Development of a denoising convolutional neural network-based algorithm for metal artifact reduction in digital tomosynthesis for arthroplasty: A phantom study

English version

Autoři: Tsutomu Gomi ^aff001; Rina Sakai ^aff001; Hidetake Hara ^aff001; Yusuke Watanabe ^aff001; Shinya Mizukami ^aff001
Působiště autorů: School of Allied Health Sciences, Kitasato University, Sagamihara, Kanagawa, Japan ^aff001
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222406

Souhrn

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.

Klíčová slova:

Physical sciences – Mathematics – Applied mathematics – Algorithms – Research and analysis methods – Simulation and modeling – Imaging techniques – Bone imaging – X-ray radiography – Mathematical and statistical techniques – Mathematical functions – Engineering and technology – Signal processing – Image processing – Assistive technologies – Prosthetics – Medicine and health sciences – Diagnostic medicine – Diagnostic radiology – Radiology and imaging – Surgical and invasive medical procedures – Musculoskeletal system procedures – Arthroplasty – Biology and life sciences – Bioengineering – Biotechnology – Medical devices and equipment – Neuroscience – Neural networks – Computer and information sciences

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Development of a denoising convolutional neural network-based algorithm for metal artifact reduction in digital tomosynthesis for arthroplasty: A phantom study

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