Cone-beam breast computed tomography using ultra-fast image reconstruction with constrained, total-variation minimization for suppression of artifacts

Tseng, Hsin Wu; Vedantham, Srinivasan; Karellas, Andrew

doi:10.1016/j.ejmp.2020.04.020

Cited by 13 publications

(25 citation statements)

References 48 publications

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“…The extended FOV could be of benefit for evaluating the lymph nodes in the lower axillary region, 3 but needs to be demonstrated through a clinical trial. Evaluation of reconstruction algorithms appropriate for this geometry is in progress 33,35,45 …”

Section: Methodsmentioning

confidence: 99%

Radiation dosimetry of a clinical prototype dedicated cone‐beam breast CT system with offset detector

2021

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Purpose A clinical‐prototype, dedicated, cone‐beam breast computed tomography (CBBCT) system with offset detector is undergoing clinical evaluation at our institution. This study is to estimate the normalized glandular dose coefficients (DgNCT) that provide air kerma‐to‐mean glandular dose conversion factors using Monte Carlo simulations. Materials and methods The clinical prototype CBBCT system uses 49 kV x‐ray spectrum with 1.39 mm 1st half‐value layer thickness. Monte Carlo simulations (GATE, version 8) were performed with semi‐ellipsoidal, homogeneous breasts of various fibroglandular weight fractions (fg=0.01,0.15,0.5,1false), chest wall diameters (d=8,10,14,18,20 cm), and chest wall to nipple length (l=0.75d), aligned with the axis of rotation (AOR) located at 65 cm from the focal spot to determine the DgNCT. Three geometries were considered – 40×30‐cm detector with no offset that served as reference and corresponds to a clinical CBBCT system, 30×30‐cm detector with 5 cm offset, and a 30×30‐cm detector with 10 cm offset. Results For 5 cm lateral offset, the DgNCT ranged 0.177‐0.574 mGy/mGy and reduction in DgNCT with respect to reference geometry was observed only for 18 cm (6.4%±0.23%) and 20 cm (9.6%±0.22%) diameter breasts. For the 10 cm lateral offset, the DgNCT ranged 0.221‐0.581 mGy/mGy and reduction in DgNCT was observed for all breast diameters. The reduction in DgNCT was 1.4%±0.48%, 7.1%±0.13%, 17.5%±0.19%, 25.1%±0.15%, and 27.7%±0.08% for 8, 10, 14, 18, and 20 cm diameter breasts, respectively. For a given breast diameter, the reduction in DgNCT with offset‐detector geometries was not dependent on fg. Numerical fits of italicDgNitalicCTd,l,fg were generated for each geometry. Conclusion The DgNCT and the numerical fit, DgNitalicCTd,l,fg would be of benefit for current CBBCT systems using the reference geometry and for future generations using offset‐detector geometry. There exists a potential for radiation dose reduction with offset‐detector geometry, provided the same technique factors as the reference geometry are used, and the image quality is clinically acceptable.

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Section: Methodsmentioning

confidence: 99%

Radiation dosimetry of a clinical prototype dedicated cone‐beam breast CT system with offset detector

2021

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“…The fast, iterative, tv-regularized, statistical reconstruction technique (FIRST 22 ) was also used for sparse-view image reconstruction. This algorithm is an ultra-fast variant of the adaptive steepest descent-projection on to convex sets (ASD-POCS 72 ) and has been shown to suppress additional artifacts on the periphery of the object.…”

Section: Methodsmentioning

confidence: 99%

“…This study is complementary to ongoing hardware-oriented research. Although, statistical iterative reconstruction 20 – 22 and denoising techniques 23 have been investigated for BCT, all BCT systems currently use Feldkamp–Davis–Kress (FDK) reconstruction 24 . Deep learning based image reconstruction has not been investigated in the context of BCT or for cone-beam CT; however, it has been explored for conventional multi-detector CT 25 – 28 .…”

Section: Introductionmentioning

confidence: 99%

A residual dense network assisted sparse view reconstruction for breast computed tomography

Tseng

Vedantham

et al. 2020

Sci Rep

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To develop and investigate a deep learning approach that uses sparse-view acquisition in dedicated breast computed tomography for radiation dose reduction, we propose a framework that combines 3D sparse-view cone-beam acquisition with a multi-slice residual dense network (MS-RDN) reconstruction. Projection datasets (300 views, full-scan) from 34 women were reconstructed using the FDK algorithm and served as reference. Sparse-view (100 views, full-scan) projection data were reconstructed using the FDK algorithm. The proposed MS-RDN uses the sparse-view and reference FDK reconstructions as input and label, respectively. Our MS-RDN evaluated with respect to fully sampled FDK reference yields superior performance, quantitatively and visually, compared to conventional compressed sensing methods and state-of-the-art deep learning based methods. The proposed deep learning driven framework can potentially enable low dose breast CT imaging.

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“…The radiation dose to the breast in diagnostic setting from this cone-beam BCT system is approximately similar to diagnostic mammography, but is twice the limit from standard 2-view screening mammogram for a breast with an assumed average thickness and density (Vedantham et al 2013c). The radiation dose to the breast can be reduced in a number of ways; including optimization of the technique factors used for image acquisition (Hernandez et al 2020), by using beam shaping filters (Luck et al 2013, Vedantham et al 2015b and with novel image reconstruction techniques (Bian et al 2014, Tseng et al 2020.…”

Section: Introductionmentioning

confidence: 96%

Sparse-view, short-scan, dedicated cone-beam breast computed tomography: image quality assessment

Tseng

Karellas

Vedantham

2020

Biomed. Phys. Eng. Express

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The purpose of this study is to quantify the impact of sparse-view acquisition in short-scan trajectories, compared to 360-degrees full-scan acquisition, on image quality measures in dedicated cone-beam breast computed tomography (BCT). Projection data from 30 full-scan (360-degrees; 300 views) BCT exams with calcified lesions were selected from an existing clinical research database. Feldkamp-Davis-Kress (FDK) reconstruction of the full-scan data served as the reference. Projection data corresponding to two short-scan trajectories, 204 and 270-degrees, which correspond to the minimum and maximum angular range achievable in a cone-beam BCT system were selected. Projection data were retrospectively sampled to provide 225, 180, and 168 views for 270-degrees short-scan, and 170 views for 204-degrees short-scan. Short-scans with 180 and 168 views in 270-degrees used non-uniform angular sampling. A fast, iterative, total variation-regularized, statistical reconstruction technique (FIRST) was used for short-scan image reconstruction. Image quality was quantified by variance, signal-difference to noise ratio (SDNR) between adipose and fibroglandular tissues, full-width at half-maximum (FWHM) of calcifications in two orthogonal directions, as well as, bias and root-mean-squared-error (RMSE) computed with respect to the reference full-scan FDK reconstruction. The median values of bias (8.6 × 10−4–10.3 × 10−4 cm−1) and RMSE (6.8 × 10−6–9.8 × 10−6 cm−1) in the short-scan reconstructions, computed with the full-scan FDK as the reference were close to, but not zero (P < 0.0001, one-sample median test). The FWHM of the calcifications in the short-scan reconstructions did not differ significantly from the reference FDK reconstruction (P > 0.118), except along the superior-inferior direction for the short-scan reconstruction with 168 views in 270-degrees (P = 0.046). The variance and SDNR from short-scan reconstructions were significantly improved compared to the full-scan FDK reconstruction (P < 0.0001). This study demonstrates the feasibility of the short-scan, sparse-view, compressed sensing-based iterative reconstruction. This study indicates that shorter scan times and reduced radiation dose without sacrificing image quality are potentially feasible.

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Cone-beam breast computed tomography using ultra-fast image reconstruction with constrained, total-variation minimization for suppression of artifacts

Cited by 13 publications

References 48 publications

Radiation dosimetry of a clinical prototype dedicated cone‐beam breast CT system with offset detector

Radiation dosimetry of a clinical prototype dedicated cone‐beam breast CT system with offset detector

A residual dense network assisted sparse view reconstruction for breast computed tomography

Sparse-view, short-scan, dedicated cone-beam breast computed tomography: image quality assessment

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