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.

Validation of a mammographic image quality modification algorithm using 3D-printed breast phantoms.

Purpose: To validate a previously proposed algorithm that modifies a mammogram to appear as if it was acquired with different technique factors using realistic phantom-based mammograms. Approach: Two digital mammography systems (an indirect- and a direct-detector-based system) were used to acquire realistic mammographic images of five 3D-printed breast phantoms with the technique factors selected by the automatic exposure control and at various other conditions (denoted by the original images). Additional images under other simulated conditions were also acquired: higher or lower tube voltages, different anode/filter combinations, or lower tube current-time products (target images). The signal and noise in the original images were modified to simulate the target images (simulated images). The accuracy of the image modification algorithm was validated by comparing the target and simulated images using the local mean, local standard deviation (SD), local variance, and power spectra (PS) of the image signals. The absolute relative percent error between the target and simulated images for each parameter was calculated at each sub-region of interest (local parameters) and frequency (PS), and then averaged. Results: The local mean signal, local SD, local variance, and PS of the target and simulated images were very similar, with a relative percent error of 5.5%, 3.8%, 7.8%, and 4.4% (indirect system), respectively, and of 3.7%, 3.8%, 7.7%, and 7.5% (direct system), respectively. Conclusions: The algorithm is appropriate for simulating different technique factors. Therefore, it can be used in various studies, for instance to evaluate the impact of technique factors in cancer detection using clinical images.

Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis.

PURPOSE: To validate a candidate instrument, to be used by different professionals to assess image quality in digital mammography (DM), against detection performance results. METHODS: A receiver operating characteristics (ROC) study was conducted to assess the detection performance in DM images with four different image quality levels due to different quality issues. Fourteen expert breast radiologists from five countries assessed a set of 80 DM cases, containing 60 lesions (40 cancers, 20 benign findings) and 20 normal cases. A visual grading analysis (VGA) study using a previously-described candidate instrument was conducted to evaluate a subset of 25 of the images used in the ROC study. Eight radiologists that had participated in the ROC study, and seven expert breast-imaging physicists, evaluated this subset. The VGA score (VGAS) and the ROC and visual grading characteristics (VGC) areas under the curve (AUCROC and AUCVGC) were compared. RESULTS: No large differences in image quality among the four levels were detected by either ROC or VGA studies. However, the ranking of the four levels was consistent: level 1 (partial AUCROC: 0.070, VGAS: 6.77) performed better than levels 2 (0.066, 6.15), 3 (0.061, 5.82), and 4 (0.062, 5.37). Similarity between radiologists' and physicists' assessments was found (average VGAS difference of 10 %). CONCLUSIONS: The results from the candidate instrument were found to correlate with those from ROC analysis, when used by either observer group. Therefore, it may be used by different professionals, such as radiologists, radiographers, and physicists, to assess clinically-relevant image quality variations in DM.

How does image quality affect radiologists' perceived ability for image interpretation and lesion detection in digital mammography?

OBJECTIVES: To study how radiologists' perceived ability to interpret digital mammography (DM) images is affected by decreases in image quality. METHODS: One view from 45 DM cases (including 30 cancers) was degraded to six levels each of two acquisition-related issues (lower spatial resolution and increased quantum noise) and three post-processing-related issues (lower and higher contrast and increased correlated noise) seen during clinical evaluation of DM systems. The images were shown to fifteen breast screening radiologists from five countries. Aware of lesion location, the radiologists selected the most-degraded mammogram (indexed from 1 (reference) to 7 (most degraded)) they still felt was acceptable for interpretation. The median selected index, per degradation type, was calculated separately for calcification and soft tissue (including normal) cases. Using the two-sided, non-parametric Mann-Whitney test, the median indices for each case and degradation type were compared. RESULTS: Radiologists were not tolerant to increases (medians: 1.5 (calcifications) and 2 (soft tissue)) or decreases (median: 2, for both types) in contrast, but were more tolerant to correlated noise (median: 3, for both types). Increases in quantum noise were tolerated more for calcifications than for soft tissue cases (medians: 3 vs. 4, p = 0.02). Spatial resolution losses were considered less acceptable for calcification detection than for soft tissue cases (medians: 3.5 vs. 5, p = 0.001). CONCLUSIONS: Perceived ability of radiologists for image interpretation in DM was affected not only by image acquisition-related issues but also by image post-processing issues, and some of those issues affected calcification cases more than soft tissue cases. KEY POINTS: • Lower spatial resolution and increased quantum noise affected the radiologists' perceived ability to interpret calcification cases more than soft tissue lesion or normal cases. • Post-acquisition image processing-related effects, not only image acquisition-related effects, also impact the perceived ability of radiologists to interpret images and detect lesions. • In addition to current practices, post-acquisition image processing-related effects need to also be considered during the testing and evaluation of digital mammography systems.

Development and content validity evaluation of a candidate instrument to assess image quality in digital mammography: A mixed-method study.

PURPOSE: To develop a candidate instrument to assess image quality in digital mammography, by identifying clinically relevant features in images that are affected by lower image quality. METHODS: Interviews with fifteen expert breast radiologists from five countries were conducted and analysed by using adapted directed content analysis. During these interviews, 45 mammographic cases, containing 44 lesions (30 cancers, 14 benign findings), and 5 normal cases, were shown with varying image quality. The interviews were performed to identify the structures from breast tissue and lesions relevant for image interpretation, and to investigate how image quality affected the visibility of those structures. The interview findings were used to develop tentative items, which were evaluated in terms of wording, understandability, and ambiguity with expert breast radiologists. The relevance of the tentative items was evaluated using the content validity index (CVI) and modified kappa index (k*). RESULTS: Twelve content areas, representing the content of image quality in digital mammography, emerged from the interviews and were converted into 29 tentative items. Fourteen of these items demonstrated excellent CVI ≥ 0.78 (k* > 0.74), one showed good CVI < 0.78 (0.60 ≤ k* ≤ 0.74), while fourteen were of fair or poor CVI < 0.78 (k* ≤ 0.59). In total, nine items were deleted and five were revised or combined resulting in 18 items. CONCLUSIONS: By following a mixed-method methodology, a candidate instrument was developed that may be used to characterise the clinically-relevant impact that image quality variations can have on digital mammography.

Optimization of the difference-of-Gaussian channel sets for the channelized Hotelling observer.

The channelized-Hotelling observer (CHO) was investigated as a surrogate of human observers in task-based image quality assessment. The CHO with difference-of-Gaussian (DoG) channels has shown potential for the prediction of human detection performance in digital mammography (DM) images. However, the DoG channels employ parameters that describe the shape of each channel. The selection of these parameters influences the performance of the DoG CHO and needs further investigation. The detection performance of the DoG CHO was calculated and correlated with the detection performance of three humans who evaluated DM images in 2-alternative forced-choice experiments. A set of DM images of an anthropomorphic breast phantom with and without calcification-like signals was acquired at four different dose levels. For each dose level, 200 square regions-of-interest (ROIs) with and without signal were extracted. Signal detectability was assessed on ROI basis using the CHO with various DoG channel parameters and it was compared to that of the human observers. It was found that varying these DoG parameter values affects the correlation ( r 2 ) of the CHO with human observers for the detection task investigated. In conclusion, it appears that the the optimal DoG channel sets that maximize the prediction ability of the CHO might be dependent on the type of background and signal of ROIs investigated.

A model observer study using acquired mammographic images of an anthropomorphic breast phantom.

PURPOSE: To study the feasibility of a task-based framework composed of an anthropomorphic breast phantom and mathematical model observers (MOs) for the evaluation of system-processed mammographic images. METHODS: A prototype anthropomorphic breast phantom with inserted gold discs of 0.1 mm and 0.25 mm diameter was imaged with two digital mammography systems (system A and B) at four different dose levels. From the acquired processed and unprocessed images, signal-present and signal-absent regions of interest (ROIs) were extracted. The ROIs were evaluated by a non-pre-whitening MO with eye filter (NPWE) and by three human observers in a two-alternative forced-choice experiment. We compared the human and the MO performance on a simple detection task of the calcification-like discs in ROIs with and without postprocessing. Proportion of correct responses of the human (PCH ) and NPWE (PCNPWE ) experiments was calculated and the correlation between the two was analyzed using a mixed-effect regression model. Correlation results including the goodness of fit (r2 ) of PCH and PCNPWE for all different parameters investigated were evaluated to determine whether NPWE MO can be used to predict human observer performance. RESULTS: PCH and PCNPWE increased with dose for all conditions investigated (signal size, processing status, and different system). In case of the 0.1 mm discs, for system A, r2 between PCH with PCNPWE was 0.81. For system B, r2 was 0.93. In case of the 0.25 mm discs, r2 in system A was 0.79 and for system B, r2 was 0.82. For the combined parameters investigated, and after excluding the 0.1 mm discs on system A because the results were influenced by aliasing, the overall r2 was 0.81. Image processing did not affect the detectability of calcification-like signals. No significant difference (P > 0.05) was found between the predicted PCH(pred) by the MO and the PCH for all different conditions. CONCLUSIONS: The framework seems promising to be used in objective image quality assessment. It was found to be relatively robust for the range of parameters investigated. However, further optimization of the anthropomorphic breast phantom and investigation of other MOs for a broader range of image quality assessment tasks is needed.

New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers.

Background The image quality of digital breast tomosynthesis (DBT) volumes depends greatly on the reconstruction algorithm. Purpose To compare two DBT reconstruction algorithms used by the Siemens Mammomat Inspiration system, filtered back projection (FBP), and FBP with iterative optimizations (EMPIRE), using qualitative analysis by human readers and detection performance of machine learning algorithms. Material and Methods Visual grading analysis was performed by four readers specialized in breast imaging who scored 100 cases reconstructed with both algorithms (70 lesions). Scoring (5-point scale: 1 = poor to 5 = excellent quality) was performed on presence of noise and artifacts, visualization of skin-line and Cooper's ligaments, contrast, and image quality, and, when present, lesion visibility. In parallel, a three-dimensional deep-learning convolutional neural network (3D-CNN) was trained (n = 259 patients, 51 positives with BI-RADS 3, 4, or 5 calcifications) and tested (n = 46 patients, nine positives), separately with FBP and EMPIRE volumes, to discriminate between samples with and without calcifications. The partial area under the receiver operating characteristic curve (pAUC) of each 3D-CNN was used for comparison. Results EMPIRE reconstructions showed better contrast (3.23 vs. 3.10, P = 0.010), image quality (3.22 vs. 3.03, P < 0.001), visibility of calcifications (3.53 vs. 3.37, P = 0.053, significant for one reader), and fewer artifacts (3.26 vs. 2.97, P < 0.001). The 3D-CNN-EMPIRE had better performance than 3D-CNN-FBP (pAUC-EMPIRE = 0.880 vs. pAUC-FBP = 0.857; P < 0.001). Conclusion The new algorithm provides DBT volumes with better contrast and image quality, fewer artifacts, and improved visibility of calcifications for human observers, as well as improved detection performance with deep-learning algorithms.

Signal template generation from acquired images for model observer-based image quality analysis in mammography.

Mammography images undergo vendor-specific processing, which may be nonlinear, before radiologist interpretation. Therefore, to test the entire imaging chain, the effect of image processing should be included in the assessment of image quality, which is not current practice. For this purpose, model observers (MOs), in combination with anthropomorphic breast phantoms, are proposed to evaluate image quality in mammography. In this study, the nonprewhitening MO with eye filter and the channelized Hotelling observer were investigated. The goal of this study was to optimize the efficiency of the procedure to obtain the expected signal template from acquired images for the detection of a 0.25-mm diameter disk. Two approaches were followed: using acquired images with homogeneous backgrounds (approach 1) and images from an anthropomorphic breast phantom (approach 2). For quality control purposes, a straightforward procedure using a single exposure of a single disk was found adequate for both approaches. However, only approach 2 can yield templates from processed images since, due to its nonlinearity, image postprocessing cannot be evaluated using images of homogeneous phantoms. Based on the results of the current study, a phantom should be designed, which can be used for the objective assessment of image quality.

Toward image quality assessment in mammography using model observers: Detection of a calcification-like object.

PURPOSE: Model observers (MOs) are of interest in the field of medical imaging to assess image quality. However, before procedures using MOs can be proposed in quality control guidelines for mammography systems, we need to know whether MOs are sensitive to changes in image quality and correlations in background structure. Therefore, as a proof of principle, in this study human and model observer (MO) performance are compared for the detection of calcification-like objects using different background structures and image quality levels of unprocessed mammography images. METHOD: Three different phantoms, homogeneous polymethyl methacrylate, BR3D slabs with swirled patterns (CIRS, Norfolk, VA, USA), and a prototype anthropomorphic breast phantom (Institute of Medical Physics and Radiation Protection, Technische Hochschule Mittelhessen, Germany) were imaged on an Amulet Innovality (FujiFilm, Tokyo, Japan) mammographic X-ray unit. Because the complexities of the structures of these three phantoms were different and not optimized to match the characteristics of real mammographic images, image processing was not applied in this study. In addition, real mammograms were acquired on the same system. Regions of interest (ROIs) were extracted from each image. In half of the ROIs, a 0.25-mm diameter disk was inserted at four different contrast levels to represent a calcification-like object. Each ROI was then modified, so four image qualities relevant for mammography were simulated. The signal-present and signal-absent ROIs were evaluated by a non-pre-whitening model observer with eye filter (NPWE) and a channelized Hotelling observer (CHO) using dense difference of Gaussian channels. The ROIs were also evaluated by human observers in a two alternative forced choice experiment. Detectability results for the human and model observer experiments were correlated using a mixed-effect regression model. Threshold disk contrasts for human and predicted human observer performance based on the NPWE MO and CHO were estimated. RESULTS: Global trends in threshold contrast were similar for the different background structures, but absolute contrast threshold levels differed. Contrast thresholds tended to be lower in ROIs from simple phantoms compared with ROIs from real mammographic images. The correlation between human and model observer performance was not affected by the range of image quality levels studied. CONCLUSIONS: The correlation between human and model observer performance does not depend on image quality. This is a promising outcome for the use of model observers in image quality analysis and allows for subsequent research toward the development of MO-based quality control procedures and guidelines.

A Monte Carlo model for mean glandular dose evaluation in spot compression mammography.

PURPOSE: To characterize the dependence of normalized glandular dose (DgN) on various breast model and image acquisition parameters during spot compression mammography and other partial breast irradiation conditions, and evaluate alternative previously proposed dose-related metrics for this breast imaging modality. METHODS: Using Monte Carlo simulations with both simple homogeneous breast models and patient-specific breasts, three different dose-related metrics for spot compression mammography were compared: the standard DgN, the normalized glandular dose to only the directly irradiated portion of the breast (DgNv), and the DgN obtained by the product of the DgN for full field irradiation and the ratio of the mid-height area of the irradiated breast to the entire breast area (DgNM ). How these metrics vary with field-of-view size, spot area thickness, x-ray energy, spot area and position, breast shape and size, and system geometry was characterized for the simple breast model and a comparison of the simple model results to those with patient-specific breasts was also performed. RESULTS: The DgN in spot compression mammography can vary considerably with breast area. However, the difference in breast thickness between the spot compressed area and the uncompressed area does not introduce a variation in DgN. As long as the spot compressed area is completely within the breast area and only the compressed breast portion is directly irradiated, its position and size does not introduce a variation in DgN for the homogeneous breast model. As expected, DgN is lower than DgNv for all partial breast irradiation areas, especially when considering spot compression areas within the clinically used range. DgNM underestimates DgN by 6.7% for a W/Rh spectrum at 28 kVp and for a 9 × 9 cm2 compression paddle. CONCLUSION: As part of the development of a new breast dosimetry model, a task undertaken by the American Association of Physicists in Medicine and the European Federation of Organizations of Medical Physics, these results provide insight on how DgN and two alternative dose metrics behave with various image acquisition and model parameters.

Comparison of a flexible versus a rigid breast compression paddle: pain experience, projected breast area, radiation dose and technical image quality.

PURPOSE: To compare pain, projected breast area, radiation dose and image quality between flexible (FP) and rigid (RP) breast compression paddles. METHODS: The study was conducted in a Dutch mammographic screening unit (288 women). To compare both paddles one additional image with RP was made, consisting of either a mediolateral-oblique (MLO) or craniocaudal-view (CC). Pain experience was scored using the Numeric Rating Scale (NRS). Projected breast area was estimated using computer software. Radiation dose was estimated using the model by Dance. Image quality was reviewed by three radiologists and three radiographers. RESULTS: There was no difference in pain experience between both paddles (mean difference NRS: 0.08 ± 0.08, p = 0.32). Mean radiation dose was 4.5 % lower with FP (0.09 ± 0.01 p = 0.00). On MLO-images, the projected breast area was 0.79 % larger with FP. Paired evaluation of image quality indicated that FP removed fibroglandular tissue from the image area and reduced contrast in the clinically relevant retroglandular area at chest wall side. CONCLUSIONS: Although FP performed slightly better in the projected breast area, it moved breast tissue from the image area at chest wall side. RP showed better contrast, especially in the retroglandular area. We therefore recommend the use of RP for standard MLO and CC views.

Consequences of digital mammography in population-based breast cancer screening: initial changes and long-term impact on referral rates.

OBJECTIVES: To investigate the referral pattern after the transition to full-field digital mammography (FFDM) in a population-based breast cancer screening programme. METHODS: Preceding the nationwide digitalisation of the Dutch screening programme, an FFDM feasibility study was conducted. Detection and referral rates for FFDM and screen-film mammography (SFM) were compared for first and subsequent screens. Furthermore, radiological characteristics of referrals in digital screening were assessed. RESULTS: A total of 312,414 screening mammograms were performed (43,913 digital and 268,501 conventional), with 4,473 consecutive referrals (966 following FFDM). Initially the FFDM referral rate peaked, and many false-positive results were noted as a consequence of pseudolesions and increased detection of (benign) microcalcifications. A higher overall referral rate was observed in FFDM screening in both first and subsequent examinations (p < .001), with a significant increase in cancer detection (p = .010). CONCLUSION: As a result of initial inexperience with digital screening images implementing FFDM in a population-based breast cancer screening programme may lead to a strong, but temporary increase in referral. Dedicated training in digital screening for radiographers and screening radiologists is therefore recommended. Referral rates decrease and stabilise (learning curve effect) at a higher level than in conventional screening, yet with significantly enhanced cancer detection.