Thick Slices from Tomosynthesis Data Sets: Phantom Study for the Evaluation of Different Algorithms

Abstract

Tomosynthesis is a 3-dimensional mammography technique that generates thin slices separated one to the other by typically 1 mm from source data sets. The relatively high image noise in these thin slices raises the value of 1-cm thick slices computed from the set of reconstructed slices for image interpretation. In an initial evaluation, we investigated the potential of different algorithms for generating thick slices from tomosynthesis source data (maximum intensity projection-MIP; average algorithm-AV, and image generation by means of a new algorithm, so-called softMip). The three postprocessing techniques were evaluated using a homogeneous phantom with one textured slab with a total thickness of about 5 cm in which two 0.5-cm-thick slabs contained objects to simulate microcalcifications, spiculated masses, and round masses. The phantom was examined by tomosynthesis (GE Healthcare). Microcalcifications were simulated by inclusion of calcium particles of four different sizes. The slabs containing the inclusions were examined in two different configurations: adjacent to each other and close to the detector and with the two slabs separated by two 1-cm thick breast equivalent material slabs. The reconstructed tomosynthesis slices were postprocessed using MIP, AV, and softMip to generate 1-cm thick slices with a lower noise level. The three postprocessing algorithms were assessed by calculating the resulting contrast versus background for the simulated microcalcifications and contrast-to-noise ratios (CNR) for the other objects. The CNRs of the simulated round and spiculated masses were most favorable for the thick slices generated with the average algorithm, followed by softMip and MIP. Contrast of the simulated microcalcifications was best for MIP, followed by softMip and average projections. Our results suggest that the additional generation of thick slices may improve the visualization of objects in tomosynthesis. This improvement differs from the different algorithms for microcalcifications, speculated objects, and round masses. SoftMip is a new approach combining features of MIP and average showing image properties in between MIP and AV.

Digital mammography and tomosynthesis for breast cancer diagnosis

“…Chawla et al [62] based their study on mastectomy samples which they imaged with tomosynthesis and simulated lesions were added to the tomosynthesis volumes. At a dose level similar to single-view mammography, they found that the optimum number of projection images was 15 --17 at an angular range of 45 . The optimum angular spacing in both the studies conducted by Sechopoulos and Ghetti [50] and Chawla et al [62] was around 3…”

“…Detection of subtle details, like small tumors with low contrast, is therefore improved (Figure 2). For diagnostic purposes, a slice separation of 1 mm has commonly been used, but to reduce radiologist's reading time, which is directly connected to the number of slice images, projects are underway to generate thicker image slabs [45] and to study the effect of thicker slices on the detection of breast lesions.…”

Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers

“…Several reconstruction methods have been explored for use in DBT, including filtered backprojection (FBP) (Lauritsch and Haerer, 1998;Stevens et al, 2001;Claus et al, 2006;Mertelmeier et al, 2006), algebraic reconstruction (ART) (Kaczmarz, 1937;Andersen et al, 1984;Kak and Slaney, 2001;Kempston et al, 2006;Diekmann et al, 2007;Zhang et al, 2007) and maximum likelihood (ML) techniques (Wu et al, 2003;Wu et al, 2008). Unfortunately, the DBT reconstruction problem is under-defined, meaning that there exists an infinite set of possible solutions that satisfy the projection data, also known as the null space.…”

Breast tomosynthesis: Dosimetry and image quality assessment on phantom

“…Detection of microcalcifications is essential since intraductal carcinoma (CIC) detected only through microcalcifications represent 19% of the cancers screened [30,31]. But 3D allows radiologists to use methods, such as the maximum intensity projection (MIP), specific to multislice imaging, to detect and analyze fine structures, such as microcalcifications [8,32]. This has not been assessed in our study.…”