Improvement of image performance in digital breast tomosynthesis (DBT) by incorporating a compressed-sensing (CS)-based deblurring scheme

Kim, Kyuseok; Park, Yeonok; Cho, Heemoon; Cho, Hyosung; Je, Uikyu; Park, Chulkyu; Lim, Hyunwoo; Park, Soyoung; Woo, Tae Ho; Choi, Sungwon

doi:10.1016/j.radphyschem.2016.06.025

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…The IQA of DBT showed a non‐significantly increase compared to that of CBBCT, aligning with the findings of Gong et al 25 FDK, CGLS, and MLEM algorithms outperformed their SART and SARTTV counterparts in processing time. Our SART and SARTTV required 2–5 iterations, while MLEM demanded 15–100 for similar image quality, as per Zhang et al 38 and van de Sompel et al 47 CBBCT images were computationally costlier because of the limited projection of DBT acquisition 33 . CUDA parallelization was used to reduce the computational burden 45 .…”

Section: Discussionmentioning

confidence: 93%

“…Our SART and SARTTV required 2-5 iterations, while MLEM demanded 15-100 for similar image quality, as per Zhang et al 38 and van de Sompel et al 47 CBBCT images were computationally costlier because of the limited projection of DBT acquisition. 33 CUDA parallelization was used to reduce the computational burden. 45 According to the study by O'Connell et al 11 reported a non-significantly improvement in glandular dose for DBT versus CBBCT with 6.5 ± 2.9 and 8.2 ± 1.4 mGy for CBBCT with 21% dose reduction for DBT.…”

Section: Discussionmentioning

confidence: 99%

“…The MLEM reconstruction algorithm is a statistical method to reconstruct an image based on the analysis of the process of x‐ray photons measured by statistical means, which is based on an iterative algorithm 33 . The Image x can be recovered using maximum likelihood estimation by solving an optimization problem, when performing MLEM reconstruction, a priori constraints

\min L(x)\ \mathrm{s}.\mathrm{t}.x\ge 0

based on the data fidelity term and the edge‐preserving regularization term is enforced 34 .…”

Section: Methodsmentioning

confidence: 99%

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Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms

Komolafe,

Tian,

Awoniya

et al. 2024

Int J Imaging Syst Tech

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Breast cancer (BC) is the most commonly diagnosed non‐skin cancer in women. To achieve early and accurate diagnosis, three‐dimensional (3D) cone‐beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) modalities are used. Importantly, the comparison of reconstruction accuracy of both CBBCT and DBT has rarely been investigated, thus constituting a research gap. This study systematically investigated the performances of the CBBCT and DBT for different reconstruction algorithms using both BR3D breast phantom and breast images. We acquired clinical breast images and scanned the BR3D phantom for additional breast images. These acquired images were used to simulate projection data using predefined CBBCT and DBT geometries. The simulated projections were reconstructed using five different reconstruction algorithms. To evaluate the reconstruction accuracy, we calculated average image quality assessment (IQA) indices, including peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and others, across different algorithms and modalities. The pooled PSNR, SSIM, and RMSE for DBT and CBBCT images are (31.6265 ± 0.8725), (0.9353 ± 0.0077), and (0.0270 ± 0.0025) and (29.7007 ± 0.9249), (0.9136 ± 0.0130), and (0.0342 ± 0.0040), which implies that the overall IQA indices of DBT are superior to CBBCT; therefore, DBT tends to reveal more BC detectability as the diagnosis outcome would largely depend on good quality images. The results show that DBT gives an improved result for all algorithms compared to CBBCT, although further experimental trials may be needed to establish the findings fully. The findings suggest that using DBT may enhance the accuracy of BC diagnosis compared to CBBCT due to its superior image quality in clinical practice, emphasizing the importance of selecting optimal reconstruction algorithms for improved diagnostic outcomes.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

\min L(x)\ \mathrm{s}.\mathrm{t}.x\ge 0

based on the data fidelity term and the edge‐preserving regularization term is enforced 34 .…”

Section: Methodsmentioning

confidence: 99%

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Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms

Komolafe,

Tian,

Awoniya

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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“…was solved in this work using the backward‐forward splitting method together with variable splitting methods to convert the 2D TV denoising problem into a denoising problem with a generalized shrinkage operator as a solution. However, there are many other ways to solve the same optimization problem including other strategies to leverage the elegance of a variety of other variable splitting method and corresponding ADMM update strategies or to incorporate the benefits of TV regularization …”

Section: Discussionmentioning

confidence: 99%

Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction

Garrett

et al. 2018

Medical Physics

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Statistical image reconstruction enabled a significant reduction of both the through-plane artifacts level and anatomical clutter in the DBT reconstructions. The β value was found to be β≈2.14 with the SIR method. This value stays in the middle between the β≈1.8 for cone beam CT and β≈3.2 for mammography. In contrast, the measured β value in the clinical reconstructions (β≈3.17) remains close to that of mammography.

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Synthesising two-dimensional mammographic images using compressed sensing-reconstructed digital breast tomosynthesis images

Han,

Yang,

et al. 2024

J. Inst.

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This study presents a method synthesizing a two-dimensional (2D) mammographic image of reasonable quality directly from a 3D digital breast tomosynthesis (DBT) image, reconstructed using an advanced compressed sensing (CS)-based algorithm. This approach aims to reduce the radiation dose required for complementary imaging technologies of digital mammography (DM) and DBT, eliminating the need for additional DM examinations. The method involves three main steps: projection data acquisition from a DBT system, CS-based DBT reconstruction, and synthesis of a 2D mammographic image from the reconstructed DBT image. To verify the efficacy of the proposed method, we conducted both a simulation and an experiment on a numerical breast and commercially available BR3D phantoms, respectively, prior to practical implementation in real-world DBT systems. Our simulation and experimental results indicated that the CS-based algorithm yielded markedly improved DBT reconstruction quality, preserving superior image homogeneity, better edge contour and sharpening, and fewer image artifacts. The measured contrast-to-noise ratio and structural similarity values of the CS-reconstructed DBT images were 10.31 and 0.78, respectively, which were approximately 2.2 and 3.1 times larger, respectively, than those of the filtered-backprojection-reconstructed DBT images in the simulation. The quality of the synthetic mammographic images using the CS-reconstructed DBT images was similar to that of conventional mammographic images obtained using a full dose, indicating the efficacy of the proposed method. Consequently, we successfully reconstructed DBT images of substantially high quality using the CS-based algorithm and synthesised 2D mammographic images of reasonable quality, potentially reducing the radiation dose to patients in the complementary imaging technologies of DM and DBT.

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Improvement of image performance in digital breast tomosynthesis (DBT) by incorporating a compressed-sensing (CS)-based deblurring scheme

Cited by 3 publications

References 10 publications

Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms

Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms

Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction

Synthesising two-dimensional mammographic images using compressed sensing-reconstructed digital breast tomosynthesis images

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