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.

Technical performance of a dual-energy CT system with a novel deep-learning based reconstruction process: Evaluation using an abdomen protocol.

BACKGROUND: A new tube voltage-switching dual-energy (DE) CT system using a novel deep-learning based reconstruction process has been introduced. Characterizing the performance of this DE approach can help demonstrate its benefits and potential drawbacks. PURPOSE: To evaluate the technical performance of a novel DECT system and compare it to that of standard single-kV CT and a rotate/rotate DECT, for abdominal imaging. METHODS: DE and single-kV images of four different phantoms were acquired on a kV-switching DECT system, and on a rotate/rotate DECT. The dose for the acquisitions of each phantom was set to that selected for the kV-switching DE mode by the automatic tube current modulation (ATCM) at manufacturer-recommended settings. The dose that the ATCM would have selected in single-kV mode was also recorded. Virtual monochromatic images (VMIs) from 40 to 130 keV, as well as iodine maps, were reconstructed from the DE data. Single-kV images, acquired at 120 kV, were reconstructed using body hybrid iterative reconstruction. All reconstructions were made at 0.5 mm section thickness. Task transfer functions (TTFs) were determined for a Teflon and LDPE rod. Noise magnitude (SD), and noise power spectrum (NPS) were calculated using 240 and 320 mm diameter water phantoms. Iodine quantification accuracy and contrast-to-noise ratios (CNRs) relative to water for 2, 5, 10, and 15 mg I/ml were determined using a multi-energy CT (MECT) phantom. Low-contrast visibility was determined and the presence of beam-hardening artifacts and inhomogeneities were evaluated. RESULTS: The TTFs of the kV-switching DE VMIs were higher than that of the single-kV images for Teflon (20% TTF: 6.8 lp/cm at 40 keV, 6.2 lp/cm for single-kV), while for LDPE the DE TTFs at 70 keV and above were equivalent or higher than the single-kV TTF. All TTFs of the kV-switching DECT were higher than for the rotate/rotate DECT. The SD was lowest in the 70 keV VMI (12.0 HU), which was lower than that of single-kV (18.3 HU). The average NPS frequency varied between 2.3 lp/cm and 4.2 lp/cm for the kV-switching VMIs and was 2.2 lp/cm for single-kV. The error in iodine quantification was at maximum 1 mg I/ml (at 5 mg I/ml). The highest CNR for all iodine concentrations was at 60 keV, 2.5 times higher than the CNR for single-kV. At 70-90 keV, the number of visible low contrast objects was comparable to that in single-kV, while other VMIs showed fewer objects. At manufacturer-recommended ATCM settings, the CTDIvol for the DE acquisitions of the water and MECT phantoms were 12.6 and 15.4 mGy, respectively, and higher than that for single-kV. The 70 keV VMI had less severe beam hardening artifacts than single-kV images. Hyper- and hypo-dense blotches may appear in VMIs when object attenuation exceeds manufacturer recommended limits. CONCLUSIONS: At manufacturer-recommended ATCM settings for abdominal imaging, this DE implementation results in higher CTDIvol compared to single-kV acquisitions. However, it can create sharper, lower noise VMIs with up to 2.5 times higher iodine CNR compared to single-kV images acquired at the same dose.

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.

Inter-laboratory comparison of channelized hotelling observer computation.

PURPOSE: The task-based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well-established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise. MATERIALS AND METHODS: Image samples to estimate model observer performance for detection tasks were generated from two-dimensional CT image slices of a uniform water phantom. A common set of images was sent to participating laboratories to perform and document the following tasks: (a) estimate the detectability index of a well-defined CHO and its uncertainty in three conditions involving different sized targets all at the same dose, and (b) apply this CHO to an image set where ground truth was unknown to participants (lower image dose). In addition, and on an optional basis, we asked the participating laboratories to (c) estimate the performance of real human observers from a psychophysical experiment of their choice. Each of the 13 participating laboratories was confidentially assigned a participant number and image sets could be downloaded through a secure server. Results were distributed with each participant recognizable by its number and then each laboratory was able to modify their results with justification as model observer calculation are not yet a routine and potentially error prone. RESULTS: Detectability index increased with signal size for all participants and was very consistent for 6 mm sized target while showing higher variability for 8 and 10 mm sized target. There was one order of magnitude between the lowest and the largest uncertainty estimation. CONCLUSIONS: This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting.

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.

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.