Cargando…
Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase
Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrades the quality of CT-based diagnosis. Traditional approaches such as high-resolution computed tomography (HRCT) and linear interpolation can solve thi...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754169/ https://www.ncbi.nlm.nih.gov/pubmed/36520814 http://dx.doi.org/10.1371/journal.pone.0279005 |
_version_ | 1784851123966836736 |
---|---|
author | Wu, Shuqiong Nakao, Megumi Imanishi, Keiho Nakamura, Mitsuhiro Mizowaki, Takashi Matsuda, Tetsuya |
author_facet | Wu, Shuqiong Nakao, Megumi Imanishi, Keiho Nakamura, Mitsuhiro Mizowaki, Takashi Matsuda, Tetsuya |
author_sort | Wu, Shuqiong |
collection | PubMed |
description | Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrades the quality of CT-based diagnosis. Traditional approaches such as high-resolution computed tomography (HRCT) and linear interpolation can solve this problem. However, HRCT suffers from dose increase, and linear interpolation causes artifacts. In this study, we propose a deep-learning-based approach to reconstruct densely sliced CT from sparsely sliced CT data without any dose increase. The proposed method reconstructs CT images from neighboring slices using a U-net architecture. To prevent multiple reconstructed slices from influencing one another, we propose a parallel architecture in which multiple U-net architectures work independently. Moreover, for a specific organ (i.e., the liver), we propose a range-clip technique to improve reconstruction quality, which enhances the learning of CT values within this organ by enlarging the range of the training data. CT data from 130 patients were collected, with 80% used for training and the remaining 20% used for testing. Experiments showed that our parallel U-net architecture reduced the mean absolute error of CT values in the reconstructed slices by 22.05%, and also reduced the incidence of artifacts around the boundaries of target organs, compared with linear interpolation. Further improvements of 15.12%, 11.04%, 10.94%, and 10.63% were achieved for the liver, left kidney, right kidney, and stomach, respectively, using the proposed range-clip algorithm. Also, we compared the proposed architecture with original U-net method, and the experimental results demonstrated the superiority of our approach. |
format | Online Article Text |
id | pubmed-9754169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-97541692022-12-16 Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase Wu, Shuqiong Nakao, Megumi Imanishi, Keiho Nakamura, Mitsuhiro Mizowaki, Takashi Matsuda, Tetsuya PLoS One Research Article Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrades the quality of CT-based diagnosis. Traditional approaches such as high-resolution computed tomography (HRCT) and linear interpolation can solve this problem. However, HRCT suffers from dose increase, and linear interpolation causes artifacts. In this study, we propose a deep-learning-based approach to reconstruct densely sliced CT from sparsely sliced CT data without any dose increase. The proposed method reconstructs CT images from neighboring slices using a U-net architecture. To prevent multiple reconstructed slices from influencing one another, we propose a parallel architecture in which multiple U-net architectures work independently. Moreover, for a specific organ (i.e., the liver), we propose a range-clip technique to improve reconstruction quality, which enhances the learning of CT values within this organ by enlarging the range of the training data. CT data from 130 patients were collected, with 80% used for training and the remaining 20% used for testing. Experiments showed that our parallel U-net architecture reduced the mean absolute error of CT values in the reconstructed slices by 22.05%, and also reduced the incidence of artifacts around the boundaries of target organs, compared with linear interpolation. Further improvements of 15.12%, 11.04%, 10.94%, and 10.63% were achieved for the liver, left kidney, right kidney, and stomach, respectively, using the proposed range-clip algorithm. Also, we compared the proposed architecture with original U-net method, and the experimental results demonstrated the superiority of our approach. Public Library of Science 2022-12-15 /pmc/articles/PMC9754169/ /pubmed/36520814 http://dx.doi.org/10.1371/journal.pone.0279005 Text en © 2022 Wu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wu, Shuqiong Nakao, Megumi Imanishi, Keiho Nakamura, Mitsuhiro Mizowaki, Takashi Matsuda, Tetsuya Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase |
title | Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase |
title_full | Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase |
title_fullStr | Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase |
title_full_unstemmed | Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase |
title_short | Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase |
title_sort | computed tomography slice interpolation in the longitudinal direction based on deep learning techniques: to reduce slice thickness or slice increment without dose increase |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754169/ https://www.ncbi.nlm.nih.gov/pubmed/36520814 http://dx.doi.org/10.1371/journal.pone.0279005 |
work_keys_str_mv | AT wushuqiong computedtomographysliceinterpolationinthelongitudinaldirectionbasedondeeplearningtechniquestoreduceslicethicknessorsliceincrementwithoutdoseincrease AT nakaomegumi computedtomographysliceinterpolationinthelongitudinaldirectionbasedondeeplearningtechniquestoreduceslicethicknessorsliceincrementwithoutdoseincrease AT imanishikeiho computedtomographysliceinterpolationinthelongitudinaldirectionbasedondeeplearningtechniquestoreduceslicethicknessorsliceincrementwithoutdoseincrease AT nakamuramitsuhiro computedtomographysliceinterpolationinthelongitudinaldirectionbasedondeeplearningtechniquestoreduceslicethicknessorsliceincrementwithoutdoseincrease AT mizowakitakashi computedtomographysliceinterpolationinthelongitudinaldirectionbasedondeeplearningtechniquestoreduceslicethicknessorsliceincrementwithoutdoseincrease AT matsudatetsuya computedtomographysliceinterpolationinthelongitudinaldirectionbasedondeeplearningtechniquestoreduceslicethicknessorsliceincrementwithoutdoseincrease |