Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers.
We assess the performance of filtered backprojection (FBP), the simultaneous algebraic reconstruction technique (SART) and the maximum likelihood (ML) algorithm for digital breast tomosynthesis (DBT) under variations in key imaging parameters, including the number of iterations, number of projection...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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2011
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| _version_ | 1826286904324128768 |
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| author | Van de Sompel, D Brady, S Boone, J |
| author_facet | Van de Sompel, D Brady, S Boone, J |
| author_sort | Van de Sompel, D |
| collection | OXFORD |
| description | We assess the performance of filtered backprojection (FBP), the simultaneous algebraic reconstruction technique (SART) and the maximum likelihood (ML) algorithm for digital breast tomosynthesis (DBT) under variations in key imaging parameters, including the number of iterations, number of projections, angular range, initial guess, and radiation dose. This is the first study to compare these algorithms for the application of DBT. We present a methodology for the evaluation of DBT reconstructions, and use it to conduct preliminary experiments investigating trade-offs between the selected imaging parameters. This investigation includes trade-offs not previously considered in the DBT literature, such as the use of a stationary detector versus a C-arm imaging geometry. A real breast CT volume serves as a ground truth digital phantom from which to simulate X-ray projections under the various acquisition parameters. The reconstructed image quality is measured using task-based metrics, namely signal CNR and the AUC of a Channelised Hotelling Observer with Laguerre-Gauss basis functions. The task at hand is the detection of a simulated mass inserted into the breast CT volume. We find that the image quality in limited view tomography is highly dependent on the particular acquisition and reconstruction parameters used. In particular, we draw the following conclusions. First, we find that optimising the FBP filter design and SART relaxation parameter yields significant improvements in reconstruction quality from the same projection data. Second, we show that the convergence rate of the maximum likelihood algorithm, optimised with paraboloidal surrogates and conjugate gradient ascent (ML-PSCG), can be greatly accelerated using view-by-view updates. Third, we find that the optimal initial guess is algorithm dependent. In particular, we obtained best results with a zero initial guess for SART, and an FBP initial guess for ML-PSCG. Fourth, when the exposure per view is constant, increasing the total number of views within a given angular range improves the reconstruction quality, albeit with diminishing returns. When the total dose of all views combined is constant, there is a trade-off between increased sampling using a larger number of views and increased levels of quantum noise in each view. Fifth, we do not observe significant differences when testing various access ordering schemes, presumably due to the limited angular range of DBT. Sixth, we find that adjusting the z-resolution of the reconstruction can improve image quality, but that this resolution is best adjusted by using post-reconstruction binning, rather than by declaring lower-resolution voxels. Seventh, we find that the C-arm configuration yields higher image quality than a stationary detector geometry, the difference being most outspoken for the FBP algorithm. Lastly, we find that not all prototype systems found in the literature are currently being run under the best possible system or algorithm configurations. In other words, the present study demonstrates the critical importance (and reward) of using optimisation methodologies such as the one presented here to maximise the DBT reconstruction quality from a single scan of the patient. |
| first_indexed | 2024-03-07T01:50:38Z |
| format | Journal article |
| id | oxford-uuid:99f80729-5e82-4390-8112-8339a9ed4802 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:50:38Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:99f80729-5e82-4390-8112-8339a9ed48022022-03-27T00:18:04ZTask-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:99f80729-5e82-4390-8112-8339a9ed4802EnglishSymplectic Elements at Oxford2011Van de Sompel, DBrady, SBoone, JWe assess the performance of filtered backprojection (FBP), the simultaneous algebraic reconstruction technique (SART) and the maximum likelihood (ML) algorithm for digital breast tomosynthesis (DBT) under variations in key imaging parameters, including the number of iterations, number of projections, angular range, initial guess, and radiation dose. This is the first study to compare these algorithms for the application of DBT. We present a methodology for the evaluation of DBT reconstructions, and use it to conduct preliminary experiments investigating trade-offs between the selected imaging parameters. This investigation includes trade-offs not previously considered in the DBT literature, such as the use of a stationary detector versus a C-arm imaging geometry. A real breast CT volume serves as a ground truth digital phantom from which to simulate X-ray projections under the various acquisition parameters. The reconstructed image quality is measured using task-based metrics, namely signal CNR and the AUC of a Channelised Hotelling Observer with Laguerre-Gauss basis functions. The task at hand is the detection of a simulated mass inserted into the breast CT volume. We find that the image quality in limited view tomography is highly dependent on the particular acquisition and reconstruction parameters used. In particular, we draw the following conclusions. First, we find that optimising the FBP filter design and SART relaxation parameter yields significant improvements in reconstruction quality from the same projection data. Second, we show that the convergence rate of the maximum likelihood algorithm, optimised with paraboloidal surrogates and conjugate gradient ascent (ML-PSCG), can be greatly accelerated using view-by-view updates. Third, we find that the optimal initial guess is algorithm dependent. In particular, we obtained best results with a zero initial guess for SART, and an FBP initial guess for ML-PSCG. Fourth, when the exposure per view is constant, increasing the total number of views within a given angular range improves the reconstruction quality, albeit with diminishing returns. When the total dose of all views combined is constant, there is a trade-off between increased sampling using a larger number of views and increased levels of quantum noise in each view. Fifth, we do not observe significant differences when testing various access ordering schemes, presumably due to the limited angular range of DBT. Sixth, we find that adjusting the z-resolution of the reconstruction can improve image quality, but that this resolution is best adjusted by using post-reconstruction binning, rather than by declaring lower-resolution voxels. Seventh, we find that the C-arm configuration yields higher image quality than a stationary detector geometry, the difference being most outspoken for the FBP algorithm. Lastly, we find that not all prototype systems found in the literature are currently being run under the best possible system or algorithm configurations. In other words, the present study demonstrates the critical importance (and reward) of using optimisation methodologies such as the one presented here to maximise the DBT reconstruction quality from a single scan of the patient. |
| spellingShingle | Van de Sompel, D Brady, S Boone, J Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers. |
| title | Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers. |
| title_full | Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers. |
| title_fullStr | Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers. |
| title_full_unstemmed | Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers. |
| title_short | Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers. |
| title_sort | task based performance analysis of fbp sart and ml for digital breast tomosynthesis using signal cnr and channelised hotelling observers |
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