Mr. Lin Chen Profile

Lin Chen

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Proceedings Article | 6 March 2013 Paper

Impact of subtraction and reconstruction strategies on dual-energy contrast enhanced breast tomosynthesis with interleaved acquisition

Gene Gindi, Lin Chen, Yihuan Lu, Yue-Houng Hu, Wei Zhao

Proceedings Volume 8668, 86685O (2013) https://doi.org/10.1117/12.2007960

KEYWORDS: Digital breast tomosynthesis, Reconstruction algorithms, Remote sensing, Breast, Data acquisition, Sensors, X-rays, Image quality, Iodine, Image contrast enhancement

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Contrast enhanced digital breast tomosynthesis can yield superior visualization of tumors relative to conventional tomosynthesis and can provide the contrast uptake kinetics available in breast MR while maintaining a higher image spatial resolution. Conventional dual-energy (DE) acquisition protocols for contrast enhancement at a given time point often involve two separate continuous motion sweeps of the X-ray tube (one per energy) followed by weighted subtraction of the HE (high energy)and LE (low energy) projection data. This subtracted data is then reconstructed. Relative to two-sweep acquisition, interleaved acquisition suffers from a lesser degree of patient motion artifacts and entails less time spent under uncomfortable breast compression. These advantages for DE interleaved acquisition are reduced by subtraction artifacts due to the fact that each HE, LE acquisition pair is offset in angle for the usual case of continuous tube motion. These subtraction artifacts propagate into the reconstruction and are present even in the absence of patient motion. To reduce these artifacts, we advocate a strategy in which the HE and LE projection data are separately reconstructed then undergo weighted subtraction in the reconstruction domain. We compare the SDNR of masses in a phantom for the subtract-then-reconstruct vs. reconstruct-then-subtract strategies and evaluate each strategy for two algorithms, FBP and SART. We also compare the interleave SDNR results with those obtained with the conventional dual-energy double-sweep method. For interleave scans and for either algorithm the reconstruct-thensubtract strategy yields higher SDNR than the subtract-then-reconstruct strategy. For any of the three acquisition modes, SART reconstruction yields better SDNR than FBP reconstruction. Finally the interleave reconstruct-then-subtract method using SART yields higher SDNR than any of the double-sweep conventional acquisitions.

Proceedings Article | 6 March 2013 Paper

TV-Stokes strategy for sparse-view CT image reconstruction

Yan Liu, Lin Chen, Hao Zhang, Ke Wang, Jianhua Ma, Zhengrong Liang

Proceedings Volume 8668, 86683D (2013) https://doi.org/10.1117/12.2006957

KEYWORDS: Televisions, CT reconstruction, Computed tomography, Neuroimaging, Brain, Image restoration, Image resolution, Data acquisition, Scanners, Image analysis

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This paper introduces a new strategy to reconstruct computed tomography (CT) images from sparse-view projection data based on total variation stokes (TVS) strategy. Previous works have shown that CT images can be reconstructed from sparse-view data by solving a constrained TV problem. Considering the incompressible property of the voxels along the tangent direction of isophote lines, a tangent vector is consolidated in this newly-proposed algorithm for normal vector estimation. Then, a minimization problem based on this estimated normal vector is addressed and resolved in computation. The to-be-estimated image is obtained by executing this two-step framework iteratively with projection data fidelity constraints. By introducing this normal vector estimation, the edge information of the image is well preserved and the artifacts are efficiently inhibited. In addition, the new proposed algorithm can mitigate the staircase effects which are usually observed from the results of the conventional constrained TV method. In this study, the TVS method was evaluated by patients’ brain raw data which was acquired from Siemens SOMATOM Sensation 16-slice CT scanner. The results suggest that the proposed TVS strategy can accurately reconstruct the brain images and produce comparable results relative to the TV-projection onto convex sets (TV-POCS) method and its general case: adaptiveweighted TV-POCS (AwTV-POCS) method from 232,116 projection views. In addition, an improvement was observed when using only 77 views for TVS method compared to the AwTV/TV-POCS methods. In the quantitative evaluation, the TVS method showed adequate noise-resolution property and highest universal quality index value.

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