Details of cardiac datasets used in our experiments for proposed methods 1 and 2.

<p>For the training and testing purpose, we used 8 receiver coils human cardiac (simulated) dataset. Similarly, for testing the proposed methods, we also used multi-coil human cardiac clinical dataset, publicly available from Ohio State University [<a href="http://www.plosone.org/artic...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Madiha Arshad (20542711) (author)
مؤلفون آخرون:	Faisal Najeeb (20542714) (author), Rameesha Khawaja (20542717) (author), Amna Ammar (20542720) (author), Kashif Amjad (7665488) (author), Hammad Omer (17412783) (author)
منشور في:	2025
الموضوعات:	Cell Biology Space Science Astronomical and Space Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified typically use u higher peak signal experimental results demonstrate enhance overall robustness current research focus >- space domain single domain methods coil data consistency improve image quality conventional deep learning div >< p image domain learning proposed methods outperform dual domain methods proposed hybrid dual deep learning sampled data image reconstruction variable density two categories rmse ), respiratory motion resolution applications residual artifacts reduce artifacts recovering diagnostic reconstruction process parallel imaging paper introduces often resulting noise ratio k </ incorporates multi high spatial gained attention fully exploit compressed sensing addressing cardiac
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Details of cardiac datasets used in our experiments for proposed methods 1 and 2.

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