An Iterative Reconstruction Algorithm Based on Detail Transfer for Few-View Computed Tomography

Abstract

Computed Tomography (CT) is widely used in medical diagnosis. Filtered Back Projection (FBP), a traditional analytical method, is commonly used in clinical CT to preserve high-frequency details but introduces streak artifacts in few-view data. In contrast, iterative reconstruction algorithms improve image quality by incorporating accurate models of imaging physics and noise. However, they often require explicit regularization or specialized network architectures, leading to complex optimization challenges. This paper proposes an iterative reconstruction algorithm based on detail transfer (DT), which requires the prior detail information extracted from the FBP-reconstructed image. Specifically, the detail information extracted from the FBP-reconstructed image is combined with the SART reconstruction results using mask-allocated weights to generate the initial value for the iterative reconstruction process. During the iterations, as characteristics of iterative algorithm, the low-frequency information is restored first and high-frequency information is gradually recovered, the extracted detail information is weighted with the iteratively reconstructed image to accelerate the restoration of high-frequency information. This approach speeds up the convergence of the algorithm. The iterative reconstruction algorithm adopts the Simultaneous Algebraic Reconstruction Technique (SART), and thus, the proposed method is referred to as SART-DT. Experimental results show that SART-DT effectively removes artifacts and restores details, offering superior reconstruction quality and better preservation of fine details compared to other methods.