Multigrid reconstruction with block‐iterative updates for breast tomosynthesis

Michielsen, Koen

doi:10.1118/1.4933247

Cited by 4 publications

(6 citation statements)

References 57 publications

(67 reference statements)

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“…Three reconstruction algorithms were applied: standard Filtered Back Projection (FBP), 10 FBP with Super Resolution and Statistical Artifact Reduction (SRSAR) 11 and multigrid Maximum Likelihood for Transmission with Resolution modeling (MLTR pr ). 12,13 These algorithms may affect performance as they lead to very different image characteristics: the noise and the structures inside the phantom have a different appearance, as illustrated in fig. 2.…”

Section: Image and Phantom Propertiesmentioning

confidence: 98%

Development and application of a channelized Hotelling observer for DBT optimization on structured background test images with mass simulating targets

et al. 2016

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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, MLTR pr ) 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 MLTR pr . 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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Section: Image and Phantom Propertiesmentioning

confidence: 98%

Development and application of a channelized Hotelling observer for DBT optimization on structured background test images with mass simulating targets

et al. 2016

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“…This also means that the reconstruction problem is under-determined, and that the result of an iterative reconstruction will depend on its starting point. Therefore, a good initialization not only helps increase convergence speed, but, in DBT, also improves the reconstruction itself [1].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis

Michielsen¹,

Moriakov²,

Teuwen³

et al. 2020

Preprint

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Reconstruction of digital breast tomosynthesis is a challenging problem due to the limited angle data available in such systems. Due to memory limitations, deep learningbased methods can help improve these reconstructions, but can not (yet) attain sufficiently high resolution. In addition to this practical issue, questions remain on the possibility of such models introducing 'ghost' information from the training data that is not compatible with the projection data. To take advantage of some of the benefits of deep learning-based reconstructions while avoiding these limitations, we propose to use the low resolution deep learning-based reconstruction as an initialization of a regular high resolution iterative method.The network was trained using digital phantoms, some based on a mathematical model and some derived from patient dedicated breast CT scans. The output of this network was then used as initialization for 10 000 iterations of MLTR for nine patient based phantoms that were not included in the training. The same nine cases were also reconstructed without any initialization for comparison.The reconstructions including initialization were found to reach a lower mean squared error than those without, and visual inspection found much improved retrieval of the breast outline and depiction of the skin, confirming that adding the deep learning-based initialization adds valuable information to the reconstruction.

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“…Forward and backward projections are required by iterative DBT reconstruction methods, such as the algebraic reconstruction technique (ART), the simultaneous algebraic reconstruction technique (SART), and the maximum likelihood expectation–maximization (MLEM) reconstruction technique . A better match between the digital implementation and the actual system physics can potentially improve the quality of reconstructed images in iterative reconstruction methods, as has been found in other 3D modalities …”

Section: Introductionmentioning

confidence: 99%

Segmented separable footprint projector for digital breast tomosynthesis and its application for subpixel reconstruction

2017

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Purpose Digital forward and back projectors play a significant role in iterative image reconstruction. The accuracy of the projector affects the quality of the reconstructed images. Digital breast tomosynthesis (DBT) often uses the ray-tracing (RT) projector that ignores finite detector element size. This paper proposes a modified version of the separable footprint (SF) projector, called the segmented separable footprint (SG) projector, that calculates efficiently the Radon transform mean value over each detector element. The SG projector is specifically designed for DBT reconstruction because of the large height-to-width ratio of the voxels generally used in DBT. This study evaluates the effectiveness of the SG projector in reducing projection error and improving DBT reconstruction quality. Methods We quantitatively compared the projection error of the RT and the SG projector at different locations and their performance in regular and subpixel DBT reconstruction. Subpixel reconstructions used finer voxels in the imaged volume than the detector pixel size. Subpixel reconstruction with RT projector uses interpolated projection views as input to provide adequate coverage of the finer voxel grid with the traced rays. Subpixel reconstruction with the SG projector, however, uses the measured projection views without interpolation. We simulated DBT projections of a test phantom using CatSim (GE Global Research, Niskayuna, NY) under idealized imaging conditions without noise and blur, to analyze the effects of the projectors and subpixel reconstruction without other image degrading factors. The phantom contained an array of horizontal and vertical line pair patterns (1 to 9.5 line pairs/mm) and pairs of closely spaced spheres (diameters 0.053 to 0.5 mm) embedded at the mid-plane of a 5-cm-thick breast-tissue-equivalent uniform volume. The images were reconstructed with regular simultaneous algebraic reconstruction technique (SART) and subpixel SART using different projectors. The resolution and contrast of the test objects in the reconstructed images and the computation times were compared under different reconstruction conditions. Results The SG projector reduced the projector error by 1 to 2 orders of magnitude at most locations. In the worst case, the SG projector still reduced the projection error by about 50%. In the DBT reconstructed slices parallel to the detector plane, the SG projector not only increased the contrast of the line pairs and spheres, but also produced more smooth and continuous reconstructed images whereas the discrete and sparse nature of the RT projector caused artifacts appearing as patterned noise. For subpixel reconstruction, the SG projector significantly increased object contrast and computation speed, especially for high subpixel ratios, compared with the RT projector implemented with accelerated Siddon’s algorithm. The difference in the depth resolution among the projectors is negligible under the conditions studied. Our results also demonstrated that subpixel reconstruction can imp...

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Multigrid reconstruction with block‐iterative updates for breast tomosynthesis

Abstract: Multigrid reconstruction improves upon reconstruction with a fixed grid when evaluated at a fixed computational cost. For multigrid reconstruction, using plane-by-plane updates or applying ordered subsets resulted in similar performance.

Cited by 4 publications

References 57 publications

Development and application of a channelized Hotelling observer for DBT optimization on structured background test images with mass simulating targets

Development and application of a channelized Hotelling observer for DBT optimization on structured background test images with mass simulating targets

Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis

Segmented separable footprint projector for digital breast tomosynthesis and its application for subpixel reconstruction

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