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However, issues such as loss of structure and edge information and low denoising efficiency still exist.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To improve image denoising quality, an enhanced multi-dimensional hybrid attention LDCT image denoising network based on edge detection is proposed in this paper.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In our network, we employ a trainable Sobel convolution to design an edge enhancement module and fuse an enhanced triplet attention network (ETAN) after each\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                          <mml:mn>3<\/mml:mn>\n                          <mml:mo>\u00d7<\/mml:mo>\n                          <mml:mn>3<\/mml:mn>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      convolutional layer to extract richer features more comprehensively and suppress useless information. During the training process, we adopt a strategy that combines total variation loss (TVLoss) with mean squared error (MSE) loss to reduce high-frequency artifacts in image reconstruction and balance image denoising and detail preservation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared with other advanced algorithms (CT-former, REDCNN and EDCNN), our proposed model achieves the best PSNR and SSIM values in CT image of the abdomen, which are 34.8211and 0.9131, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Through comparative experiments with other related algorithms, it can be seen that the algorithm proposed in this article has achieved significant improvements in both subjective vision and objective indicators.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/08953996241306696","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T13:54:30Z","timestamp":1738158870000},"page":"393-404","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A cross-type multi-dimensional network based on feature enhancement and triple interactive attention for LDCT denoising"],"prefix":"10.1177","volume":"33","author":[{"given":"Lina","family":"Jia","sequence":"first","affiliation":[{"name":"School of Physics and Electronic Engineering, Shanxi University, Taiyuan, China"},{"name":"Shanxi Key Laboratory of Wireless Communication and Detection, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beibei","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic Engineering, Shanxi University, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongyang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic Engineering, Shanxi University, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic Engineering, Shanxi University, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiguo","family":"Gui","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07034-x"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/22221751.2020.1750307"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2986388"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.radi.2021.07.010"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3014721"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06359-6"},{"key":"e_1_3_2_8_2","first-page":"766","article-title":"The application value of 256-slice spiral CT dynamic enhancement and multi-mode post-processing technology in preoperative diagnosis and staging of colorectal cancer","volume":"24","author":"Rong P","year":"2020","unstructured":"Rong P, Jiancheng H, Guoquan H, et al. 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