this orientation dependence and allow for quantitative comparison of algorithms and associated parameters in the context of imaging fiber signals. The imaging properties of fibers, characterized in simulation, are then demonstrated in detail with physical DBT data of the ACR-DM phantom. The characterization of imaging of fiber signals is used to explain features of an actual clinical DBT case. For the algorithms investigated, at low regularization setting, the results show a striking variation in conspicuity as a function of orientation in the viewing plane. In particular, the conspicuity of fibers nearly aligned with the plane of the x-ray source trajectory is decreased relative to more obliquely oriented fibers. Increasing regularization strength mitigates this orientation dependence at the cost of increasing depth blur of these structures.

Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification.

PURPOSE: Many computer aided diagnosis (CADx) tools for breast cancer begin by fully or semiautomatically segmenting a given breast lesion and then classifying the lesion's likelihood of malignancy using quantitative features extracted from the image. It is often assumed that better segmentation will result in better classification. However, this has not been thoroughly evaluated. The purpose of this study is to evaluate the relationship between computer segmentation performance and computer classification performance. METHOD: We used 85 breast lesions (32 benign, 56 malignant) from breast computed tomography (CT) cases of 82 women. We prepared one smooth and one sharp iterative image reconstructions (IIR) and a clinical reconstruction for each of the 82 breast CT scans. For each reconstruction, we created 15 segmentation outcomes by applying 15 different segmentation algorithms. Specifically, we simulated 15 segmentation algorithms by changing parameters in a single segmentation algorithm. We then created 15 classification outcomes by conducting quantitative image feature analysis on the segmented image results. Using a 10-fold cross-validation, we evaluated the relationship between segmentation and classification performances. RESULT: We found a low positive correlation between segmentation and classification performances for the smooth IIR (median Pearson's rho = 0.18), while a moderate positive correlation (median Pearson's rho = 0.4-0.43) was found between the two performances for the sharp IIR and clinical reconstruction. However, we found large variations in both segmentation and classification performances for the sharp IIR and clinical reconstruction. There were cases where segmentation algorithms resulted in similar segmentation performances, but the corresponding classification performances were different. These results indicate that an improvement in segmentation performance does not guarantee an improvement in the corresponding classification performance. CONCLUSION: Computer segmentation is an indirect variable affecting the computer classification. As better segmentation does not guarantee better classification, we should report both segmentation and classification performances when comparing segmentation algorithms.

Neutrosophic segmentation of breast lesions for dedicated breast computed tomography.

We proposed the neutrosophic approach for segmenting breast lesions in breast computed tomography (bCT) images. The neutrosophic set considers the nature and properties of neutrality (or indeterminacy). We considered the image noise as an indeterminate component while treating the breast lesion and other breast areas as true and false components. We iteratively smoothed and contrast-enhanced the image to reduce the noise level of the true set. We then applied one existing algorithm for bCT images, the RGI segmentation, on the resulting noise-reduced image to segment the breast lesions. We compared the segmentation performance of the proposed method (named as NS-RGI) to that of the regular RGI segmentation. We used 122 breast lesions (44 benign and 78 malignant) of 111 noncontrast enhanced bCT cases. We measured the segmentation performances of the NS-RGI and the RGI using the Dice coefficient. The average Dice values of the NS-RGI and RGI were 0.82 and 0.80, respectively, and their difference was statistically significant ([Formula: see text]). We conducted a subsequent feature analysis on the resulting segmentations. The classifier performance for the NS-RGI ([Formula: see text]) improved over that of the RGI ([Formula: see text], [Formula: see text]).

Analysis of Volume Overestimation Artifacts in the Breast Outline Segmentation in Tomosynthesis.

In digital breast tomosynthesis (DBT), the reconstruction is calculated from x-ray projection images acquired over a small range of angles. One step in the reconstruction process is to identify the pixels that fall outside the shadow of the breast, to segment the breast from the background (air). In each projection, rays are back-projected from these pixels to the focal spot. All voxels along these rays are identified as air. By combining these results over all projections, a breast outline can be determined for the reconstruction. This paper quantifies the accuracy of this breast segmentation strategy in DBT. In this study, a physical phantom modeling a breast under compression was analyzed with a prototype next-generation tomosynthesis (NGT) system described in previous work. Multiple wires were wrapped around the phantom. Since the wires are thin and high contrast, their exact location can be determined from the reconstruction. Breast parenchyma was portrayed outside the outline defined by the wires. Specifically, the size of the phantom was overestimated along the posteroanterior (PA) direction; i.e., perpendicular to the plane of conventional source motion. To analyze how the acquisition geometry affects the accuracy of the breast outline segmentation, a computational phantom was also simulated. The simulation identified two ways to improve the segmentation accuracy; either by increasing the angular range of source motion laterally or by increasing the range in the PA direction. The latter approach is a unique feature of the NGT design; the advantage of this approach was validated with our prototype system.

Lack of agreement between radiologists: implications for image-based model observers.

We tested the agreement of radiologists' rankings of different reconstructions of breast computed tomography images based on their diagnostic (classification) performance and on their subjective image quality assessments. We used 102 pathology proven cases (62 malignant, 40 benign), and an iterative image reconstruction (IIR) algorithm to obtain 24 reconstructions per case with different image appearances. Using image feature analysis, we selected 3 IIRs and 1 clinical reconstruction and 50 lesions. The reconstructions produced a range of image quality from smooth/low-noise to sharp/high-noise, which had a range in classifier performance corresponding to AUCs of 0.62 to 0.96. Six experienced Mammography Quality Standards Act (MQSA) radiologists rated the likelihood of malignancy for each lesion. We conducted an additional reader study with the same radiologists and a subset of 30 lesions. Radiologists ranked each reconstruction according to their preference. There was disagreement among the six radiologists on which reconstruction produced images with the highest diagnostic content, but they preferred the midsharp/noise image appearance over the others. However, the reconstruction they preferred most did not match with their performance. Due to these disagreements, it may be difficult to develop a single image-based model observer that is representative of a population of radiologists for this particular imaging task.

Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT.

PURPOSE: The purpose of this study is to determine the optimal representative reconstruction and quantitative image feature set for a computer-aided diagnosis (CADx) scheme for dedicated breast computer tomography (bCT). METHOD: We used 93 bCT scans that contain 102 breast lesions (62 malignant, 40 benign). Using an iterative image reconstruction (IIR) algorithm, we created 37 reconstructions with different image appearances for each case. In addition, we added a clinical reconstruction for comparison purposes. We used image sharpness, determined by the gradient of gray value in a parenchymal portion of the reconstructed breast, as a surrogate measure of the image qualities/appearances for the 38 reconstructions. After segmentation of the breast lesion, we extracted 23 quantitative image features. Using leave-one-out-cross-validation (LOOCV), we conducted the feature selection, classifier training, and testing. For this study, we used the linear discriminant analysis classifier. Then, we selected the representative reconstruction and feature set for the classifier with the best diagnostic performance among all reconstructions and feature sets. Then, we conducted an observer study with six radiologists using a subset of breast lesions (N = 50). Using 1000 bootstrap samples, we compared the diagnostic performance of the trained classifier to those of the radiologists. RESULT: The diagnostic performance of the trained classifier increased as the image sharpness of a given reconstruction increased. Among combinations of reconstructions and quantitative image feature sets, we selected one of the sharp reconstructions and three quantitative image feature sets with the first three highest diagnostic performances under LOOCV as the representative reconstruction and feature set for the classifier. The classifier on the representative reconstruction and feature set achieved better diagnostic performance with an area under the ROC curve (AUC) of 0.94 (95% CI = [0.81, 0.98]) than those of the radiologists, where their maximum AUC was 0.78 (95% CI = [0.63, 0.90]). Moreover, the partial AUC, at 90% sensitivity or higher, of the classifier (pAUC = 0.085 with 95% CI = [0.063, 0.094]) was statistically better (P-value < 0.0001) than those of the radiologists (maximum pAUC = 0.009 with 95% CI = [0.003, 0.024]). CONCLUSION: We found that image sharpness measure can be a good candidate to estimate the diagnostic performance of a given CADx algorithm. In addition, we found that there exists a reconstruction (i.e., sharp reconstruction) and a feature set that maximizes the diagnostic performance of a CADx algorithm. On this optimal representative reconstruction and feature set, the CADx algorithm outperformed radiologists.

Unified Database for Rejected Image Analysis Across Multiple Vendors in Radiography.

Reject rate analysis has been part of radiography departments' quality control since the days of screen-film radiography. In the era of digital radiography, one might expect that reject rate analysis is easily facilitated because of readily available information produced by the modality during the examination procedure. Unfortunately, this is not always the case. The lack of an industry standard and the wide variety of system log entries and formats have made it difficult to implement a robust multivendor reject analysis program, and logs do not always include all relevant information. The increased use of digital detectors exacerbates this problem because of higher reject rates associated with digital radiography compared with computed radiography. In this article, the authors report on the development of a unified database for vendor-neutral reject analysis across multiple sites within an academic institution and share their experience from a team-based approach to reduce reject rates.

Effect of comfort pads and incubator design on neonatal radiography.

BACKGROUND: There has been increasing interest in patient dose reduction in neonatal intensive care units. Removing comfort pads for radiography has been identified as a potential means to decrease patient dose. OBJECTIVE: To assess the effect of comfort pads and support trays on detector entrance exposure (DEE) and image quality for neonatal radiography, and its implication for patient dose. MATERIALS AND METHODS: Comfort pads and support trays from three incubator and warmer systems were examined. The attenuation of the primary beam by these structures was measured using a narrow beam geometry. Their effect on DEE and image quality was then assessed using typical neonatal chest radiography techniques with three configurations: 1) both the comfort pad and support included in the beam, 2) only the support tray included and 3) both the comfort pad and support tray removed. RESULTS: Comfort pads and support trays were found to attenuate the primary beam by 6-15%. Eliminating these structures from the X-ray beam's path was found to increase the detector entrance exposure by 28-36% and increase contrast-to-noise ratio by more than 21%, suggesting room for patient dose reduction when the same image quality is maintained. CONCLUSION: Comfort pads and tray support devices can have a considerable effect on DEE and image quality, with large variations among different incubator designs. Positioning the image detector directly underneath neonatal patients for radiography is a potential means for patient dose reduction. However, such benefit should be weighed against the risks of moving the patient.

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT.

PURPOSE: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. METHODS: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. RESULTS: Among curvature measures, the normalized total curvature (CT) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and CT yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and CT were conducted. The results showed that CT was able to replace the other four image features for the classification task. CONCLUSIONS: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets.

Level set segmentation of breast masses in contrast-enhanced dedicated breast CT and evaluation of stopping criteria.

Dedicated breast CT (bCT) produces high-resolution 3D tomographic images of the breast, fully resolving fibroglandular tissue structures within the breast and allowing for breast lesion detection and assessment in 3D. In order to enable quantitative analysis, such as volumetrics, automated lesion segmentation on bCT is highly desirable. In addition, accurate output from CAD (computer-aided detection/diagnosis) methods depends on sufficient segmentation of lesions. Thus, in this study, we present a 3D lesion segmentation method for breast masses in contrast-enhanced bCT images. The segmentation algorithm follows a two-step approach. First, 3D radial-gradient index segmentation is used to obtain a crude initial contour, which is then refined by a 3D level set-based active contour algorithm. The data set included contrast-enhanced bCT images from 33 patients containing 38 masses (25 malignant, 13 benign). The mass centers served as input to the algorithm. In this study, three criteria for stopping the contour evolution were compared, based on (1) the change of region volume, (2) the average intensity in the segmented region increase at each iteration, and (3) the rate of change of the average intensity inside and outside the segmented region. Lesion segmentation was evaluated by computing the overlap ratio between computer segmentations and manually drawn lesion outlines. For each lesion, the overlap ratio was averaged across coronal, sagittal, and axial planes. The average overlap ratios for the three stopping criteria ranged from 0.66 to 0.68 (dice coefficient of 0.80 to 0.81), indicating that the proposed segmentation procedure is promising for use in quantitative dedicated bCT analyses.

Impact of lesion segmentation metrics on computer-aided diagnosis/detection in breast computed tomography.

Evaluation of segmentation algorithms usually involves comparisons of segmentations to gold-standard delineations without regard to the ultimate medical decision-making task. We compare two segmentation evaluations methods-a Dice similarity coefficient (DSC) evaluation and a diagnostic classification task-based evaluation method using lesions from breast computed tomography. In our investigation, we use results from two previously developed lesion-segmentation algorithms [a global active contour model (GAC) and a global with local aspects active contour model]. Although similar DSC values were obtained (0.80 versus 0.77), we show that the global + local active contour (GLAC) model, as compared with the GAC model, is able to yield significantly improved classification performance in terms of area under the receivers operating characteristic (ROC) curve in the task of distinguishing malignant from benign lesions. [Area under the [Formula: see text] compared to 0.63, [Formula: see text]]. This is mainly because the GLAC model yields better detailed information required in the calculation of morphological features. Based on our findings, we conclude that the DSC metric alone is not sufficient for evaluating segmentation lesions in computer-aided diagnosis tasks.

A statistically defined anthropomorphic software breast phantom.

PURPOSE: Digital anthropomorphic breast phantoms have emerged in the past decade because of recent advances in 3D breast x-ray imaging techniques. Computer phantoms in the literature have incorporated power-law noise to represent glandular tissue and branching structures to represent linear components such as ducts. When power-law noise is added to those phantoms in one piece, the simulated fibroglandular tissue is distributed randomly throughout the breast, resulting in dense tissue placement that may not be observed in a real breast. The authors describe a method for enhancing an existing digital anthropomorphic breast phantom by adding binarized power-law noise to a limited area of the breast. METHODS: Phantoms with (0.5 mm)(3) voxel size were generated using software developed by Bakic et al. Between 0% and 40% of adipose compartments in each phantom were replaced with binarized power-law noise (ß = 3.0) ranging from 0.1 to 0.6 volumetric glandular fraction. The phantoms were compressed to 7.5 cm thickness, then blurred using a 3 × 3 boxcar kernel and up-sampled to (0.1 mm)(3) voxel size using trilinear interpolation. Following interpolation, the phantoms were adjusted for volumetric glandular fraction using global thresholding. Monoenergetic phantom projections were created, including quantum noise and simulated detector blur. Texture was quantified in the simulated projections using power-spectrum analysis to estimate the power-law exponent ß from 25.6 × 25.6 mm(2) regions of interest. RESULTS: Phantoms were generated with total volumetric glandular fraction ranging from 3% to 24%. Values for ß (averaged per projection view) were found to be between 2.67 and 3.73. Thus, the range of textures of the simulated breasts covers the textures observed in clinical images. CONCLUSIONS: Using these new techniques, digital anthropomorphic breast phantoms can be generated with a variety of glandular fractions and patterns. ß values for this new phantom are comparable with published values for breast tissue in x-ray projection modalities. The combination of conspicuous linear structures and binarized power-law noise added to a limited area of the phantom qualitatively improves its realism.

Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms.

PURPOSE: The authors develop a practical, iterative algorithm for image-reconstruction in undersampled tomographic systems, such as digital breast tomosynthesis (DBT). METHODS: The algorithm controls image regularity by minimizing the image total p variation (TpV), a function that reduces to the total variation when p = 1.0 or the image roughness when p = 2.0. Constraints on the image, such as image positivity and estimated projection-data tolerance, are enforced by projection onto convex sets. The fact that the tomographic system is undersampled translates to the mathematical property that many widely varied resultant volumes may correspond to a given data tolerance. Thus the application of image regularity serves two purposes: (1) Reduction in the number of resultant volumes out of those allowed by fixing the data tolerance, finding the minimum image TpV for fixed data tolerance, and (2) traditional regularization, sacrificing data fidelity for higher image regularity. The present algorithm allows for this dual role of image regularity in undersampled tomography. RESULTS: The proposed image-reconstruction algorithm is applied to three clinical DBT data sets. The DBT cases include one with microcalcifications and two with masses. CONCLUSIONS: Results indicate that there may be a substantial advantage in using the present image-reconstruction algorithm for microcalcification imaging.

Comparison of power spectra for tomosynthesis projections and reconstructed images.

Burgess et al. [Med. Phys. 28, 419-437 (2001)] showed that the power spectrum of mammographic breast background follows a power law and that lesion detectability is affected by the power-law exponent beta which measures the amount of structure in the background. Following the study of Burgess et al., the authors measured and compared the power-law exponent of mammographic backgrounds in tomosynthesis projections and reconstructed slices to investigate the effect of tomosynthesis imaging on background structure. Our data set consisted of 55 patient cases. For each case, regions of interest (ROIs) were extracted from both projection images and reconstructed slices. The periodogram of each ROI was computed by taking the squared modulus of the Fourier transform of the ROI. The power-law exponent was determined for each periodogram and averaged across all ROIs extracted from all projections or reconstructed slices for each patient data set. For the projections, the mean beta averaged across the 55 cases was 3.06 (standard deviation of 0.21), while it was 2.87 (0.24) for the corresponding reconstructions. The difference in beta for a given patient between the projection ROIs and the reconstructed ROIs averaged across the 55 cases was 0.194, which was statistically significant (p < 0.001). The 95% CI for the difference between the mean value of beta for the projections and reconstructions was [0.170, 0.218]. The results are consistent with the observation that the amount of breast structure in the tomosynthesis slice is reduced compared to projection mammography and that this may lead to improved lesion detectability.