The work presented provides an objective way to quantitatively assess the image quality of a newly introduced CT IR algorithm. The performance of the model observers using the IR images was always higher than that seen using the FBP images in the authors' SKE and SKE location unknown detection tasks. To achieve a FBP-equivalent image quality in CT systems, the authors can lower the radiation dose by using this IR image reconstruction algorithm. Further studies are warranted using clinical data and human observer to validate these results for more complicated and realistic tasks.

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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.

Background: High-resolution, low-noise detectors with minimal dead-space at chest-wall could improve posterior coverage and microcalcification visibility in dedicated cone-beam breast CT (CBBCT). However, their smaller field-of-view necessitates laterally-shifted detector geometry to enable optimizing the air-gap for x-ray scatter rejection. Objective: To evaluate laterally-shifted detector geometry for CBBCT with clinical projection datasets that provide for anatomical structures and lesions. Methods: CBBCT projection datasets (n=17 breasts) acquired with a 40x30-cm detector (1024x768-pixels, 0.388-mm pixels) were truncated along the fan-angle to emulate 20.3x30-cm, 22.2x30-cm and 24.1x30-cm detector formats and correspond to 20, 120, 220-pixels overlap in conjugate views, respectively. Feldkamp-Davis-Kress (FDK) algorithm with three different weighting schemes were used for reconstruction. Visual analysis for artifacts and quantitative analysis of root-mean-squared-error (RMSE), absolute difference between truncated and 40x30cm reconstructions (Diff), and its power spectrum (PS Diff) were performed. Results: Artifacts were observed for 20.3x30-cm, but not for other formats. The 24.1x30-cm provided the best quantitative results with RMSE and Diff (both in units of μ, cm-1) of 4.39x10-3 ±1.98x10-3 and 4.95x10-4 ±1.34x10-4 , respectively. The PS Diff (>0.3 cycles/mm) was in the order of 10-14 μ 2 mm 3 and was spatial-frequency independent. Conclusions: Laterally-shifted detector CBBCT with at least 220-pixels overlap in conjugate views (24.1x30-cm detector format), provides quantitatively accurate and artifact-free reconstruction.

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.

Design of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems," J. Med. Imag. 3(3), 035503 (2016), doi: 10.1117/1.JMI.3.3.035503.Design of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems Abstract. The use of a channelization mechanism on model observers not only makes mimicking human visual behavior possible, but also reduces the amount of image data needed to estimate the model observer parameters. The channelized Hotelling observer (CHO) and channelized scanning linear observer (CSLO) have recently been used to assess CT image quality for detection tasks and combined detection/estimation tasks, respectively. Although the use of channels substantially reduces the amount of data required to compute image quality, the number of scans required for CT imaging is still not practical for routine use. It is our desire to further reduce the number of scans required to make CHO or CSLO an image quality tool for routine and frequent system validations and evaluations. This work explores different data-reduction schemes and designs an approach that requires only a few CT scans. Three different kinds of approaches are included in this study: a conventional CHO/CSLO technique with a large sample size, a conventional CHO/CSLO technique with fewer samples, and an approach that we will show requires fewer samples to mimic conventional performance with a large sample size. The mean value and standard deviation of areas under ROC/EROC curve were estimated using the well-validated shuffle approach. The results indicate that an 80% data reduction can be achieved without loss of accuracy. This substantial data reduction is a step toward a practical tool for routine-task-based QA/QC CT system assessment.

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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