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Balta C, Reiser I, Broeders MJM, Veldkamp WJH, van Engen RE, Sechopoulos I. Lesion detection in digital breast tomosynthesis: human reader experiments indicate no benefit from the integration of information from multiple planes. J Med Imaging (Bellingham) 2023; 10:S11915. [PMID: 37378263 PMCID: PMC10292860 DOI: 10.1117/1.jmi.10.s1.s11915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/07/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
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: AUC 2 D = 0.790 , AUC 3 D = 0.735 , P = 0.03 ; bCT: AUC 2 D = 0.869 , AUC 3 D = 0.716 , P < 0.05 ), but equivalent for capsule-shaped signals (DBT: AUC 2 D = 0.891 , AUC 3 D = 0.915 , P = 0.19 ; bCT: AUC 2 D = 0.854 , AUC 3 D = 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.
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Affiliation(s)
- Christiana Balta
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Ingrid Reiser
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Mireille J. M. Broeders
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
- Radboud University Medical Center, Department for Health Evidence, Nijmegen, The Netherlands
| | | | | | - Ioannis Sechopoulos
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
- University of Twente, Technical Medicine Center, Enschede, The Netherlands
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Yang K, Abbey CK, Chou SHS, Dontchos BN, Li X, Lehman CD, Liu B. Power Spectrum Analysis of Breast Parenchyma with Digital Breast Tomosynthesis Images in a Longitudinal Screening Cohort from Two Vendors. Acad Radiol 2022; 29:841-850. [PMID: 34563442 PMCID: PMC9924291 DOI: 10.1016/j.acra.2021.08.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/19/2021] [Accepted: 08/19/2021] [Indexed: 01/05/2023]
Abstract
RATIONALE AND OBJECTIVES To quantitatively compare breast parenchymal texture between two Digital Breast Tomosynthesis (DBT) vendors using images from the same patients. MATERIALS AND METHODS This retrospective study included consecutive patients who had normal screening DBT exams performed in January 2018 from GE and normal screening DBT exams in adjacent years from Hologic. Power spectrum analysis was performed within the breast tissue region. The slope of a linear function between log-frequency and log-power, β, was derived as a quantitative measure of breast texture and compared within and across vendors along with secondary parameters (laterality, view, year, image format, and breast density) with correlation tests and t-tests. RESULTS A total of 24,339 DBT slices or synthetic 2D images from 85 exams in 25 women were analyzed. Strong power-law behavior was verified from all images. Values of β d did not differ significantly for laterality, view, or year. Significant differences of β were observed across vendors for DBT images (Hologic: 3.4±0.2 vs GE: 3.1±0.2, 95% CI on difference: 0.27 to 0.30) and synthetic 2D images (Hologic: 2.7±0.3 vs GE: 3.0±0.2, 95% CI on difference: -0.36 to -0.27), and density groups with each vendor: scattered (GE: 3.0±0.3, Hologic: 3.3±0.3) vs. heterogeneous (GE: 3.2±0.2, Hologic: 3.4±0.1), 95% CI (-0.27, -0.08) and (-0.21, -0.05), respectively. CONCLUSION There are quantitative differences in the presentation of breast imaging texture between DBT vendors and across breast density categories. Our findings have relevance and importance for development and optimization of AI algorithms related to breast density assessment and cancer detection.
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Affiliation(s)
- Kai Yang
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
| | - Craig K Abbey
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, California
| | | | - Brian N Dontchos
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Xinhua Li
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Constance D Lehman
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Bob Liu
- Division of Diagnostic Imaging Physics, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
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Schmidt TG, Sammut BA, Barber RF, Pan X, Sidky EY. Addressing CT metal artifacts using photon-counting detectors and one-step spectral CT image reconstruction. Med Phys 2022; 49:3021-3040. [PMID: 35318699 PMCID: PMC9353719 DOI: 10.1002/mp.15621] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 02/08/2022] [Accepted: 03/06/2022] [Indexed: 01/06/2023] Open
Abstract
PURPOSE The constrained one-step spectral CT image reconstruction (cOSSCIR) algorithm with a nonconvex alternating direction method of multipliers optimizer is proposed for addressing computed tomography (CT) metal artifacts caused by beam hardening, noise, and photon starvation. The quantitative performance of cOSSCIR is investigated through a series of photon-counting CT simulations. METHODS cOSSCIR directly estimates basis material maps from photon-counting data using a physics-based forward model that accounts for beam hardening. The cOSSCIR optimization framework places constraints on the basis maps, which we hypothesize will stabilize the decomposition and reduce streaks caused by noise and photon starvation. Another advantage of cOSSCIR is that the spectral data need not be registered, so that a ray can be used even if some energy window measurements are unavailable. Photon-counting CT acquisitions of a virtual pelvic phantom with low-contrast soft tissue texture and bilateral hip prostheses were simulated. Bone and water basis maps were estimated using the cOSSCIR algorithm and combined to form a virtual monoenergetic image for the evaluation of metal artifacts. The cOSSCIR images were compared to a "two-step" decomposition approach that first estimated basis sinograms using a maximum likelihood algorithm and then reconstructed basis maps using an iterative total variation constrained least-squares optimization (MLE+TV min $_{\text{min}}$ ). Images were also compared to a nonspectral TV min $_{\text{min}}$ reconstruction of the total number of counts detected for each ray with and without normalized metal artifact reduction (NMAR) applied. The simulated metal density was increased to investigate the effects of increasing photon starvation. The quantitative error and standard deviation in regions of the phantom were compared across the investigated algorithms. The ability of cOSSCIR to reproduce the soft-tissue texture, while reducing metal artifacts, was quantitatively evaluated. RESULTS Noiseless simulations demonstrated the convergence of the cOSSCIR and MLE+TV min $_{\text{min}}$ algorithms to the correct basis maps in the presence of beam-hardening effects. When noise was simulated, cOSSCIR demonstrated a quantitative error of -1 HU, compared to 2 HU error for the MLE+TV min $_{\text{min}}$ algorithm and -154 HU error for the nonspectral TV min $_{\text{min}}$ +NMAR algorithm. For the cOSSCIR algorithm, the standard deviation in the central iodine region of interest was 20 HU, compared to 299 HU for the MLE+TV min $_{\text{min}}$ algorithm, 41 HU for the MLE+TV min $_{\text{min}}$ +Mask algorithm that excluded rays through metal, and 55 HU for the nonspectral TV min $_{\text{min}}$ +NMAR algorithm. Increasing levels of photon starvation did not impact the bias or standard deviation of the cOSSCIR images. cOSSCIR was able to reproduce the soft-tissue texture when an appropriate regularization constraint value was selected. CONCLUSIONS By directly inverting photon-counting CT data into basis maps using an accurate physics-based forward model and a constrained optimization algorithm, cOSSCIR avoids metal artifacts due to beam hardening, noise, and photon starvation. The cOSSCIR algorithm demonstrated improved stability and accuracy compared to a two-step method of decomposition followed by reconstruction.
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Affiliation(s)
- Taly Gilat Schmidt
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Barbara A Sammut
- Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | | | - Xiaochuan Pan
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Emil Y Sidky
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
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Huerga C, Morcillo A, Alejo L, Marín A, Obesso A, Travaglio D, Bayón J, Rodriguez D, Coronado M. Role of correlated noise in textural features extraction. Phys Med 2021; 91:87-98. [PMID: 34742098 DOI: 10.1016/j.ejmp.2021.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 10/19/2022] Open
Abstract
Predictive models of tumor response based on heterogeneity metrics in medical images, such as textural features, are highly suggestive. However, the demonstrated sensitivity of these features to noise does affect the model being developed. An in-depth analysis of the noise influence on the extraction of texture features was performed based on the assumption that an improvement in information quality can also enhance the predictive model. A heuristic approach was used that recognizes from the beginning that the noise has its own texture and it was analysed how it affects the quantitative signal data. A simple procedure to obtain noise image estimation is shown; one which makes it possible to extract the noise-texture features at each observation. The distance measured between the textural features in signal and estimated noise images allows us to determine the features affected in each observation by the noise and, for example, to exclude some of them from the model. A demonstration was carried out using synthetic images applying realistic noise models found in medical images. Drawn conclusions were applied to a public cohort of clinical images obtained using FDG-PET to show how the predictive model could be improved. A gain in the area under the receiver operating characteristic curve between 10 and 20% when noise texture information is used was shown. An improvement between 20 and 30% can be appreciated in the estimated model quality.
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Affiliation(s)
- Carlos Huerga
- Department of Medical Physics and Radiation Protection, Hospital Universitario La Paz, Madrid, Spain.
| | - Ana Morcillo
- Department of Medical Physics and Radiation Protection, Hospital Universitario La Paz, Madrid, Spain
| | - Luis Alejo
- Department of Medical Physics and Radiation Protection, Hospital Universitario La Paz, Madrid, Spain
| | - Alberto Marín
- Department of Medical Physics and Radiation Protection, Hospital Universitario La Paz, Madrid, Spain
| | - Alba Obesso
- Servicio de Radiofísica y Protección Radiológica, ESI/OSI Donostialdea, Donostia, Spain
| | - Daniela Travaglio
- Department of Nuclear Medicine, Hospital Universitario La Paz, Madrid, Spain
| | - Jose Bayón
- Servicio de Radiofísica y Protección Radiológica. Hospital Universitario Rey Juan Carlos, Madrid, Spain
| | | | - Monica Coronado
- Department of Nuclear Medicine, Hospital Universitario La Paz, Madrid, Spain
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Tanguay J, Lalonde R, Bjarnason TA, Yang CYJ. Cascaded systems analysis of anatomic noise in digital mammography and dual-energy digital mammography. Phys Med Biol 2019; 64:215002. [PMID: 31470440 DOI: 10.1088/1361-6560/ab3fcd] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In x-ray based imaging of the breast, contrast between fibroglandular (Fg) tissue and adipose (Ad) tissue is a source of anatomic noise. The goal of this work was to validate by simulation and experiment a mathematical framework for modelling the Fg component of anatomic noise in digital mammograpy (DM) and dual-energy (DE) DM. Our mathematical framework unifies and generalizes existing approaches. We compared mathematical predictions directly with empirical measurements of the anatomic noise power spectrum of the CIRS BR3D structured breast phantom using two clinical mammography systems and four beam qualities. Our simulation and experimental results showed agreement with mathematical predictions. As a demonstration of utility, we used our mathematical framework in a theoretical spectral optimization of DM for the task of detecting breast masses. Our theoretical optimization showed that the optimal tube voltage for DM may be higher than that based on predictions that do not account for anatomic noise, in agreement with recent theoretical findings. Additionally, our theoretical optimization predicts that filtering tungsten-anode x-ray spectra with rhodium has little influence on lesion detectability, in contrast with previous findings. The mathematical methods validated in this work can be incorporated easily into cascaded systems analysis of breast imaging systems and will be useful when optimizating novel techniques for x-ray-based imaging of the breast.
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Affiliation(s)
- Jesse Tanguay
- Department of Physics, Ryerson University, Toronto, Ontario, Canada. Author to whom correspondence should be addressed
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Glick SJ, Ikejimba LC. Advances in digital and physical anthropomorphic breast phantoms for x-ray imaging. Med Phys 2018; 45:e870-e885. [DOI: 10.1002/mp.13110] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Revised: 06/05/2018] [Accepted: 06/10/2018] [Indexed: 01/27/2023] Open
Affiliation(s)
- Stephen J. Glick
- Division of Imaging, Diagnostics, and Software Reliability; Office of Science and Engineering Laboratories; Center for Devices and Radiological Health, Food and Drug Administration; Silver Spring MD 20993 USA
| | - Lynda C. Ikejimba
- Division of Imaging, Diagnostics, and Software Reliability; Office of Science and Engineering Laboratories; Center for Devices and Radiological Health, Food and Drug Administration; Silver Spring MD 20993 USA
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7
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Baldelli P, Bertolini M, Contillo A, Della Gala G, Golinelli P, Pagan L, Rivetti S, Taibi A. A comparative study of physical image quality in digital and synthetic mammography from commercially available mammography systems. Phys Med Biol 2018; 63:165020. [PMID: 29972144 DOI: 10.1088/1361-6560/aad106] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
We present a comparison between full field digital mammography and synthetic mammography, performed on several mammography systems from four different manufacturers. The analysis is carried out on both the digital and synthetic images of two commercially available mammography phantoms, and focuses on a set of objective metrics that encode the geometrical appearance of imaging features of diagnostic interest. In particular, we measured sizes and contrasts of several clusters of microcalcification specks, shapes and contrasts of circular masses, and the power spectrum of background regions mimicking the heterogeneous texture of the breast parenchyma. Despite the potential issues of tomosynthesis in terms of image blurring, the synthetic images do not highlight any globally significant differences in the rendering of the details of interest, when compared to the original digital mammograms: relative contrasts are generally preserved, as well as the geometry of broad structures. We conclude that, as far as the considered objective metrics are concerned, the image quality of synthetic mammography does not exhibit significant differences with respect to the one of full field digital mammography, for all the considered systems.
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Affiliation(s)
- Paola Baldelli
- BreastCheck, National Cancer Screening Service, 36 Eccles Street, Dublin 7, Ireland
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Rose SD, Sanchez AA, Sidky EY, Pan X. Investigating simulation-based metrics for characterizing linear iterative reconstruction in digital breast tomosynthesis. Med Phys 2018; 44:e279-e296. [PMID: 28901614 DOI: 10.1002/mp.12445] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 05/29/2017] [Accepted: 06/21/2017] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Simulation-based image quality metrics are adapted and investigated for characterizing the parameter dependences of linear iterative image reconstruction for DBT. METHODS Three metrics based on a 2D DBT simulation are investigated: (1) a root-mean-square-error (RMSE) between the test phantom and reconstructed image, (2) a gradient RMSE where the comparison is made after taking a spatial gradient of both image and phantom, and (3) a region-of-interest (ROI) Hotelling observer (HO) for signal-known-exactly/background-known-exactly (SKE/BKE) and signal-known-exactly/background-known-statistically (SKE/BKS) detection tasks. Two simulation studies are performed using the aforementioned metrics, varying voxel aspect ratio, and regularization strength for two types of Tikhonov-regularized least-squares optimization. The RMSE metrics are applied to a 2D test phantom with resolution bar patterns at varying angles, and the ROI-HO metric is applied to two tasks relevant to DBT: lesion detection, modeled by use of a large, low-contrast signal, and microcalcification detection, modeled by use of a small, high-contrast signal. The RMSE metric trends are compared with visual assessment of the reconstructed bar-pattern phantom. The ROI-HO metric trends are compared with 3D reconstructed images from ACR phantom data acquired with a Hologic Selenia Dimensions DBT system. RESULTS Sensitivity of the image RMSE to mean pixel value is found to limit its applicability to the assessment of DBT image reconstruction. The image gradient RMSE is insensitive to mean pixel value and appears to track better with subjective visualization of the reconstructed bar-pattern phantom. The ROI-HO metric shows an increasing trend with regularization strength for both forms of Tikhonov-regularized least-squares; however, this metric saturates at intermediate regularization strength indicating a point of diminishing returns for signal detection. Visualization with the reconstructed ACR phantom images appear to show a similar dependence with regularization strength. CONCLUSIONS From the limited studies presented it appears that image gradient RMSE trends correspond with visual assessment better than image RMSE for DBT image reconstruction. The ROI-HO metric for both detection tasks also appears to reflect visual trends in the ACR phantom reconstructions as a function of regularization strength. We point out, however, that the true utility of these metrics can only be assessed after amassing more data.
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Affiliation(s)
- Sean D Rose
- University of Chicago, Department of Radiology MC-2026, 5841 S. Maryland Avenue, Chicago, IL, 60637, USA
| | - Adrian A Sanchez
- University of Chicago, Department of Radiology MC-2026, 5841 S. Maryland Avenue, Chicago, IL, 60637, USA
| | - Emil Y Sidky
- University of Chicago, Department of Radiology MC-2026, 5841 S. Maryland Avenue, Chicago, IL, 60637, USA
| | - Xiaochuan Pan
- University of Chicago, Department of Radiology MC-2026, 5841 S. Maryland Avenue, Chicago, IL, 60637, USA
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Balta C, Bouwman RW, Sechopoulos I, Broeders MJM, Karssemeijer N, van Engen RE, Veldkamp WJH. A model observer study using acquired mammographic images of an anthropomorphic breast phantom. Med Phys 2017; 45:655-665. [DOI: 10.1002/mp.12703] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 10/19/2017] [Accepted: 11/12/2017] [Indexed: 12/31/2022] Open
Affiliation(s)
- Christiana Balta
- Dutch Expert Centre for Screening (LRCB), Radboud University Medical Center, Wijchenseweg 101, 6538 SW, Nijmegen, The Netherlands.,Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Ramona W Bouwman
- Dutch Expert Centre for Screening (LRCB), Radboud University Medical Center, Wijchenseweg 101, 6538 SW, Nijmegen, The Netherlands
| | - Ioannis Sechopoulos
- Dutch Expert Centre for Screening (LRCB), Radboud University Medical Center, Wijchenseweg 101, 6538 SW, Nijmegen, The Netherlands.,Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Mireille J M Broeders
- Dutch Expert Centre for Screening (LRCB), Radboud University Medical Center, Wijchenseweg 101, 6538 SW, Nijmegen, The Netherlands.,Department for Health Evidence, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Nico Karssemeijer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Ruben E van Engen
- Dutch Expert Centre for Screening (LRCB), Radboud University Medical Center, Wijchenseweg 101, 6538 SW, Nijmegen, The Netherlands
| | - Wouter J H Veldkamp
- Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
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Wen G, Markey MK, Park S. Model observer design for multi-signal detection in the presence of anatomical noise. Phys Med Biol 2017; 62:1396-1415. [DOI: 10.1088/1361-6560/aa51e9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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