Literature DB >> 29173918

Evaluation of digital tomosynthesis reconstruction algorithms used to reduce metal artifacts for arthroplasty: A phantom study.

Tsutomu Gomi1, Rina Sakai2, Masami Goto2, Hidetake Hara2, Yusuke Watanabe2, Tokuo Umeda2.   

Abstract

To investigate methods to reduce metal artifacts during digital tomosynthesis for arthroplasty, we evaluated five algorithms with and without metal artifact reduction (MAR)-processing tested under different radiation doses (0.54, 0.47, and 0.33mSv): adaptive steepest descent projection onto convex sets (ASD-POCS), simultaneous algebraic reconstruction technique total variation (SART-TV), filtered back projection (FBP), maximum likelihood expectation maximization (MLEM), and SART. The algorithms were assessed by determining the artifact index (AI) and artifact spread function (ASF) on a prosthesis phantom. The AI data were statistically analyzed by two-way analysis of variance. Without MAR-processing, the greatest degree of effectiveness of the MLEM algorithm for reducing prosthetic phantom-related metal artifacts was achieved by quantification using the AI (MLEM vs. ASD-POCS, SART-TV, SART, and FBP; all P<0.05). With MAR-processing, the greatest degree of effectiveness of the MLEM, ASD-POCS, SART-TV, and SART algorithms for reducing prosthetic phantom-related metal artifacts was achieved by quantification using the AI (MLEM, ASD-POCS, SART-TV, and SART vs. FBP; all P<0.05). When assessed by ASF, metal artifact reduction was largest for the MLEM algorithm without MAR-processing and ASD-POCS, SART-TV, and SART algorithm with MAR-processing. In ASF, the effect of metal artifact reduction was always greater at reduced radiation doses, regardless of which reconstruction algorithm with and without MAR-processing was used. In this phantom study, the MLEM algorithm without MAR-processing and ASD-POCS, SART-TV, and SART algorithm with MAR-processing gave improved metal artifact reduction.
Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Arthroplasty; Metal artifacts; Tomosynthesis

Mesh:

Substances:

Year:  2017        PMID: 29173918     DOI: 10.1016/j.ejmp.2017.07.023

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  4 in total

1.  Accelerated Stimulated Raman Projection Tomography by Sparse Reconstruction From Sparse-View Data.

Authors:  Xueli Chen; Shouping Zhu; Huiyuan Wang; Cuiping Bao; Defu Yang; Chi Zhang; Peng Lin; Ji-Xin Cheng; Yonghua Zhan; Jimin Liang; Jie Tian
Journal:  IEEE Trans Biomed Eng       Date:  2019-08-14       Impact factor: 4.538

2.  Diagnosis of and Early Revision Surgery for Biological Fixation Failure Due to Proximal-Distal Mismatch of Proximally Coated Tapered Cementless Stem.

Authors:  Yasuhiro Homma; So Kawakita; Tomonori Baba; Taiji Watari; Kazuo Kaneko
Journal:  Arthroplast Today       Date:  2020-11-04

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

Authors:  Tsutomu Gomi; Rina Sakai; Hidetake Hara; Yusuke Watanabe; Shinya Mizukami
Journal:  PLoS One       Date:  2019-09-13       Impact factor: 3.240

4.  Evaluation of Patient Positioning during Digital Tomosynthesis and Reconstruction Algorithms for Ilizarov Frames: A Phantom Study.

Authors:  Yuki Abe; Makoto Shimada; Yoshihiro Takeda; Taisuke Enoki; Kumiko Omachi; Shuji Abe
Journal:  Strategies Trauma Limb Reconstr       Date:  2020 Jan-Apr
  4 in total

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