{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T14:14:05Z","timestamp":1770473645500,"version":"3.49.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Magnetic resonance image (MRI) is extensively adapted in clinical diagnosis due to its improved representation of changes with soft tissue. The current innovations in compressive sensing (CS) showed that it is viable to precisely reconstruct MRI with K-space data with reduced scanning duration and it takes more time for computation and it is complex to capture the fine details. The CS is capable to minimize the imaging time and it uses compressibility for reconstructing the images. In this paper, a novel deep-learning method is devised for reconstructing images using a set of MRI. Here, MRI acquisition and MRIsub sampling is performed for reducing the size of images to perform improved analysis. Here, a convolution layer is utilized for imitating the process of compressed sampling that can learn the sampling matrix to prevent complex designs. In addition, a convolution layer is utilized to execute the initial reconstruction. In addition, Deep Convolutional Neural Network (Deep CNN) is adapted for image reconstruction. The training of Deep CNN is performed using proposed Fractional Sailfish Optimizer (FSO) algorithm. The proposed FSO is designed by incorporating fractional concepts in Sail fish optimization for tuning the optimal weights of Deep CNN. The proposed FSO-based CSNet outperformed other methods with minimal MSE of 1.877 and maximal PSNR of 45.396\u00a0dB.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab160","type":"journal-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T19:12:26Z","timestamp":1632251546000},"page":"280-294","source":"Crossref","is-referenced-by-count":3,"title":["Fractional Sailfish Optimizer with Deep Convolution Neural Network for Compressive Sensing Based Magnetic Resonance Image Reconstruction"],"prefix":"10.1093","volume":"66","author":[{"given":"Penta Anil","family":"Kumar","sequence":"first","affiliation":[{"name":"Electronics and Communication Engineering Department , , East Coast Road, Pillaichavadi, Puducherry 605014, India"},{"name":"Pondicherry Engineering College , , East Coast Road, Pillaichavadi, Puducherry 605014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R","family":"Gunasundari","sequence":"additional","affiliation":[{"name":"Electronics and Communication Engineering Department , , East Coast Road, Pillaichavadi, Puducherry 605014, India"},{"name":"Pondicherry Engineering College , , East Coast Road, Pillaichavadi, Puducherry 605014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R","family":"Aarthi","sequence":"additional","affiliation":[{"name":"Department of Electronics and Instrumentation Engineering , , Bharathi Salai, Ramapuram, Chennai, Tamil Nadu 600089, India"},{"name":"SRM Easwari Engineering College , , Bharathi Salai, Ramapuram, Chennai, Tamil Nadu 600089, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,10,17]]},"reference":[{"key":"2023022013493814800_","first-page":"707","article-title":"Comparison of macular thickness measurements between time-domain and spectral-domain optical coherence tomographies in eyes with and without macular abnormalities","author":"Giammaria","year":"2011"},{"key":"2023022013493814800_","first-page":"81","article-title":"Evaluation of neuropsychological functions in patients with Friedreich ataxia before and after cognitive therapy","volume":"25","author":"Ciancarelli","year":"2010","journal-title":"Funct. 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