{"id":26574,"date":"2024-02-26T07:56:11","date_gmt":"2024-02-26T08:56:11","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%a8%d8%b1%d8%b1%d8%b3%db%8c-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-%d9%88%db%8c%d8%af%db%8c%d9%88%d9%87%d8%a7%db%8c-%d8%b7%d8%a8%db%8c%d8%b9%db%8c\/"},"modified":"2024-02-26T07:56:11","modified_gmt":"2024-02-26T08:56:11","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%a8%d8%b1%d8%b1%d8%b3%db%8c-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-%d9%88%db%8c%d8%af%db%8c%d9%88%d9%87%d8%a7%db%8c-%d8%b7%d8%a8%db%8c%d8%b9%db%8c","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%a8%d8%b1%d8%b1%d8%b3%db%8c-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-%d9%88%db%8c%d8%af%db%8c%d9%88%d9%87%d8%a7%db%8c-%d8%b7%d8%a8%db%8c%d8%b9%db%8c\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 \u0628\u0631\u0631\u0633\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0648\u06cc\u062f\u06cc\u0648\u0647\u0627\u06cc \u0637\u0628\u06cc\u0639\u06cc \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u0639\u0645\u0648\u0645\u06cc \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u062a\u0635\u0648\u06cc\u0631 MR \u067e\u0648\u06cc\u0627"},"content":{"rendered":"<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>Investigating the use of publicly available natural videos to learn Dynamic MR image reconstruction<\/td>\n<\/tr>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n<td>\u0645\u0642\u0627\u0644\u0647 \u0628\u0631\u0631\u0633\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0648\u06cc\u062f\u06cc\u0648\u0647\u0627\u06cc \u0637\u0628\u06cc\u0639\u06cc \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u0639\u0645\u0648\u0645\u06cc \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u062a\u0635\u0648\u06cc\u0631 MR \u067e\u0648\u06cc\u0627<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n<td>Olivier Jaubert, Michele Pascale, Javier Montalt-Tordera, Julius Akesson, Ruta Virsinskaite, Daniel Knight, Simon Arridge, Jennifer Steeden, Vivek Muthurangu<\/td>\n<\/tr>\n<tr>\n<td>\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n<td>\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\n<td>PDF<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n<td>0<\/td>\n<\/tr>\n<tr>\n<td>\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a  <\/td>\n<td>Image and Video Processing,Computer Vision and Pattern Recognition,\u067e\u0631\u062f\u0627\u0632\u0634 \u062a\u0635\u0648\u06cc\u0631 \u0648 \u0648\u06cc\u062f\u06cc\u0648 , \u0686\u0634\u0645 \u0627\u0646\u062f\u0627\u0632 \u0631\u0627\u06cc\u0627\u0646\u0647 \u0648 \u062a\u0634\u062e\u06cc\u0635 \u0627\u0644\u06af\u0648\u06cc ,<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a    <\/td>\n<td>Submitted 23 November, 2023; originally announced November 2023.<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc    <\/td>\n<td>\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 23 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n<p style=\"direction:ltr;\">Purpose: To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Materials and Methods: Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian, radial, spiral) either from true multi-coil cardiac MR data (N=692) or from pseudo-MR data simulated from Inter4K natural videos (N=692). Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS). Differences were assessed in simulations (N=104 datasets) in terms of MSE, PSNR, and SSIM and prospectively for cardiac (short axis, four chambers, N=20) and speech (N=10) data in terms of subjective image quality ranking, SNR and Edge sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were performed to assess statistical significance. Results: For all simulation metrics, DL networks trained with cardiac data outperformed DL networks trained with natural videos, which outperformed CS (p<0.05). However, in prospective experiments DL reconstructions using both training datasets were ranked similarly (and higher than CS) and presented no statistical differences in SNR and Edge Sharpness for most conditions. Additionally, high SSIM was measured between the DL methods with cardiac data and natural videos (SSIM>0.85). Conclusion: The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). The natural video dataset, code and pre-trained networks are made readily available on github. Key Words: real-time; dynamic MRI; deep learning; image reconstruction; machine learning;<\/p>\n<h2>\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n<p>\u0647\u062f\u0641: \u0628\u0631\u0627\u06cc \u062a\u0648\u0633\u0639\u0647 \u0648 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u062e\u0637 \u0644\u0648\u0644\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 (DL) \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u062a\u0635\u0648\u06cc\u0631 \u067e\u0648\u06cc\u0627 MR \u0627\u0632 \u0641\u06cc\u0644\u0645 \u0647\u0627\u06cc \u0637\u0628\u06cc\u0639\u06cc \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u0639\u0645\u0648\u0645\u06cc (Inter4K).\u0645\u0648\u0627\u062f \u0648 \u0631\u0648\u0634\u0647\u0627: \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0628\u0631\u0627\u06cc \u0637\u06cc\u0641 \u0648\u0633\u06cc\u0639\u06cc \u0627\u0632 \u0645\u0639\u0645\u0627\u0631\u06cc \u0647\u0627\u06cc DL (Varnet \u060c Unet \u0633\u0647 \u0628\u0639\u062f\u06cc \u060c FastDVDNET) \u0648 \u0627\u0644\u06af\u0648\u0647\u0627\u06cc \u0646\u0645\u0648\u0646\u0647 \u0628\u0631\u062f\u0627\u0631\u06cc \u0645\u0631\u0628\u0648\u0637\u0647 (\u062f\u06a9\u0627\u0631\u062a\u06cc \u060c \u0634\u0639\u0627\u0639\u06cc \u060c \u0645\u0627\u0631\u067e\u06cc\u0686) \u06cc\u0627 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc MR \u0642\u0644\u0628\u06cc \u0686\u0646\u062f \u0633\u06cc\u0645 \u067e\u06cc\u0686 (692 \u0646\u0641\u0631) \u06cc\u0627 \u0627\u0632 \u0634\u0628\u0647 \u0634\u0628\u0647 \u0627\u0646\u062c\u0627\u0645 \u0634\u062f.\u062f\u0627\u062f\u0647 \u0647\u0627\u06cc MR \u0627\u0632 \u0641\u06cc\u0644\u0645 \u0647\u0627\u06cc \u0637\u0628\u06cc\u0639\u06cc Inter4K \u0634\u0628\u06cc\u0647 \u0633\u0627\u0632\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a (692 = n).\u062a\u0635\u0627\u0648\u06cc\u0631 MR \u067e\u0648\u06cc\u0627 \u062f\u0631 \u0632\u0645\u0627\u0646 \u0648\u0627\u0642\u0639\u06cc \u062f\u0631 \u0632\u0645\u0627\u0646 \u0648\u0627\u0642\u0639\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc DL \u06a9\u0647 \u0628\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0642\u0644\u0628\u06cc \u0648 \u0641\u06cc\u0644\u0645 \u0647\u0627\u06cc \u0637\u0628\u06cc\u0639\u06cc \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0648 \u0633\u0646\u062c\u0634 \u0641\u0634\u0631\u062f\u0647 \u0634\u062f\u0647 (CS) \u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u0634\u062f\u0646\u062f.\u0627\u062e\u062a\u0644\u0627\u0641\u0627\u062a \u062f\u0631 \u0634\u0628\u06cc\u0647 \u0633\u0627\u0632\u06cc (104 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647) \u0627\u0632 \u0646\u0638\u0631 MSE \u060c PSNR \u0648 SSIM \u0648 \u0628\u0647 \u0635\u0648\u0631\u062a \u0622\u06cc\u0646\u062f\u0647 \u0646\u06af\u0631 \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0642\u0644\u0628\u06cc (\u0645\u062d\u0648\u0631 \u06a9\u0648\u062a\u0627\u0647 \u060c \u0686\u0647\u0627\u0631 \u0627\u062a\u0627\u0642 \u060c 20 \u0646\u0641\u0631) \u0648 \u06af\u0641\u062a\u0627\u0631 (10 \u0646\u0641\u0631) \u0627\u0632 \u0646\u0638\u0631 \u0631\u062a\u0628\u0647 \u0628\u0646\u062f\u06cc \u06a9\u06cc\u0641\u06cc\u062a \u062a\u0635\u0648\u06cc\u0631 \u0630\u0647\u0646\u06cc \u0645\u0648\u0631\u062f \u0628\u0631\u0631\u0633\u06cc \u0642\u0631\u0627\u0631 \u06af\u0631\u0641\u062a.\u060c \u0648\u0636\u0648\u062d SNR \u0648 \u0644\u0628\u0647.\u0622\u0632\u0645\u0648\u0646 \u0647\u0627\u06cc \u0645\u06cc\u062f\u0627\u0646 \u0641\u0631\u06cc\u062f\u0645\u0646 \u0686\u06cc \u0628\u0627 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0646\u0645\u0646\u0647\u06cc \u067e\u0633 \u0627\u0632 \u062a\u0639\u0642\u06cc\u0644\u0647 \u0628\u0631\u0627\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0627\u0647\u0645\u06cc\u062a \u0622\u0645\u0627\u0631\u06cc \u0627\u0646\u062c\u0627\u0645 \u0634\u062f.\u06cc\u0627\u0641\u062a\u0647 \u0647\u0627: \u0628\u0631\u0627\u06cc \u06a9\u0644\u06cc\u0647 \u0645\u0639\u06cc\u0627\u0631\u0647\u0627\u06cc \u0634\u0628\u06cc\u0647 \u0633\u0627\u0632\u06cc \u060c \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc DL \u06a9\u0647 \u0628\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0642\u0644\u0628\u06cc \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0646\u062f \u0627\u0632 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc DL \u06a9\u0647 \u0628\u0627 \u0641\u06cc\u0644\u0645 \u0647\u0627\u06cc \u0637\u0628\u06cc\u0639\u06cc \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0646\u062f \u060c \u06a9\u0647 \u0627\u0632 CS \u0628\u0647\u062a\u0631 \u0627\u0633\u062a (P <0.05).\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644 \u060c \u062f\u0631 \u0622\u0632\u0645\u0627\u06cc\u0634\u0627\u062a \u0622\u06cc\u0646\u062f\u0647 \u0646\u06af\u0631 \u0628\u0627\u0632\u0633\u0627\u0632\u06cc DL \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0647\u0631 \u062f\u0648 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0628\u0647 \u0637\u0648\u0631 \u0645\u0634\u0627\u0628\u0647 (\u0648 \u0628\u0627\u0644\u0627\u062a\u0631 \u0627\u0632 CS) \u0642\u0631\u0627\u0631 \u06af\u0631\u0641\u062a\u0646\u062f \u0648 \u0647\u06cc\u0686 \u062a\u0641\u0627\u0648\u062a \u0622\u0645\u0627\u0631\u06cc \u062f\u0631 SNR \u0648 \u0648\u0636\u0648\u062d \u0644\u0628\u0647 \u0628\u0631\u0627\u06cc \u0627\u06a9\u062b\u0631 \u0634\u0631\u0627\u06cc\u0637 \u0627\u0631\u0627\u0626\u0647 \u0646\u06a9\u0631\u062f\u0646\u062f.\u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646 \u060c SSIM \u0628\u0627\u0644\u0627 \u0628\u06cc\u0646 \u0631\u0648\u0634\u0647\u0627\u06cc DL \u0628\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0642\u0644\u0628\u06cc \u0648 \u0641\u06cc\u0644\u0645 \u0647\u0627\u06cc \u0637\u0628\u06cc\u0639\u06cc \u0627\u0646\u062f\u0627\u0632\u0647 \u06af\u06cc\u0631\u06cc \u0634\u062f (SSIM> 0.85).\u0646\u062a\u064a\u062c\u0647 \u06af\u064a\u0631\u064a: \u062e\u0637 \u0644\u0648\u0644\u0647 \u062a\u0648\u0633\u0639\u0647 \u06cc\u0627\u0641\u062a\u0647 \u060c \u064a\u0627\u062f\u06af\u064a\u0631\u064a \u0631\u0627 \u0641\u0639\u0627\u0644\u064a\u062a \u0645\u064a \u0643\u0646\u062f \u060c \u0628\u0627\u0632\u0633\u0627\u0632\u06cc Dynamic MR \u0627\u0632 \u0641\u06cc\u0644\u0645 \u0647\u0627\u064a \u0637\u0628\u064a\u0639\u064a \u0643\u0647 \u0645\u0632\u0627\u06cc\u0627\u06cc \u0628\u0627\u0632\u0633\u0627\u0632\u06cc DL \u0631\u0627 \u062d\u0641\u0638 \u0645\u064a \u0643\u0646\u0646\u062f \u0645\u0627\u0646\u0646\u062f \u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u0647\u0627\u064a \u0633\u0631\u06cc\u0639 \u0648 \u0641\u0648\u0642 \u0627\u0644\u0639\u0627\u062f\u0647 \u0628\u0627 \u0643\u064a\u0641\u064a\u062a \u0628\u0627\u0644\u0627 \u0636\u0645\u0646 \u063a\u0644\u0628\u0647 \u0628\u0631 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0645\u062d\u062f\u0648\u062f\u06cc\u062a \u0647\u0627 (\u0643\u0645\u0628\u0648\u062f \u062f\u0627\u062f\u0647 \u0647\u0627 \u064a\u0627 \u0628\u0647 \u0627\u0634\u062a\u0631\u0627\u06a9 \u06af\u0630\u0627\u0631\u06cc).\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0648\u06cc\u062f\u06cc\u0648\u06cc\u06cc \u0637\u0628\u06cc\u0639\u06cc \u060c \u06a9\u062f \u0648 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0627\u0632 \u0642\u0628\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0628\u0647 \u0631\u0627\u062d\u062a\u06cc \u062f\u0631 GitHub \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u0647\u0633\u062a\u0646\u062f.\u06a9\u0644\u0645\u0627\u062a \u06a9\u0644\u06cc\u062f\u06cc: \u0632\u0645\u0627\u0646 \u0648\u0627\u0642\u0639\u06cc \u061bMRI \u067e\u0648\u06cc\u0627 \u061b\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\u061b\u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u062a\u0635\u0648\u06cc\u0631 \u061b\u0641\u0631\u0627\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646\u061b<\/p>\n<table class=\"table table-bordered table-primary\">\r\n<tr>\r\n\t<td>\r\n\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0632\u0628\u0627\u0646 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u0627\u0633\u062a.\r\n\t<\/td>\r\n<\/tr>\r\n\t\r\n<tr>\r\n\t<td>\r\n     \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u062a\u0631\u062c\u0645\u0647 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0648\u0634 \u0647\u0627\u06cc \u062a\u0645\u0627\u0633\u060c \u067e\u06cc\u0627\u0645\u06a9\u060c \u062a\u0644\u06af\u0631\u0627\u0645 \u0648 \u06cc\u0627 \u0648\u0627\u062a\u0633 \u0627\u067e \u0628\u0627 \u0634\u0645\u0627\u0631\u0647 \u0632\u06cc\u0631 \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f:\r\n\t<br \/><br \/>\r\n\t09395106248\r\n\t\t\r\n\t\t\r\n\t<br \/><br \/>\r\n\t\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0634\u0631\u0627\u06cc\u0637 \u062a\u0631\u062c\u0645\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0627\u0633\u062a:\r\n\t\t<br \/>\r\n\t\t<ul>\r\n\t\t<li>\r\n\t\t\u0642\u06cc\u0645\u062a \u0647\u0631 \u0635\u0641\u062d\u0647 \u062a\u0631\u062c\u0645\u0647 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 40 \u0647\u0632\u0627\u0631 \u062a\u0648\u0645\u0627\u0646 \u0645\u06cc \u0628\u0627\u0634\u062f. \r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u062a\u062d\u0648\u06cc\u0644 \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f \u0645\u06cc \u0628\u0627\u0634\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0635\u0648\u0631\u062a \u062f\u0627\u0634\u062a\u0646 \u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0639\u0627\u062f\u06cc \u0628\u06cc\u0646 3 \u062a\u0627 5 \u0631\u0648\u0632 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t\r\n\t\t<li>\r\n\t\t\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0627\u0634\u062f. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.\r\n\t\t<\/li>\r\n\t<\/td>\r\n\t\r\n\t\r\n<\/tr>\r\n<\/table>\r\n\r\n\r\n\n","protected":false},"excerpt":{"rendered":"<p>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Investigating the use of publicly available natural videos to learn Dynamic MR image reconstruction \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 [&hellip;]<\/p>\n","protected":false},"featured_media":27,"comment_status":"open","ping_status":"closed","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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