{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T18:23:32Z","timestamp":1783535012002,"version":"3.55.0"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"DOI":"10.1007\/s10278-022-00744-2","type":"journal-article","created":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T21:03:23Z","timestamp":1670360603000},"page":"725-738","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Perceptually Motivated Generative Model for Magnetic Resonance Image Denoising"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3297-9949","authenticated-orcid":false,"given":"Hazique","family":"Aetesam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suman Kumar","family":"Maji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,6]]},"reference":[{"key":"744_CR1","doi-asserted-by":"crossref","unstructured":"Henkelman, R.M.: Measurement of signal intensities in the presence of noise in mr images. Medical physics 12(2), 232\u2013233 (1985)","DOI":"10.1118\/1.595711"},{"key":"744_CR2","doi-asserted-by":"crossref","unstructured":"Nowak, R.D.: Wavelet-based rician noise removal for magnetic resonance imaging. IEEE Transactions on Image Processing 8(10), 1408\u20131419 (1999)","DOI":"10.1109\/83.791966"},{"key":"744_CR3","doi-asserted-by":"crossref","unstructured":"Luisier, F., Blu, T., Wolfe, P.J.: A cure for noisy magnetic resonance images: Chi-square unbiased risk estimation. IEEE Transactions on Image Processing 21(8), 3454\u20133466 (2012)","DOI":"10.1109\/TIP.2012.2191565"},{"key":"744_CR4","doi-asserted-by":"crossref","unstructured":"You, S., Lei, B., Wang, S., Chui, C.K., Cheung, A.C., Liu, Y., Gan, M., Wu, G., Shen, Y.: Fine perceptive gans for brain mr image super-resolution in wavelet domain. IEEE transactions on neural networks and learning systems (2022)","DOI":"10.1109\/TNNLS.2022.3153088"},{"key":"744_CR5","doi-asserted-by":"crossref","unstructured":"Hu, S., Lei, B., Wang, S., Wang, Y., Feng, Z., Shen, Y.: Bidirectional mapping generative adversarial networks for brain mr to pet synthesis. IEEE Transactions on Medical Imaging 41(1), 145\u2013157 (2021)","DOI":"10.1109\/TMI.2021.3107013"},{"key":"744_CR6","doi-asserted-by":"crossref","unstructured":"Kumar, A., Welti, D., Ernst, R.R.: Nmr fourier zeugmatography. Journal of Magnetic Resonance (1969) 18(1), 69\u201383 (1975)","DOI":"10.1016\/0022-2364(75)90224-3"},{"key":"744_CR7","doi-asserted-by":"crossref","unstructured":"Bhujle, H.V., Vadavadagi, B.H.: Nlm based magnetic resonance image denoising\u2013a review. Biomedical Signal Processing and Control 47, 252\u2013261 (2019)","DOI":"10.1016\/j.bspc.2018.08.031"},{"key":"744_CR8","doi-asserted-by":"crossref","unstructured":"Coup\u00e9, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images. IEEE transactions on medical imaging 27(4), 425\u2013441 (2008)","DOI":"10.1109\/TMI.2007.906087"},{"key":"744_CR9","doi-asserted-by":"crossref","unstructured":"Manj\u00f3n, J.V., Coup\u00e9, P., Mart\u00ed-Bonmat\u00ed, L., Collins, D.L., Robles, M.: Adaptive non-local means denoising of mr images with spatially varying noise levels. Journal of Magnetic Resonance Imaging 31(1), 192\u2013203 (2010)","DOI":"10.1002\/jmri.22003"},{"key":"744_CR10","doi-asserted-by":"crossref","unstructured":"Breuer, F.A., Kellman, P., Griswold, M.A., Jakob, P.M.: Dynamic autocalibrated parallel imaging using temporal grappa (tgrappa). Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 53(4), 981\u2013985 (2005)","DOI":"10.1002\/mrm.20430"},{"key":"744_CR11","doi-asserted-by":"crossref","unstructured":"Coup\u00e9, P., Manj\u00f3n, J.V., Robles, M., Collins, D.L.: Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising. IET image Processing 6(5), 558\u2013568 (2012)","DOI":"10.1049\/iet-ipr.2011.0161"},{"key":"744_CR12","doi-asserted-by":"crossref","unstructured":"Manj\u00f3n, J.V., Coup\u00e9, P., Buades, A., Collins, D.L., Robles, M.: New methods for mri denoising based on sparseness and self-similarity. Medical image analysis 16(1), 18\u201327 (2012)","DOI":"10.1016\/j.media.2011.04.003"},{"key":"744_CR13","doi-asserted-by":"crossref","unstructured":"Tasdizen, T.: Principal neighborhood dictionaries for nonlocal means image denoising. IEEE Transactions on Image Processing 18(12), 2649\u20132660 (2009)","DOI":"10.1109\/TIP.2009.2028259"},{"key":"744_CR14","doi-asserted-by":"crossref","unstructured":"Lu, K., He, N., Li, L.: Nonlocal means-based denoising for medical images. Computational and mathematical methods in medicine 2012 (2012)","DOI":"10.1155\/2012\/438617"},{"key":"744_CR15","doi-asserted-by":"crossref","unstructured":"Gurney-Champion, O.J., Collins, D.J., Wetscherek, A., Rata, M., Klaassen, R., van Laarhoven, H.W., Harrington, K.J., Oelfke, U., Orton, M.R.: Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted mr images. Physics in Medicine & Biology 64(10), 105015 (2019)","DOI":"10.1088\/1361-6560\/ab1786"},{"key":"744_CR16","doi-asserted-by":"crossref","unstructured":"Veraart, J., Novikov, D.S., Christiaens, D., Ades-Aron, B., Sijbers, J., Fieremans, E.: Denoising of diffusion mri using random matrix theory. Neuroimage 142, 394\u2013406 (2016)","DOI":"10.1016\/j.neuroimage.2016.08.016"},{"key":"744_CR17","doi-asserted-by":"crossref","unstructured":"Wirestam, R., Bibic, A., L\u00e4tt, J., Brockstedt, S., St\u00e5hlberg, F.: Denoising of complex mri data by wavelet-domain filtering: Application to high-b-value diffusion-weighted imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 56(5), 1114\u20131120 (2006)","DOI":"10.1002\/mrm.21036"},{"key":"744_CR18","doi-asserted-by":"crossref","unstructured":"Golshan, H.M., Hasanzadeh, R.P., Yousefzadeh, S.C.: An mri denoising method using image data redundancy and local snr estimation. Magnetic resonance imaging 31(7), 1206\u20131217 (2013)","DOI":"10.1016\/j.mri.2013.04.004"},{"key":"744_CR19","unstructured":"Kala, R., Deepa, P.: Adaptive fuzzy hexagonal bilateral filter for brain mri denoising. Multimedia Tools and Applications, 1\u201318 (2019)"},{"key":"744_CR20","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Shen, W., Cheng, F., Jin, C., Cao, G.: Removal of high density gaussian noise in compressed sensing mri reconstruction through modified total variation image denoising method. Heliyon 6(3), 03680 (2020)","DOI":"10.1016\/j.heliyon.2020.e03680"},{"key":"744_CR21","doi-asserted-by":"crossref","unstructured":"Liu, R.W., Shi, L., Huang, W., Xu, J., Yu, S.C.H., Wang, D.: Generalized total variation-based mri rician denoising model with spatially adaptive regularization parameters. Magnetic resonance imaging 32(6), 702\u2013720 (2014)","DOI":"10.1016\/j.mri.2014.03.004"},{"key":"744_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhou, H.: Total variation wavelet-based medical image denoising. International Journal of Biomedical Imaging 2006 (2006)","DOI":"10.1155\/IJBI\/2006\/89095"},{"key":"744_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Z., Hu, J., Zou, S., Fu, Y.: Mri denoising using low rank prior and sparse gradient prior. IEEE Access 7, 45858\u201345865 (2019)","DOI":"10.1109\/ACCESS.2019.2907637"},{"key":"744_CR24","doi-asserted-by":"crossref","unstructured":"Wink, A.M., Roerdink, J.B.: Denoising functional mr images: a comparison of wavelet denoising and gaussian smoothing. IEEE transactions on medical imaging 23(3), 374\u2013387 (2004)","DOI":"10.1109\/TMI.2004.824234"},{"key":"744_CR25","doi-asserted-by":"crossref","unstructured":"Simi, V., Edla, D.R., Joseph, J., Kuppili, V.: Analysis of controversies in the formulation and evaluation of restoration algorithms for mr images. Expert Systems with Applications 135, 39\u201359 (2019)","DOI":"10.1016\/j.eswa.2019.06.003"},{"key":"744_CR26","doi-asserted-by":"crossref","unstructured":"Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on mri. Zeitschrift f\u00fcr Medizinische Physik 29(2), 102\u2013127 (2019)","DOI":"10.1016\/j.zemedi.2018.11.002"},{"key":"744_CR27","doi-asserted-by":"crossref","unstructured":"Benou, A., Veksler, R., Friedman, A., Raviv, T.R.: Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced mri sequences. Medical image analysis 42, 145\u2013159 (2017)","DOI":"10.1016\/j.media.2017.07.006"},{"key":"744_CR28","doi-asserted-by":"crossref","unstructured":"Jiang, D., Dou, W., Vosters, L., Xu, X., Sun, Y., Tan, T.: Denoising of 3d magnetic resonance images with multi-channel residual learning of convolutional neural network. Japanese journal of radiology 36(9), 566\u2013574 (2018)","DOI":"10.1007\/s11604-018-0758-8"},{"key":"744_CR29","doi-asserted-by":"crossref","unstructured":"You, X., Cao, N., Lu, H., Mao, M., Wanga, W.: Denoising of mr images with rician noise using a wider neural network and noise range division. Magnetic Resonance Imaging 64, 154\u2013159 (2019)","DOI":"10.1016\/j.mri.2019.05.042"},{"key":"744_CR30","doi-asserted-by":"crossref","unstructured":"Panda, A., Naskar, R., Rajbans, S., Pal, S.: A 3d wide residual network with perceptual loss for brain mri image denoising. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1\u20137 (2019). IEEE","DOI":"10.1109\/ICCCNT45670.2019.8944535"},{"key":"744_CR31","doi-asserted-by":"crossref","unstructured":"Ran, M., Hu, J., Chen, Y., Chen, H., Sun, H., Zhou, J., Zhang, Y.: Denoising of 3d magnetic resonance images using a residual encoder\u2013decoder wasserstein generative adversarial network. Medical image analysis 55, 165\u2013180 (2019)","DOI":"10.1016\/j.media.2019.05.001"},{"key":"744_CR32","doi-asserted-by":"crossref","unstructured":"Aetesam, H., Maji, S.K.: Attention-based noise prior network for magnetic resonance image denoising. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20134 (2022). IEEE","DOI":"10.1109\/ISBI52829.2022.9761530"},{"key":"744_CR33","doi-asserted-by":"crossref","unstructured":"Bermudez, C., Plassard, A.J., Davis, L.T., Newton, A.T., Resnick, S.M., Landman, B.A.: Learning implicit brain mri manifolds with deep learning. In: Medical Imaging 2018: Image Processing, vol. 10574, p. 105741 (2018). International Society for Optics and Photonics","DOI":"10.1117\/12.2293515"},{"key":"744_CR34","doi-asserted-by":"crossref","unstructured":"Moreno\u00a0L\u00f3pez, M., Frederick, J.M., Ventura, J.: Evaluation of mri denoising methods using unsupervised learning. Frontiers in Artificial Intelligence 4, 75 (2021)","DOI":"10.3389\/frai.2021.642731"},{"key":"744_CR35","doi-asserted-by":"crossref","unstructured":"Isa, I.S., Sulaiman, S.N., Mustapha, M., Darus, S.: Evaluating denoising performances of fundamental filters for t2-weighted mri images. Procedia Computer Science 60, 760\u2013768 (2015)","DOI":"10.1016\/j.procs.2015.08.231"},{"key":"744_CR36","doi-asserted-by":"crossref","unstructured":"Aetesam, H., Maji, S.K.: L2- l1 fidelity based elastic net regularisation for magnetic resonance image denoising. In: 2020 International Conference on Contemporary Computing and Applications (IC3A), pp. 137\u2013142 (2020). IEEE","DOI":"10.1109\/IC3A48958.2020.233285"},{"key":"744_CR37","doi-asserted-by":"crossref","unstructured":"Shlykov, V., Kotovskyi, V., Vi\u0161niakov, N., \u0160e\u0161ok, A.: Model for elimination of mixed noise from mri heart images. Applied Sciences 10(14), 4747 (2020)","DOI":"10.3390\/app10144747"},{"key":"744_CR38","doi-asserted-by":"crossref","unstructured":"Toprak, A., G\u00fcler, I.: Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter. Digital signal processing 17(4), 711\u2013723 (2007)","DOI":"10.1016\/j.dsp.2006.11.008"},{"key":"744_CR39","doi-asserted-by":"crossref","unstructured":"Toprak, A., \u00d6zerdem, M.S., G\u00fcler, \u0130.: Suppression of impulse noise in mr images using artificial intelligent based neuro-fuzzy adaptive median filter. Digital signal processing 18(3), 391\u2013405 (2008)","DOI":"10.1016\/j.dsp.2007.04.008"},{"key":"744_CR40","doi-asserted-by":"crossref","unstructured":"Lin, L., Meng, X., Liang, X.: Reduction of impulse noise in mri images using block-based adaptive median filter. In: 2013 IEEE International Conference on Medical Imaging Physics and Engineering, pp. 132\u2013134 (2013). IEEE","DOI":"10.1109\/ICMIPE.2013.6864519"},{"key":"744_CR41","doi-asserted-by":"crossref","unstructured":"Mafi, M., Martin, H., Adjouadi, M.: High impulse noise intensity removal in mri images. In: 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1\u20136 (2017). IEEE","DOI":"10.1109\/SPMB.2017.8257030"},{"key":"744_CR42","unstructured":"HosseinKhani, Z., Hajabdollahi, M., Karimi, N., Soroushmehr, S., Shirani, S., Samavi, S., Najarian, K.: Real-time impulse noise removal from mr images for radiosurgery applications. arXiv preprint arXiv:1707.05975 (2017)"},{"key":"744_CR43","doi-asserted-by":"crossref","unstructured":"HosseinKhani, Z., Hajabdollahi, M., Karimi, N., Soroushmehr, R., Shirani, S., Najarian, K., Samavi, S.: Adaptive real-time removal of impulse noise in medical images. Journal of medical systems 42(11), 216 (2018)","DOI":"10.1007\/s10916-018-1074-7"},{"key":"744_CR44","doi-asserted-by":"crossref","unstructured":"Chanu, P.R., Singh, K.M.: Impulse noise removal from medical images by two stage quaternion vector median filter. Journal of medical systems 42(10), 197 (2018)","DOI":"10.1007\/s10916-018-1057-8"},{"key":"744_CR45","doi-asserted-by":"crossref","unstructured":"Sheela, C.J.J., Suganthi, G.: An efficient denoising of impulse noise from mri using adaptive switching modified decision based unsymmetric trimmed median filter. Biomedical Signal Processing and Control 55, 101657 (2020)","DOI":"10.1016\/j.bspc.2019.101657"},{"key":"744_CR46","doi-asserted-by":"crossref","unstructured":"HosseinKhani, Z., Karimi, N., Soroushmehr, S.M.R., Hajabdollahi, M., Samavi, S., Ward, K., Najarian, K.: Real-time removal of random value impulse noise in medical images. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3916\u20133921 (2016). IEEE","DOI":"10.1109\/ICPR.2016.7900246"},{"key":"744_CR47","doi-asserted-by":"crossref","unstructured":"HosseinKhani, Z., Hajabdollahi, M., Karimi, N., Najarian, K., Emami, A., Shirani, S., Samavi, S., Soroushmehr, S.M.R.: Real-time removal of impulse noise from mr images for radiosurgery applications. International Journal of Circuit Theory and Applications 47(3), 406\u2013426 (2019)","DOI":"10.1002\/cta.2591"},{"key":"744_CR48","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhang, L., Yang, J.: Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE transactions on image processing 23(6), 2651\u20132662 (2014)","DOI":"10.1109\/TIP.2014.2317985"},{"key":"744_CR49","doi-asserted-by":"crossref","unstructured":"Huang, T., Dong, W., Xie, X., Shi, G., Bai, X.: Mixed noise removal via laplacian scale mixture modeling and nonlocal low-rank approximation. IEEE Transactions on Image Processing 26(7), 3171\u20133186 (2017)","DOI":"10.1109\/TIP.2017.2676466"},{"key":"744_CR50","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)"},{"key":"744_CR51","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017)"},{"key":"744_CR52","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. In: Advances in Neural Information Processing Systems, pp. 5767\u20135777 (2017)"},{"key":"744_CR53","doi-asserted-by":"crossref","unstructured":"Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Transactions on computational imaging 3(1), 47\u201357 (2016)","DOI":"10.1109\/TCI.2016.2644865"},{"key":"744_CR54","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"744_CR55","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694\u2013711 (2016). Springer","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"744_CR56","doi-asserted-by":"crossref","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600\u2013612 (2004)","DOI":"10.1109\/TIP.2003.819861"},{"key":"744_CR57","doi-asserted-by":"crossref","unstructured":"Brunet, D., Vrscay, E.R., Wang, Z.: On the mathematical properties of the structural similarity index. IEEE Transactions on Image Processing 21(4), 1488\u20131499 (2011)","DOI":"10.1109\/TIP.2011.2173206"},{"key":"744_CR58","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456 (2015). PMLR"},{"key":"744_CR59","unstructured":"Ioffe, S.: Batch renormalization: Towards reducing minibatch dependence in batch-normalized models. In: Advances in Neural Information Processing Systems, pp. 1945\u20131953 (2017)"},{"key":"744_CR60","doi-asserted-by":"crossref","unstructured":"Ye, R., Liu, F., Zhang, L.: 3d depthwise convolution: Reducing model parameters in 3d vision tasks. In: Canadian Conference on Artificial Intelligence, pp. 186\u2013199 (2019). Springer","DOI":"10.1007\/978-3-030-18305-9_15"},{"key":"744_CR61","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"744_CR62","unstructured":"Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. arXiv preprint arXiv:1903.10082 (2019)"},{"key":"744_CR63","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Transactions on Image Processing 26(7), 3142\u20133155 (2017)","DOI":"10.1109\/TIP.2017.2662206"},{"key":"744_CR64","unstructured":"Cocosco, C.A., Kollokian, V., Kwan, R.K.-S., Pike, G.B., Evans, A.C.: Brainweb: Online interface to a 3d mri simulated brain database. In: NeuroImage (1997). Citeseer"},{"key":"744_CR65","doi-asserted-by":"crossref","unstructured":"Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: 2011 International Conference on Computer Vision, pp. 479\u2013486 (2011). IEEE","DOI":"10.1109\/ICCV.2011.6126278"},{"key":"744_CR66","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9446\u20139454 (2018)"},{"key":"744_CR67","doi-asserted-by":"crossref","unstructured":"Aetesam, H., Maji, S.K.: Noise dependent training for deep parallel ensemble denoising in magnetic resonance images. Biomedical Signal Processing and Control 66, 102405 (2021)","DOI":"10.1016\/j.bspc.2020.102405"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00744-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-022-00744-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-022-00744-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T19:14:25Z","timestamp":1679685265000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-022-00744-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,6]]},"references-count":67,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["744"],"URL":"https:\/\/doi.org\/10.1007\/s10278-022-00744-2","relation":{},"ISSN":["1618-727X"],"issn-type":[{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,6]]},"assertion":[{"value":"21 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This is a numerical simulation study for which no ethical approval was required. The research did not involve any human\/non-human subjects.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}