{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T21:47:31Z","timestamp":1765057651011,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,23]],"date-time":"2019-04-23T00:00:00Z","timestamp":1555977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The application of compressed sensing (CS) to biomedical imaging is sensational since it permits a rationally accurate reconstruction of images by exploiting the image sparsity. The quality of CS reconstruction methods largely depends on the use of various sparsifying transforms, such as wavelets, curvelets or total variation (TV), to recover MR images. As per recently developed mathematical concepts of CS, the biomedical images with sparse representation can be recovered from randomly undersampled data, provided that an appropriate nonlinear recovery method is used. Due to high under-sampling, the reconstructed images have noise like artifacts because of aliasing. Reconstruction of images from CS involves two steps, one for dictionary learning and the other for sparse coding. In this novel framework, we choose Simultaneous code word optimization (SimCO) patch-based dictionary learning that updates the atoms simultaneously, whereas Focal underdetermined system solver (FOCUSS) is used for sparse representation because of a soft constraint on sparsity of an image. Combining SimCO and FOCUSS, we propose a new scheme called SiFo. Our proposed alternating reconstruction scheme learns the dictionary, uses it to eliminate aliasing and noise in one stage, and afterwards restores and fills in the k-space data in the second stage. Experiments were performed using different sampling schemes with noisy and noiseless cases of both phantom and real brain images. Based on various performance parameters, it has been shown that our designed technique outperforms the conventional techniques, like K-SVD with OMP, used in dictionary learning based MRI (DLMRI) reconstruction.<\/jats:p>","DOI":"10.3390\/s19081918","type":"journal-article","created":{"date-parts":[[2019,4,24]],"date-time":"2019-04-24T03:14:28Z","timestamp":1556075668000},"page":"1918","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Improved Reconstruction of MR Scanned Images by Using a Dictionary Learning Scheme"],"prefix":"10.3390","volume":"19","author":[{"given":"Shahid","family":"Ikram","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jawad Ali","family":"Shah","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, UniKL BMI, Kuala Lumpur 53100, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed","family":"Zubair","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ijaz Mansoor","family":"Qureshi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Air University Islamabad, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-3526","authenticated-orcid":false,"given":"Muhammad","family":"Bilal","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TIT.2005.862083","article-title":"Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information","volume":"52","author":"Romberg","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_3","unstructured":"Ma, S., Yin, W., Zhang, Y., and Chakraborty, A. (2008, January 23\u201328). An efficient algorithm for compressed MR imaging using total variation and wavelets. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA."},{"key":"ref_4","unstructured":"Bresler, Y., and Feng, P. (1996, January 19). Spectrum-blind minimum-rate sampling and reconstruction of 2-D multiband signals. Proceedings of the 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland."},{"key":"ref_5","unstructured":"Feng, P. (1997). Universal spectrum blind minimum rate sampling and reconstruction of multiband signals. [Ph.D. Thesis, University of Illinois at Urbana-Champaign]."},{"key":"ref_6","unstructured":"Venkataramani, R., and Bresler, Y. (1998, January 7). Further results on spectrum blind sampling of 2D signals. Proceedings of the 1998 International Conference on Image Processing, Chicago, IL, USA."},{"key":"ref_7","unstructured":"Bresler, Y., Gastpar, M., and Venkataramani, R. (1999, January 24\u201326). Image compression on-the-fly by universal sampling in Fourier imaging systems. Proceedings of the IT Worskhop on Detection, Estimation, Classification and Imaging, Santa Fe, NM, USA."},{"key":"ref_8","unstructured":"Gastpar, M., and Bresler, Y. (2000, January 25\u201330). On the necessary density for spectrum-blind nonuniform sampling subject to quantization. Proceedings of the 2000 IEEE International Symposium on Information Theory, Sorrento, Italy."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1023\/A:1020822324006","article-title":"A self-referencing level-set method for image reconstruction from sparse Fourier samples","volume":"50","author":"Ye","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bresler, Y. (February, January 27). Spectrum-blind sampling and compressive sensing for continuous-index signals. Proceedings of the 2008 Information Theory and Applications Workshop, San Diego, CA, USA.","DOI":"10.1109\/ITA.2008.4601017"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4875","DOI":"10.1109\/ACCESS.2018.2793851","article-title":"A systematic review of compressive sensing: Concepts, implementations and applications","volume":"6","author":"Rani","year":"2018","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1002\/mrm.21391","article-title":"Sparse MRI: The application of compressed sensing for rapid MR imaging","volume":"58","author":"Lustig","year":"2007","journal-title":"Magn. Reson. Med."},{"key":"ref_13","unstructured":"Lustig, M., Santos, J.M., Donoho, D.L., and Pauly, J.M. (2006, January 6\u201312). k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity. Proceedings of the 14th Annual Meeting of ISMRM, Seattle, WA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chartrand, R. (July, January 28). Fast Algorithms for Nonconvex Compressive Sensing: MRI Reconstruction from Very Few Data. Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA.","DOI":"10.1109\/ISBI.2009.5193034"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/TMI.2008.927346","article-title":"Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic \u21130 -Minimization","volume":"28","author":"Trzasko","year":"2009","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1109\/TIP.2012.2188807","article-title":"Wavelet-based compressed sensing using a Gaussian scale mixture model","volume":"21","author":"Kim","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","unstructured":"Qiu, C., Lu, W., and Vaswani, N. (2009, January 19\u201324). Real-time dynamic MR image reconstruction using Kalman filtered compressed sensing. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8799","DOI":"10.1038\/s41598-018-27261-z","article-title":"Super-resolution CT Image Reconstruction Based on Dictionary Learning and Sparse Representation","volume":"8","author":"Jiang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4203","DOI":"10.1109\/TIT.2005.858979","article-title":"Decoding by linear programming","volume":"51","author":"Candes","year":"2005","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation","volume":"54","author":"Aharon","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Engan, K., Aase, S.O., and Husoy, J.H. (1999, January 15\u201319). Method of optimal directions for frame design. Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, AZ, USA.","DOI":"10.1109\/ICASSP.1999.760624"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/TMI.2010.2090538","article-title":"MR image reconstruction from highly undersampled k-space data by dictionary learning","volume":"30","author":"Ravishankar","year":"2011","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5302","DOI":"10.1109\/TIT.2009.2030471","article-title":"Robust recovery of signals from a structured union of subspaces","volume":"55","author":"Eldar","year":"2009","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3075","DOI":"10.1109\/TSP.2009.2020754","article-title":"On the reconstruction of block-sparse signals with an optimal number of measurements","volume":"57","author":"Stojnic","year":"2009","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1109\/TIT.2009.2034789","article-title":"Average case analysis of multichannel sparse recovery using convex relaxation","volume":"56","author":"Eldar","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6340","DOI":"10.1109\/TSP.2012.2215026","article-title":"Simultaneous codeword optimization (SimCO) for dictionary update and learning","volume":"60","author":"Dai","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1111\/j.1467-9868.2007.00627.x","article-title":"The group lasso for logistic regression","volume":"70","author":"Meier","year":"2008","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1002\/mrm.21202","article-title":"Projection reconstruction MR imaging using FOCUSS","volume":"57","author":"Ye","year":"2007","journal-title":"Magn. Reson. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2017.2785879","article-title":"DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction","volume":"37","author":"Yang","year":"2018","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Seitzer, M., Yang, G., Schlemper, J., Oktay, O., W\u00fcrfl, T., Christlein, V., Wong, T., Mohiaddin, R., Firmin, D., and Keegan, J. (2018, January 16\u201320). Adversarial and perceptual refinement for compressed sensing MRI reconstruction. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00928-1_27"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Schlemper, J., Yang, G., Ferreira, P., Scott, A., McGill, L.-A., Khalique, Z., Gorodezky, M., Roehl, M., Keegan, J., and Pennell, D. (2018, January 16\u201320). Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00928-1_34"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Schlemper, J., Caballero, J., Hajnal, J.V., Price, A., and Rueckert, D. (2017, January 25\u201330). A deep cascade of convolutional neural networks for MR image reconstruction. Proceedings of the International Conference on Information Processing in Medical Imaging, Boone, IA, USA.","DOI":"10.1007\/978-3-319-59050-9_51"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MSP.2007.914728","article-title":"Compressed sensing MRI","volume":"25","author":"Lustig","year":"2008","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, Z., Zhang, C., and Xie, L. (2013, January 7\u201311). Sparse MRI for motion correction. Proceedings of the 2013 IEEE 10th International Symposium on Biomedical Imaging, San Francisco, CA, USA.","DOI":"10.1109\/ISBI.2013.6556636"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"i15","DOI":"10.1093\/jmicro\/dfu037","article-title":"Introduction to advanced image reconstruction methods and compressed sensing in medical computed tomography","volume":"63","author":"Kudo","year":"2014","journal-title":"Microscopy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3523","DOI":"10.1109\/TIT.2010.2048466","article-title":"Dictionary Identification\u2014Sparse Matrix-Factorization via \u21131 -Minimization","volume":"56","author":"Remi","year":"2010","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"223","DOI":"10.3934\/ipi.2010.4.223","article-title":"A novel method and fast algorithm for MR image reconstruction with significantly under-sampled data","volume":"4","author":"Chen","year":"2010","journal-title":"Inverse Probl. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1080\/17415977.2010.492509","article-title":"Iterative thresholding compressed sensing MRI based on contourlet transform","volume":"18","author":"Qu","year":"2010","journal-title":"Inverse Probl. Sci. Eng."},{"key":"ref_39","unstructured":"Bilgin, A., Kim, Y., Liu, F., and Nadar, M. (2010, January 1\u20137). Dictionary design for compressed sensing MRI. Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, Stockholm, Sweden."},{"key":"ref_40","unstructured":"Jain, A.K. (1989). Fundamentals of Digital Image Processing, Prentice Hall."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/8\/1918\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:46:37Z","timestamp":1760186797000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/8\/1918"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,23]]},"references-count":40,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["s19081918"],"URL":"https:\/\/doi.org\/10.3390\/s19081918","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,4,23]]}}}