{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T14:53:56Z","timestamp":1746802436166,"version":"3.40.5"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671405"],"award-info":[{"award-number":["61671405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>The <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> principal component analysis (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M6\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise amplification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M7\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M8\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> PCA that combines the <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M9\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M10\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M11\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M12\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> PCA method by further eliminating the reconstruction error derived from complex subtraction of the sampled <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M13\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M14\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> space from the original reconstructed <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M15\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M16\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> space. The proposed method is tested through both simulations and in vivo datasets with different reduction factors. Compared to the standard <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M17\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M18\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> PCA algorithm, the sparse <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M19\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M20\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> PCA can improve the normalized root-mean-square error performance and the accuracy of temporal resolution. It is thus useful for rapid dynamic MR imaging.<\/jats:p>","DOI":"10.1155\/2017\/4816024","type":"journal-article","created":{"date-parts":[[2017,7,18]],"date-time":"2017-07-18T17:02:18Z","timestamp":1500397338000},"page":"1-12","source":"Crossref","is-referenced-by-count":2,"title":["Improved <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:mi>t<\/mml:mi><\/mml:mrow><\/mml:math> PCA Algorithm Using Artificial Sparsity in Dynamic MRI"],"prefix":"10.1155","volume":"2017","author":[{"given":"Yiran","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, 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