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Digital tomosynthesis (DTS) is a geometric tomography technique using a limited-angle scan. It has been popularly used in both medical and industrial x-ray imaging applications. DTS provides the tomographic benefits of computed tomography with reduced dose and time. However, conventional DTS reconstruction based on the computationally cheap filtered back-projection (FBP) method typically produces poor image quality due to limited angular samplings. To overcome these difficulties, iterative reconstruction methods are often used in DTS reconstruction as they have the potential to provide multiplanar images of higher quality than conventional FBP-based methods. However, they require enormous computational cost in the iterative process, which remains an obstacle to practical applications. In this study, we propose a method for effectively reducing limited-angle artifacts in conventional FBP reconstruction, using a state-of-the-art deep learning scheme with a convolutional neural network. Our results indicate that the proposed DTS reconstruction method effectively minimized limited-angle artifacts, thus improving image performance in DTS, and that further it provided good image quality in both sagittal and coronal views (as in computed tomography) as well as in axial view.

Computed tomography (CT) images obtained at different monochromatic X-ray beam energies can be synthesized from conventional dual-energy CT scans. This approach to synthesizing monochromatic CT images is based on basis material decomposition and the knowledge of attenuation of basis materials. The main benefits of virtual monochromatic CT (VMCT) images include reduction of beam-hardening artifacts and provision of accurate atteuation measurements. Despite the VMCT's benefits, main concerns in the use of VMCT in clinics may be high radiation dose the patient is exposed to. In this study, we investigated a projection-based sparse-view VMCT method in an attempt to overcome these difficulties. We performed a computational simulation and evaluated the feasibility of using the VMCT method in sparse-view CT. Two polychromatic data sets of 90 projections, far less than what is required by the Nyquist sampling theory, were simulated at 80 kVp and 140 kVp and used to synthesize VMCT images at a monochromatic energy range of 40–140 keV . VMCT image characteristics were quantitatively evaluated in terms of intensity profile, the contrast-to-noise ratio, and the signal-to-noise ratio. Our results indicate that the CS-based algorithm produced high-quality sparse-view CT images, and thereby the proposed VMCT method yielded CT image results of improved beam-hardening artifacts and quantitative measurements.

This study investigated analytic digital tomosynthesis (DTS) reconstruction in partial sampling with a multislit collimator that partially blocks the x-ray beam to the patient during projection data acquisition, thereby reducing excessive radiation dose to patients. Partially-sampled DTS images reconstructed using the analytic filtered-backprojection (FBP) algorithm usually suffer from severe bright-band artifacts around multislit edges of the collimator due to incomplete spatial sampling. In this study, a new prior sinogram interpolation method was introduced to the analytic DTS reconstruction in partial sampling to alleviate such artifacts. To verify the proposed DTS method, we conducted a systematic simulation and investigated image characteristics. Three multislit collimator layouts of C(2/2), C(3/3), and C(4/4) with a 50% duty cycle were designed and used in the simulation. Here C(n/n) denotes a collimator layout that blocks the x-ray beam over n detector pixels vertically with a n-pixel interval. All projections were obtained at a tomographic angle of θ = ±40o and an angle step of Δθ = 2o and used to reconstruct DTS images using the FBP algorithm. Our results indicate that the proposed sinogram interpolation method effectively minimized bright-band artifacts in analytic DTS reconstruction in partial sampling, thus maintaining the image quality.