Abstract
The present study aimed to develop a denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm for decreasing metal objects in digital tomosynthesis (DT) for arthroplasty by using projection data. For metal artifact reduction (MAR), we implemented a DnCNN-MARHR algorithm based on a training network (mini-batch stochastic gradient descent algorithm with momentum) to estimate the residual reference (140 keV virtual monochromatic [VM]) and object (70 kV with metal artifacts) images. For this, we used projection data and subtracted the estimated residual images from the object images, involving hybrid and subjectively reconstructed image usage (back projection and maximum likelihood expectation maximization [MLEM]). The DnCNN-MARHR algorithm was compared with the dual-energy material decomposition reconstruction algorithm (DEMDRA), VM, MLEM, established and commonly used filtered back projection (FBP), and a simultaneous algebraic reconstruction technique-total variation (SART-TV) with MAR processing. MAR was compared using artifact index (AI) and texture analysis. Artifact spread functions (ASFs) for images that were out-of-plane and in-focus were evaluated using a prosthesis phantom. The overall performance of the DnCNN-MARHR algorithm was adequate with regard to the ASF, and the derived images showed better results, without being influenced by the metal type (AI was almost equal to the best value for the DEMDRA). In the ASF analysis, the DnCNN-MARHR algorithm generated better MAR compared with that obtained employing usual algorithms for reconstruction using MAR processing. In addition, comparison of the difference (mean square error) between DnCNN-MARHR and the conventional algorithm resulted in the smallest VM. The DnCNN-MARHR algorithm showed the best performance with regard to image homogeneity in the texture analysis. The proposed algorithm is particularly useful for reducing artifacts in the longitudinal direction, and it is not affected by tissue misclassification.
Highlights
Cementless hip arthroplasty has gained more popularity in clinic, recently
We developed a hybrid method of reconstruction that is based on projection space approach by combining the DnCNN and adaptive filtering [15] with a focus on reducing metal artifacts (DnCNN metal artifact reduction (MAR) hybrid reconstruction [DnCNN-MARHR] algorithm) in Digital tomosynthesis (DT)
Images produced with the help of filtered back projection (FBP)-MAR demonstrated more noise and metal artifacts
Summary
Cementless hip arthroplasty has gained more popularity in clinic, recently. It is essential that biological fixation procedures employed are reliable for achieving success with this technique [1]. Medical imaging plays an important role for assessing the proper placement of the components of hip arthroplasty, postoperatively, and to evaluate the potential complications in the long-term [2]. Digital tomosynthesis (DT), a recently developed technique, provides threedimensional (3D) structural information to a limited extent, by combining computed tomography (CT) with the advantages of digital imaging [1,2,3,4,5,6,7,8], and another advantage of DT is that it can be employed with radiography, it can help reducing the radiation doses. It is necessary to ascertain that there is no hematoma or inflammation in tissues surrounding target area and to evaluate any potential interaction of osteosynthetic materials, metallic joint prostheses or implants with nearby tissues and radiation
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