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However, in clinical applications, EAT scan is typically limited to a single view acquisition, leading to severe artifacts in the reconstruction.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n This study aims to develop novel deep\u2010learning models to substantially enhance the image quality of the single\u2010view EAT reconstruction.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We proposed a two\u2010stage deep learning model (EXPP\u2010Net) to address the limited view problem in EAT image reconstruction. The two\u2010stage model comprises extrapolation and post\u2010processing sub\u2010models. The extrapolation sub\u2010model receives a single\u2010view sinogram\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n acquired at any angle\n \u03b1<\/jats:italic>\n and a rotation angle\n \u03b3<\/jats:italic>\n \u2208[\u221260\u00b0, \u221224\u00b0, 24\u00b0, 60\u00b0], and predicts four single\u2010view sinograms at angles\n \u03b1<\/jats:italic>\n +\n \u03b3<\/jats:italic>\n . The 5 sinograms (i.e.,\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n \u201060\u00b0<\/jats:sub>\n ,\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n \u201024\u00b0<\/jats:sub>\n ,\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n ,\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n +24\u00b0<\/jats:sub>\n ,\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n +60\u00b0<\/jats:sub>\n ) are back\u2010projected to reconstruct an initial sparse\u2010view EAT image (\n \n m<\/jats:italic>\n <\/jats:bold>\n sv<\/jats:sub>\n ). The post\u2010processing sub\u2010model enhances\n \n m<\/jats:italic>\n <\/jats:bold>\n sv<\/jats:sub>\n \u2019s quality to a full\u2010view image (\n \n m<\/jats:italic>\n <\/jats:bold>\n fv<\/jats:sub>\n ). These sub\u2010models were trained using experimental data. Using a linear\u2010array probe, 54 full\u2010view datasets (60 or 90 views each) were acquired by delivering electrical pulses via two tungsten electrodes rotated from \u2212180\u00b0 to 180\u00b0 in a water tank. Each view\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n , together with {\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n \u201060\u00b0<\/jats:sub>\n ,\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n \u201024\u00b0<\/jats:sub>\n ,\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n +24\u00b0<\/jats:sub>\n ,\n \n u<\/jats:italic>\n <\/jats:bold>\n \n \u03b1<\/jats:sub>\n <\/jats:italic>\n +60\u00b0<\/jats:sub>\n } and\n \n m<\/jats:italic>\n <\/jats:bold>\n fv<\/jats:sub>\n , forms a view set for model training. Data splitting was performed at the dataset level, yielding 34\/10\/10 datasets for training\/validation\/test, corresponding to 2190\/600\/600 view sets. The model performance was evaluated using root mean square error (RMSE), structural similarity index measure (SSIM), and the iso\u2010pressure line Dice similarity coefficients (DICE). Statistical analysis used a linear mixed\u2010effects model with Benjamini\u2013Hochberg FDR correction (\n \u03b1\u00a0<\/jats:italic>\n =\u00a00.05), and effect size was defined as standardized fixed\u2010effect coefficients (\n \u03b2<\/jats:italic>\n \/\n \u03c3<\/jats:italic>\n ). For comprehensive evaluation, the model was re\u2010trained and evaluated on convex\u2010array probe data, and further tested in vivo on one mouse. The model was also compared with a previously published method to demonstrate its advantages.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The quality of EAT images reconstructed from single\u2010view acquisition was substantially improved by EXPP\u2010Net, showing good agreement with full\u2010view images (linear\u2010array probe study: RMSE: 0.0033\u00a0\u00b1\u00a00.0023, SSIM: 0.9968\u00a0\u00b1\u00a00.0059, median DICE \u2265 0.9450; convex\u2010array probe study: RMSE: 0.0083\u00a0\u00b1\u00a00.0034, SSIM: 0.9958\u00a0\u00b1\u00a00.0038, median DICE \u2265 0.8837). In vivo results demonstrated substantial distortion correction. Comparison with the prior method on RMSE, SSIM, and DICE yielded large effect sizes (|\n \u03b2<\/jats:italic>\n \/\n \u03c3<\/jats:italic>\n |\u00a0>\u00a00.8), with all comparisons reaching statistical significance (\n p<\/jats:italic>\n \u00a0<\u00a00.05).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The proposed model generated full\u2010view EAT imaging from a single\u2010view sinogram, substantially improving the quality and precision of EAT. These results suggest its potential for real\u2010time electroporation verification.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/mp.70623","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T12:56:42Z","timestamp":1786366602000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A single\u2010view\u2010based electroacoustic tomography imaging using deep learning for electroporation monitoring"],"prefix":"10.1002","volume":"53","author":[{"given":"Jie","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Maryland School of Medicine Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of California Irvine California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leshan","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of California Irvine California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yankun","family":"Lang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Maryland School of Medicine Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangzhong","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of California Irvine California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Maryland School of Medicine Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2022.09.009"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0006\u20103495(93)81052\u2010X"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/bjr\/tqae231"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bioelechem.2021.107871"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejso.2018.11.023"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvir.2010.12.014"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2021.03.020"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2021203089"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TUFFC.2019.2955900"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0244192"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1121\/1.5101973"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2512812"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s44172\u2010023\u201000125\u20109"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering12090906"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/pro6.1242"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ad0283"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2015.00036"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010021\u201087482\u20107"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15958"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13121"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICAEE47123.2019.9015130"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.221257"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2021.3119273"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3252576"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20201329"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3351722"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.375595"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17294"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3101363"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.3233\/XST\u20102006\u201000155"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3072568"},{"key":"e_1_2_8_33_1","doi-asserted-by":"crossref","unstructured":"YooS YangX WolfmanM GursoyD KatsaggelosAK Sinogram image completion for limited angle tomography with generative adversarial networks. 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