Purpose: The purpose of this work is to compare two methods of artifact spread function (ASF) estimation in digital breast tomosynthesis (DBT) and study the feasibility of a task-based ASF estimation. Methods: A homogeneous PMMA phantom with two aluminum spheres with size 0.5mm and 1.0mm was scanned on Siemens Inspiration, Giotto Class and Hologic Dimensions DBT systems. The ASF curves were estimated using a standard method from average pixel values from the DBT planes. A physical phantom with anthropomorphic features, including microcalcification simulating particles of average diameter 0.24 mm and a 3.0mm mass-like lesion, was also scanned on the same DBT systems. The in-focus and out-of-focus planes were read with a newly developed model observer in order to assess target detectability through the different slices and calculate task-based ASF curves for each DBT system. The corresponding curves for the standard sphere and the task-based methods were compared. Results: The ASF curves for the smaller targets, i.e. the 0.24 mm microcalcification particles and 0.5 mm aluminum sphere, were found to match closely, despite the size difference and the ASF curves for the larger targets. The propagation across planes for the 3.0mm mass-like lesion and the 1.0mm aluminum sphere did not match. Conclusions: The task-based ASF estimation gives better clinical relevance to the artifact spread estimation. The ASF of 0.5mm aluminum sphere calculated with the standard method can approximate the ASF of calcification cluster in a background with anthropomorphic properties.
Purpose: To investigate the possibility of evaluating synthetic mammograms (SM) with a 3D structured phantom combined with model observer scoring. Methods: SM images were acquired on the Siemens Mammomat Revelation in order to set up a human observer study with 6 readers. Regions of interest with lesions (microcalcifications and masses) present and absent were selected for use in a four-alternative forced choice study. Image acquisitions and reading was performed at AEC,½ AEC and 2×AEC dose levels. The percentage correct (PC) results were calculated for all readers together with the standard error of the mean (SEM). A two-layer non-biased Channelized Hotelling Observer (CHO) for lesion detection was used: a two Laguerre-Gauss channel CHO applied first for localization and then an eight Gabor channel CHO for classification. Observer PC results were estimated using a bootstrap method, and the standard deviation (SD) was used as a figure of merit for reproducibility. Results: Following tuning steps, good correlation was found between the MO and human observer results for both microcalcifications and masses, at the three dose levels. The CHO predicted the PC values of the human readers, but with better reproducibility than the human readers. The detection threshold trends of the CHO matched those of the human observers. Conclusion: A two-layer CHO, with appropriate tuning and testing steps, could approximate the human observer detection results for microcalcifications and masses in SM images acquired on a Siemens Revelation DBT systems over three dose levels . The model observer developed is a promising candidate to track imaging performance in SM.
Purpose: The purpose of this study is to test the applicability of a previously developed model observer for detection of calcification clusters in digital breast tomosynthesis (DBT) on five different types of DBT scanners. Methods: A physical phantom with anthropomorphic features (“L1”) was scanned on DBT scanners from five different vendors: Fujifilm Amulet Innovality, GE Senographe Pristina, IMS Giotto Class, Hologic 3Dimensions and Siemens Mammomat Revelation at three dose levels. The phantom images were then prepared for four-alternative forced choice (4AFC) reading study, where six medical physicist observers participated. The image datasets were read as well by a previously developed channelized Hotelling model observer (CHO), also using a 4AFC paradigm, and compared with the human observer results. Results: The percentage of correctly detected calcification clusters (PC) for each target size, dose level and DBT system was compared between the model and the human observers. The goodness of the fit criteria had correlation coefficients varying from 0.94 to 1.00; linear regression slopes ranged from 0.96 to 1.37 and the mean error was between -2.2PC to 5.2PC. Conclusion: The two-step CHO algorithm results closely matched the detectability results of the human observers and can therefore be used for future image quality scoring of the L1 phantom images on these DBT system within the studied dose rate.
Purpose: This work aims to develop an anthropomorphic convolutional neural network (CNN) classifier, based on the ResNet18 deep learning network and validate it for task based image quality evaluation of digital breast tomosynthesis (DBT) using a structured phantom with non-spiculated mass simulating lesions. Methods: The phantom is constructed from an acrylic breast-shaped container, filled with acrylic spheres and water resembling the background. Five 3D printed non-spiculated mass targets are also inserted in the phantom each with differing size from 1.5mm to 5.7mm. The phantom was scanned 530 times on 8 different DBT systems with 3 dose levels. Half of the image dataset was read by human readers in 4-alternative forced choice (4-AFC) paradigm. The 4-AFC human scores were used to label the cropped signal present and signal absent images. A pre-trained ResNet18 neural network was used and modified for binary classification and the labeled images were used to further train the network for the specific non-spiculated mass detection task. With completed 50 training epochs, the resulting ResNet18 classifier was validated wit the second half of the image dataset against human results. During the training process the loss and accuracy were stored, and statistical analysis was performed for the validation of the ResNet18 against human observers. Results and conclusions: The ResNet18 classifier shows good agreement against human observers for most of the DBT systems and reading sessions. The overall correlation was higher than 0.92. The study shows that a CNN can successfully approximate human scores and can be used for future DBT system image quality estimation studies.
Purpose: To develop a deep learning approach for channelization of the Hotelling model observer (DL-CHO) and apply to the task based image quality evaluation of digital breast tomosynthesis (DBT) using a structured phantom. Methods: An acrylic semi-cylindrical container was filled with different sizes of acrylic spheres and water. Five 3D printed non-spiculated mass models were also inserted in the phantom, each with different size (diameter from 1.5mm to 5.7mm). The phantom was scanned on 8 different DBT systems, at 3 dose levels on each system, giving a total of 594 DBT scans. Nearly half of the image dataset was read by human readers using a 4-alternative forced choice (4-AFC) paradigm. From the human results, an anthropomorphic DL-CHO was developed and trained, utilizing a single convolutional layer with five kernels functioning like channels. After 50 training epochs, the convolutional kernels were fixed and then validated with the second half of the image dataset. Statistical analysis of the goodness of the fit between the newly developed DL-CHO and human observers was performed to estimate the appropriateness of the new CHO for multivendor tomosynthesis studies. Results: The DL-CHO shows good agreement with human observers for all 8 DBT systems, with Pearson’s correlation between 0.90 and 0.99; linear regression slope between 0.60 and 1.17; and mean error between -5.6PC and 12PC. The DL-CHO shows better reproducibility compared to human observers for most of the lesion sizes. Conclusions: The DL-CHO offers a robust and efficient means of evaluating DBT test object images, for the purpose of DBT system image quality evaluation.
Proc. SPIE. 11312, Medical Imaging 2020: Physics of Medical Imaging
KEYWORDS: Target detection, Breast, Polymethylmethacrylate, Tissues, Signal attenuation, Photography, 3D modeling, Quality measurement, Liquids, Digital breast tomosynthesis
Purpose: In this work we present equivalent breast thickness and dose sensitivity of a next iteration 3D structured breast phantom with lesion models to demonstrate its potential use for quality assurance measurements in breast imaging. Methods: PMMA equivalent thickness was determined employing the automatic exposure control (AEC) of Siemens Mammomat Inspiration and Siemens Mammomat Revelation. A 2D projection image of the phantom was acquired and the corresponding AEC settings recorded as reference. Equivalent PMMA thickness was found by interpolating between three PMMA thicknesses with mAs values close to the reference settings selected by AEC. Dose sensitivity of the reconstructed digital breast tomosynthesis (DBT) images was assessed by two experienced readers using a four alternative forced choice (4-AFC) study. Three different dose levels for lesion models and microcalcifications were evaluated. Results: PMMA equivalent thickness of the phantom was 46.8 mm and 47.0 mm for measurements on Siemens Mammomat Inspiration and Siemens Mammomat Revelation which equals to a breast equivalent thickness of 55.5 mm and 55.8 mm, respectively, compared to a physical phantom thickness of 53.5 mm. For lesion models dose sensitiviy of the detectability was not obvious. For microcalcification the diameter threshold was found to increase for decreasing dose from high dose to AEC to low dose. Conclusions: We found the measured equivalent breast thickness of our phantom to be close to its physical thickness. It can be concluded that changes in dose can be detected by the presented phantom for the tested dose levels.
KEYWORDS: Digital breast tomosynthesis, Molybdenum, Particles, 3D modeling, Visualization, Breast, 3D image processing, Target detection, Reconstruction algorithms, Mammography
We compare the reproducibility of the human observers and a channelized Hotelling observer (CHO), when reading digital breast tomosynthesis (DBT) images of a physical phantom containing a breast simulating structured background and calcification clusters at three dose levels. The phantom is scanned 217 times on a Siemens Inspiration DBT system. Volumes of interest, with and without the calcification targets, are extracted and the human observers’ percentage of correct (PC) scores is evaluated using a four-alternative forced choice method. A two-layer CHO is developed using the human observer results. The first layer consists of a localizing CHO that identifies the most conspicuous calcifications using two Laguerre–Gauss channels. Then a CHO with eight Gabor channels estimates the PC score for the calcification cluster. Observer reproducibility is estimated by bootstrapping, and the standard deviation (SD) is used as a figure of merit. The CHO closely approximated the human observer results for all the three dose levels with a correlation of >0.97. For the larger calcification cluster sizes, both observers have similar reproducibility, whereas the CHO is more reproducible for the smaller calcifications, with a maximum of 5.5 SD against 13.1 SD for the human observers. The developed CHO is a good candidate for automated reading of the calcification clusters of the structured phantom, with better reproducibility than the human readers for small calcifications.
This work examined the impact of the presampling Modulation Transfer Function (MTF) on detectability of lesion-like targets in digital mammography. Two needle CR plates (CR1 and CR2) with different MTF curves but identical detector response (sensitivity) were selected. The plates were characterized by MTF, normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE). Three image quality phantoms were applied to study the impact of the difference in MTF: first, the CDMAM contrast-detail phantom to give gold thickness threshold (T); second, a 3D structured phantom with lesion models (calcifications and masses), evaluated via a 4-alternative forced-choice study to give threshold diameter (dtr) and third, a detectability index (d') from a 50 mm PMMA flat field image and an 0.2 mm Al contrast square. MTF coefficient of variation was ~1%, averaged up to 5 mm-1. At 5 mm-1, a significant 24% reduction in MTF was observed. The lower MTF caused a 12% reduction in NNPS for CR2 compared to CR1 (at detector air kerma 117 μGy). At 5 mm-1, there was a drop in DQE of 34% for CR2 compared to CR1. For the test objects, there was a trend to lower detectability for CR2 (lower MTF) for all but one parameter, however none of the changes were significant. The MTF is a sensitive and easily applied means of tracking changes in sharpness before these changes are uncovered using lesion simulating objects in test objects.
Proc. SPIE. 10718, 14th International Workshop on Breast Imaging (IWBI 2018)
KEYWORDS: Target detection, Breast, Polymethylmethacrylate, Imaging systems, 3D modeling, Systems modeling, Digital mammography, 3D image processing, Digital breast tomosynthesis, Breast imaging
The purpose of this study is comparing the detection performance in 2D full field digital mammography (FFDM) and digital breast tomosynthesis (DBT) using a structured phantom with inserted target objects. The phantom consists of a semi-cylindrical PMMA container, filled with water and PMMA spheres of different diameters. Microcalcifications and 3D printed masses (spiculated and non-spiculated) were inserted. The phantom was imaged ten times in both modes of five systems, using automatic exposure control (AEC) and at half and double the AEC dose. Five readers evaluated target detectability in a four-alternative forced-choice study. The percentage of correct responses (PC) was assessed based on 10 trials of each reader for each object type, size, imaging modality and dose level. Additionally, detection threshold diameters at 62.5 PC were assessed via non-linear regression fitting of the psychometric curve. Evaluation of target detection in FFDM showed that spiculated masses were better detected compared to non-spiculated masses. In DBT, detection of both mass types increased significantly (p=0.0001) compared to FFDM. Microcalcification detection thresholds ranged between 110 and 118 μm and were similar for the five systems in FFDM while larger variations (106-158 μm) were found in DBT. Mass detection was independent of dose in FFDM while weak dependence was seen for DBT. Microcalcification detection increased with increasing dose for both modalities. The phantom was able to show detectability differences between FFDM and DBT mode for five commercial systems in line with the findings from clinical trials. We suggest to use the phantom for task-based assessment methods for acceptance and commissioning testing of DBT systems.
In digital breast tomosynthesis (DBT) large number of parameters influence system performance and the requirement to achieve high quality images every day suggest the implementation of a daily quality control (DQC) procedure. In 2D digital mammography, daily QC is typically performed with homogenous plates and a minimal amount of technical inserts for assessment of NNPS, signal to noise, uniformity, defective pixels and other artefacts. This work proposes an alternative means of performing DQC in DBT with a 3D structured phantom that also includes a constancy test of reconstruction stability in the analysis. The aim of the study was to explore deep learning techniques to automatically track deviations from the normal or baseline operating point and compare the results to the standard metrics. As a first test case, changes in dose were investigated.
Feed-forward convolutional neural networks (CNN) have been successfully applied in the medical imaging domain. A 12 layer CNN model was constructed to extract features for image classification. A structured phantom was scanned on a Siemens DBT system at three dose levels: dose set by the automatic exposure control (AEC) system, half this dose and double. After training the CNN on 36 DBT acquisitions (51840 image segments), newly acquired test images were categorized by the algorithm into the dose categories with an accuracy of 99.7%. Parallel to that the standard methods as NNPS and pixel value (PV) mean and variance calculated for the projection and reconstructed planes also show ability to detect the dose level change with some limitations for the reconstructed planes. This result indicates the potential for further use of deep learning algorithms for DQC when using only the reconstructed DBT planes.
Proc. SPIE. 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
KEYWORDS: Data modeling, 3D modeling, Mammography, Signal detection, 3D image processing, Classification systems, Digital breast tomosynthesis, Breast imaging
Digital breast tomosynthesis (DBT) is a relatively new 3D breast imaging technique, which allows for better low contrast lesion detection than 2D full field digital mammography (FFDM). European guidelines for quality control in FFDM specify minimum and achievable threshold contrasts of small test inserts, determined from readings by human observers. Today model observers are being developed to predict and subsequently substitute human detectability readings. A similar performance test would be welcomed for DBT. However, since such a performance estimation is based on an observer classification, in order to circumvent misjudgments, it is important that the classification system is reliable. The aim of this study was to assess the human and model observer reliability by determining the observer reproducibility when reading 5 datasets from 60 tomosynthesis series acquired under the same conditions. For this purpose, a 3D structured phantom with calcification cluster models was scanned on a Siemens Inspiration tomosynthesis system. VOIs were extracted from these acquisitions and read under a 4 alternative forced choice (4-AFC) paradigm by 6 human observers. A channelized Hotelling model observer using 8 Laguerre-Gauss(LG) channels was developed including a scanning algorithm to detect the calcification clusters. An internal noise method was used to better approximate the human reading results. The observer reproducibility was estimated by bootstrapping and SEM was used as a figure of merit. The results show that the model observer is more reproducible for the smaller calcification sizes with maximum of 5.81 SEM, than human observer with maximum of 13.57 SEM. For the larger clusters both observers have similar reproducibility.
Proc. SPIE. 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
KEYWORDS: 3D acquisition, Visualization, 3D modeling, Medical imaging, Image quality, Mammography, Reconstruction algorithms, Quality systems, Digital breast tomosynthesis
Digital breast tomosynthesis (DBT) is a relatively new 3D mammography technique that promises better detection of low contrast masses than conventional 2D mammography. The parameter space for DBT is large however and finding an optimal balance between dose and image quality remains challenging. Given the large number of conditions and images required in optimization studies, the use of human observers (HO) is time consuming and certainly not feasible for the tuning of all degrees of freedom. Our goal was to develop a model observer (MO) that could predict human detectability for clinically relevant details embedded within a newly developed structured phantom for DBT applications. DBT series were acquired on GE SenoClaire 3D, Giotto Class, Fujifilm AMULET Innovality and Philips MicroDose systems at different dose levels, Siemens Inspiration DBT acquisitions were reconstructed with different algorithms, while a larger set of DBT series was acquired on Hologic Dimensions system for first reproducibility testing. A channelized Hotelling observer (CHO) with Gabor channels was developed The parameters of the Gabor channels were tuned on all systems at standard scanning conditions and the candidate that produced the best fit for all systems was chosen. After tuning, the MO was applied to all systems and conditions. Linear regression lines between MO and HO scores were calculated, giving correlation coefficients between 0.87 and 0.99 for all tested conditions.
Proc. SPIE. 10132, Medical Imaging 2017: Physics of Medical Imaging
KEYWORDS: Signal to noise ratio, Breast, Imaging systems, Sensors, Image processing, Image quality, Modulation transfer functions, Digital breast tomosynthesis
Digital breast tomosynthesis (DBT) is a relatively new diagnostic imaging modality for women. Currently, various models
of DBT systems are available on the market and the number of installations is rapidly increasing. EUREF, the European
Reference Organization for Quality Assured Breast Screening and Diagnostic Services, has proposed a preliminary
Guideline - protocol for the quality control of the physical and technical aspects of digital breast tomosynthesis systems,
with an ultimate aim of providing limiting values guaranteeing proper performance for different applications of DBT. In
this work, we introduce an adaptive toolkit developed in accordance with this guideline to facilitate the process of image
quality evaluation in DBT performance test. This toolkit implements robust algorithms to quantify various technical
parameters of DBT images and provides a convenient user interface in practice. Each test is built into a separate module
with configurations set corresponding to the European guideline, which can be easily adapted to different settings and
extended with additional tests. This toolkit largely improves the efficiency for image quality evaluation of DBT. It is also
going to evolve with the development of protocols in quality control of DBT systems.
Digital breast tomosynthesis (DBT) is a 3D mammography technique that promises better visualization of low contrast lesions than conventional 2D mammography. A wide range of parameters influence the diagnostic information in DBT images and a systematic means of DBT system optimization is needed. The gold standard for image quality assessment is to perform a human observer experiment with experienced readers. Using human observers for optimization is time consuming and not feasible for the large parameter space of DBT. Our goal was to develop a model observer (MO) that can predict human reading performance for standard detection tasks of target objects within a structured phantom and subsequently apply it in a first comparative study. The phantom consists of an acrylic semi-cylindrical container with acrylic spheres of different sizes and the remaining space filled with water. Three types of lesions were included: 3D printed spiculated and non-spiculated mass lesions along with calcification groups. The images of the two mass lesion types were reconstructed with 3 different reconstruction methods (FBP, FBP with SRSAR, MLTRpr) and read by human readers. A Channelized Hotelling model observer was created for the non-spiculated lesion detection task using five Laguerre-Gauss channels, tuned for better performance. For the non-spiculated mass lesions a linear relation between the MO and human observer results was found, with correlation coefficients of 0.956 for standard FBP, 0.998 for FBP with SRSAR and 0.940 for MLTRpr. Both the MO and human observer percentage correct results for the spiculated masses were close to 100%, and showed no difference from each other for every reconstruction algorithm.
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