{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T01:40:39Z","timestamp":1709343639464},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s10278-021-00467-w","type":"journal-article","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T16:20:39Z","timestamp":1632932439000},"page":"1359-1375","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Ultra-Low-Dose Spectral CT Based on a Multi-level Wavelet Convolutional Neural Network"],"prefix":"10.1007","volume":"34","author":[{"given":"Minjae","family":"Lee","sequence":"first","affiliation":[]},{"given":"Hyemi","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Hyo-Min","family":"Cho","sequence":"additional","affiliation":[]},{"given":"Hee-Joung","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"issue":"3","key":"467_CR1","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1118\/1.2836950","volume":"35","author":"G Wang","year":"2008","unstructured":"Wang G, Yu H, De Man B. An outlook on x-ray CT research and development.\u00a0Med Phys. 2008;35(3):1051\u20101064. https:\/\/doi.org\/10.1118\/1.2836950","journal-title":"Med Phys."},{"issue":"5","key":"467_CR2","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1088\/0031-9155\/21\/5\/002","volume":"21","author":"RE Alvarez","year":"1976","unstructured":"Alvarez RE, Macovski A. Energy-selective reconstructions in X-ray computerized tomography.\u00a0Phys Med Biol. 1976;21(5):733\u2010744. https:\/\/doi.org\/10.1088\/0031-9155\/21\/5\/002","journal-title":"Phys Med Biol."},{"issue":"10","key":"467_CR3","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1109\/42.959297","volume":"20","author":"B De Man","year":"2001","unstructured":"De Man B, Nuyts J, Dupont P, Marchal G, Suetens P. An iterative maximum-likelihood polychromatic algorithm for CT.\u00a0IEEE Trans Med Imaging. 2001;20(10):999\u20101008. https:\/\/doi.org\/10.1109\/42.959297","journal-title":"IEEE Trans Med Imaging."},{"issue":"1","key":"467_CR4","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00330-008-1122-7","volume":"19","author":"A Graser","year":"2009","unstructured":"Graser A, Johnson TR, Chandarana H, Macari M. Dual energy CT: preliminary observations and potential clinical applications in the abdomen.\u00a0Eur Radiol. 2009;19(1):13\u201023. https:\/\/doi.org\/10.1007\/s00330-008-1122-7","journal-title":"Eur Radiol."},{"issue":"5 Suppl","key":"467_CR5","doi-asserted-by":"publisher","first-page":"S9","DOI":"10.2214\/AJR.12.9121","volume":"199","author":"L Yu","year":"2012","unstructured":"Yu L, Leng S, McCollough CH. Dual-energy CT-based monochromatic imaging.\u00a0AJR Am J Roentgenol. 2012;199(5 Suppl):S9\u2010S15. https:\/\/doi.org\/10.2214\/AJR.12.9121","journal-title":"AJR Am J Roentgenol."},{"issue":"10","key":"467_CR6","doi-asserted-by":"publisher","first-page":"2117","DOI":"10.1007\/s00330-012-2485-3","volume":"22","author":"P Lv","year":"2012","unstructured":"Lv P, Lin XZ, Chen K, Gao J. Spectral CT in patients with small HCC: investigation of image quality and diagnostic accuracy.\u00a0Eur Radiol. 2012;22(10):2117\u20102124. https:\/\/doi.org\/10.1007\/s00330-012-2485-3","journal-title":"Eur Radiol."},{"issue":"8","key":"467_CR7","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1016\/j.ejrad.2011.02.063","volume":"81","author":"LQ Zhao","year":"2012","unstructured":"Zhao LQ, He W, Li JY, Chen JH, Wang KY, Tan L. Improving image quality in portal venography with spectral CT imaging.\u00a0Eur J Radiol. 2012;81(8):1677\u20101681. https:\/\/doi.org\/10.1016\/j.ejrad.2011.02.063","journal-title":"Eur J Radiol."},{"key":"467_CR8","doi-asserted-by":"publisher","DOI":"10.1117\/3.977546","volume-title":"Spectral computed tomography","author":"BJ Heismann","year":"2012","unstructured":"Heismann BJ, Schmidt BT, Flohr T. Spectral computed tomography. Bellingham, WA: SPIE; 2012."},{"key":"467_CR9","doi-asserted-by":"publisher","unstructured":"Yang Q, Cong W, Xi Y, Wang G. Spectral X-Ray CT Image Reconstruction with a Combination of Energy-Integrating and Photon-Counting Detectors.\u00a0PLoS One. 2016;11(5):e0155374. Published 2016 May 12. https:\/\/doi.org\/10.1371\/journal.pone.0155374","DOI":"10.1371\/journal.pone.0155374"},{"issue":"7","key":"467_CR10","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1088\/0031-9155\/56\/7\/001","volume":"56","author":"PM Shikhaliev","year":"2011","unstructured":"Shikhaliev PM, Fritz SG. Photon counting spectral CT versus conventional CT: comparative evaluation for breast imaging application.\u00a0Phys Med Biol. 2011;56(7):1905\u20101930. https:\/\/doi.org\/10.1088\/0031-9155\/56\/7\/001","journal-title":"Phys Med Biol."},{"issue":"9","key":"467_CR11","doi-asserted-by":"publisher","first-page":"4946","DOI":"10.1118\/1.3609097","volume":"38","author":"S Leng","year":"2011","unstructured":"Leng S, Yu L, Wang J, Fletcher JG, Mistretta CA, McCollough CH. Noise reduction in spectral CT: reducing dose and breaking the trade-off between image noise and energy bin selection.\u00a0Med Phys. 2011;38(9):4946\u20104957. https:\/\/doi.org\/10.1118\/1.3609097","journal-title":"Med Phys."},{"issue":"18","key":"467_CR12","doi-asserted-by":"publisher","first-page":"6707","DOI":"10.1088\/0031-9155\/61\/18\/6707","volume":"61","author":"Z Yu","year":"2016","unstructured":"Yu Z, Leng S, Li Z, McCollough CH. Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography.\u00a0Phys Med Biol. 2016;61(18):6707\u20106732. https:\/\/doi.org\/10.1088\/0031-9155\/61\/18\/6707","journal-title":"Phys Med Biol."},{"key":"467_CR13","doi-asserted-by":"publisher","unstructured":"Zeng D, Huang J, Zhang H, et al. Spectral CT Image Restoration via an Average Image-Induced Nonlocal Means Filter. IEEE Trans Biomed Eng. 2016;63(5):1044\u20101057.\u00a0https:\/\/doi.org\/10.1109\/TBME.2015.2476371","DOI":"10.1109\/TBME.2015.2476371"},{"key":"467_CR14","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.compmedimag.2016.07.002","volume":"53","author":"D Zeng","year":"2016","unstructured":"Zeng D, Gao Y, Huang J, et al. Penalized weighted least-squares approach for multienergy computed tomography image reconstruction via structure tensor total variation regularization.\u00a0Comput Med Imaging Graph. 2016;53:19\u201029. https:\/\/doi.org\/10.1016\/j.compmedimag.2016.07.002","journal-title":"Comput Med Imaging Graph."},{"issue":"15","key":"467_CR15","doi-asserted-by":"publisher","first-page":"4679","DOI":"10.1088\/0031-9155\/52\/15\/020","volume":"52","author":"E Roessl","year":"2007","unstructured":"Roessl E, Proksa R. K-edge imaging in x-ray computed tomography using multi-bin photon counting detectors.\u00a0Phys Med Biol. 2007;52(15):4679\u20104696. https:\/\/doi.org\/10.1088\/0031-9155\/52\/15\/020","journal-title":"Phys Med Biol."},{"issue":"11","key":"467_CR16","doi-asserted-by":"publisher","first-page":"6572","DOI":"10.1118\/1.4754587","volume":"39","author":"P He","year":"2012","unstructured":"He P, Wei B, Cong W, Wang G. Optimization of K-edge imaging with spectral CT.\u00a0Med Phys. 2012;39(11):6572\u20106579. https:\/\/doi.org\/10.1118\/1.4754587","journal-title":"Med Phys."},{"issue":"7","key":"467_CR17","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1109\/TMI.2013.2250991","volume":"32","author":"CO Schirra","year":"2013","unstructured":"Schirra CO, Roessl E, Koehler T, et al. Statistical reconstruction of material decomposed data in spectral CT.\u00a0IEEE Trans Med Imaging. 2013;32(7):1249\u20101257. https:\/\/doi.org\/10.1109\/TMI.2013.2250991","journal-title":"IEEE Trans Med Imaging."},{"issue":"2","key":"467_CR18","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1109\/42.993128","volume":"21","author":"IA Elbakri","year":"2002","unstructured":"Elbakri IA, Fessler JA. Statistical image reconstruction for polyenergetic X-ray computed tomography.\u00a0IEEE Trans Med Imaging. 2002;21(2):89\u201099. https:\/\/doi.org\/10.1109\/42.993128","journal-title":"IEEE Trans Med Imaging."},{"issue":"8","key":"467_CR19","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1109\/TMI.2014.2321098","volume":"33","author":"A Sawatzky","year":"2014","unstructured":"Sawatzky A, Xu Q, Schirra CO, Anastasio MA. Proximal ADMM for multi-channel image reconstruction in spectral X-ray CT.\u00a0IEEE Trans Med Imaging. 2014;33(8):1657\u20101668. https:\/\/doi.org\/10.1109\/TMI.2014.2321098","journal-title":"IEEE Trans Med Imaging."},{"issue":"3","key":"467_CR20","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1109\/TMI.2014.2380993","volume":"34","author":"K Kim","year":"2015","unstructured":"Kim K, Ye JC, Worstell W, et al. Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty.\u00a0IEEE Trans Med Imaging. 2015;34(3):748\u2010760. https:\/\/doi.org\/10.1109\/TMI.2014.2380993","journal-title":"IEEE Trans Med Imaging."},{"issue":"1","key":"467_CR21","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TMI.2016.2600249","volume":"36","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Mou X, Wang G, Yu H. Tensor-Based Dictionary Learning for Spectral CT Reconstruction.\u00a0IEEE Trans Med Imaging. 2017;36(1):142\u2010154. https:\/\/doi.org\/10.1109\/TMI.2016.2600249","journal-title":"IEEE Trans Med Imaging."},{"issue":"24","key":"467_CR22","doi-asserted-by":"publisher","first-page":"8699","DOI":"10.1088\/1361-6560\/61\/24\/8699","volume":"61","author":"M Wang","year":"2016","unstructured":"Wang M, Zhang Y, Liu R, Guo S, Yu H. An adaptive reconstruction algorithm for spectral CT regularized by a reference image.\u00a0Phys Med Biol. 2016;61(24):8699\u20108719. https:\/\/doi.org\/10.1088\/1361-6560\/61\/24\/8699","journal-title":"Phys Med Biol."},{"issue":"2","key":"467_CR23","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1109\/TMI.2018.2865198","volume":"38","author":"S Li","year":"2019","unstructured":"Li S, Zeng D, Peng J, et al. An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization.\u00a0IEEE Trans Med Imaging. 2019;38(2):360\u2010370. https:\/\/doi.org\/10.1109\/TMI.2018.2865198","journal-title":"IEEE Trans Med Imaging."},{"issue":"2","key":"467_CR24","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1118\/1.2836423","volume":"35","author":"GH Chen","year":"2008","unstructured":"Chen GH, Tang J, Leng S. Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets.\u00a0Med Phys. 2008;35(2):660\u2010663. https:\/\/doi.org\/10.1118\/1.2836423","journal-title":"Med Phys."},{"issue":"17","key":"467_CR25","doi-asserted-by":"publisher","first-page":"4777","DOI":"10.1088\/0031-9155\/53\/17\/021","volume":"53","author":"EY Sidky","year":"2008","unstructured":"Sidky EY, Pan X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization.\u00a0Phys Med Biol. 2008;53(17):4777\u20104807. https:\/\/doi.org\/10.1088\/0031-9155\/53\/17\/021","journal-title":"Phys Med Biol."},{"issue":"2","key":"467_CR26","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1148\/radiol.11101450","volume":"259","author":"S Singh","year":"2011","unstructured":"Singh S, Kalra MK, Gilman MD, et al. Adaptive statistical iterative reconstruction technique for radiation dose reduction in chest CT: a pilot study.\u00a0Radiology. 2011;259(2):565\u2010573. https:\/\/doi.org\/10.1148\/radiol.11101450","journal-title":"Radiology."},{"issue":"1","key":"467_CR27","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1177\/016173468400600107","volume":"6","author":"AH Andersen","year":"1984","unstructured":"Andersen AH, Kak AC. Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm.\u00a0Ultrason Imaging. 1984;6(1):81\u201094. https:\/\/doi.org\/10.1177\/016173468400600107","journal-title":"Ultrason Imaging."},{"issue":"4","key":"467_CR28","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"DL Donoho","year":"2006","unstructured":"Donoho DL. Compressed sensing. IEEE Transactions on information theory. 2006;52(4):1289-1306.","journal-title":"IEEE Transactions on information theory."},{"key":"467_CR29","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 2012."},{"key":"467_CR30","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"7","key":"467_CR31","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.\u00a0IEEE Trans Image Proces. 2017;26(7):3142\u20103155. https:\/\/doi.org\/10.1109\/TIP.2017.2662206","journal-title":"IEEE Trans Image Process."},{"key":"467_CR32","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A. P., Bishop, R., ... & Wang, Z. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 1874\u20131883.","DOI":"10.1109\/CVPR.2016.207"},{"issue":"10","key":"467_CR33","doi-asserted-by":"publisher","first-page":"e360","DOI":"10.1002\/mp.12344","volume":"44","author":"E Kang","year":"2017","unstructured":"Kang E, Min J, Ye JC. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.\u00a0Med Phys. 2017;44(10):e360\u2010e375. https:\/\/doi.org\/10.1002\/mp.12344","journal-title":"Med Phys."},{"key":"467_CR34","unstructured":"Kang, E., & Ye, J. C.\u00a0\"Wavelet domain residual network (WavResNet) for low-dose X-ray CT reconstruction.\"\u00a0arXiv preprint arXiv 1703.01383; 2017."},{"issue":"6","key":"467_CR35","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1109\/TMI.2018.2823756","volume":"37","author":"E Kang","year":"2018","unstructured":"Kang E, Chang W, Yoo J, Ye JC. Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.\u00a0IEEE Trans Med Imaging. 2018;37(6):1358\u20101369. https:\/\/doi.org\/10.1109\/TMI.2018.2823756","journal-title":"IEEE Trans Med Imaging."},{"key":"467_CR36","unstructured":"Mao X, Shen C, Yang YB. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Advances in neural information processing systems. 2016."},{"key":"467_CR37","unstructured":"Xie J, Xu L, Chen E. Image denoising and inpainting with deep neural networks. Advances in neural information processing systems. 2012."},{"key":"467_CR38","doi-asserted-by":"publisher","unstructured":"Kyong Hwan Jin, McCann MT, Froustey E, Unser M. Deep Convolutional Neural Network for Inverse Problems in Imaging.\u00a0IEEE Trans Image Process. 2017;26(9):4509\u20104522. https:\/\/doi.org\/10.1109\/TIP.2017.2713099","DOI":"10.1109\/TIP.2017.2713099"},{"key":"467_CR39","unstructured":"Han YS, Yoo J, Ye JC. Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis. 2016. arXiv preprint arXiv 1611.06391."},{"issue":"5","key":"467_CR40","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1109\/18.57199","volume":"36","author":"I Daubechies","year":"1990","unstructured":"Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE transactions on information theory. 1990;36(5):961-1005.","journal-title":"IEEE transactions on information theory."},{"key":"467_CR41","doi-asserted-by":"crossref","unstructured":"Daubechies I. Ten lectures on wavelets. Vol. 61. Siam; 1992.","DOI":"10.1137\/1.9781611970104"},{"issue":"5","key":"467_CR42","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1002\/mp.12863","volume":"45","author":"K Taguchi","year":"2018","unstructured":"Taguchi K, Stierstorfer K, Polster C, Lee O, Kappler S. Spatio-energetic cross-talk in photon counting detectors: Numerical detector model (PcTK) and workflow for CT image quality assessment.\u00a0Med Phys. 2018;45(5):1985\u20101998. https:\/\/doi.org\/10.1002\/mp.12863","journal-title":"Med Phys."},{"key":"467_CR43","unstructured":"ICRU Report 44. Tissue Substitutes in Radiation Dosimetry and Measurement. Bethesda, MD: International Commission on Radiation Units and Measurements (ICRU); 1989."},{"key":"467_CR44","unstructured":"Yu F, and Koltun V. Multi-scale context aggregation by dilated convolutions. 2015. arXiv preprint arXiv 1511.07122."},{"key":"467_CR45","unstructured":"Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations. International conference on curves and surfaces. Springer, Berlin, Heidelberg; 2010."},{"key":"467_CR46","doi-asserted-by":"crossref","unstructured":"Wang P, Chen P, Yuan Y, et al. Understanding convolution for semantic segmentation. 2018 IEEE winter conference on applications of computer vision (WACV). IEEE; 2018.","DOI":"10.1109\/WACV.2018.00163"},{"issue":"2","key":"467_CR47","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1137\/17M1141771","volume":"11","author":"JC Ye","year":"2018","unstructured":"Ye JC, Han Y, Cha E. Deep convolutional framelets: A general deep learning framework for inverse problems. SIAM Journal on Imaging Sciences. 2018;11(2):991-1048.","journal-title":"SIAM Journal on Imaging Sciences."},{"issue":"5","key":"467_CR48","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1118\/1.595025","volume":"8","author":"LA Lehmann","year":"1981","unstructured":"Lehmann LA, Alvarez RE, Macovski A, et al. Generalized image combinations in dual KVP digital radiography.\u00a0Med Phys. 1981;8(5):659\u2010667. https:\/\/doi.org\/10.1118\/1.595025.","journal-title":"Med Phys."},{"issue":"5","key":"467_CR49","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1118\/1.594520","volume":"6","author":"F Kelcz","year":"1979","unstructured":"Kelcz F, Joseph PM, Hilal SK. Noise considerations in dual energy CT scanning.\u00a0Med Phys. 1979;6(5):418\u2010425. https:\/\/doi.org\/10.1118\/1.594520.","journal-title":"Med Phys."},{"issue":"9","key":"467_CR50","doi-asserted-by":"publisher","first-page":"091903","DOI":"10.1118\/1.4892174","volume":"41","author":"HM Cho","year":"2014","unstructured":"Cho HM, Barber WC, Ding H, Iwanczyk JS, Molloi S. Characteristic performance evaluation of a photon counting Si strip detector for low dose spectral breast CT imaging.\u00a0Med Phys. 2014;41(9):091903. https:\/\/doi.org\/10.1118\/1.4892174.","journal-title":"Med Phys."},{"key":"467_CR51","doi-asserted-by":"crossref","unstructured":"Lee MJ, Lee DH, Kim DH, et al. Development of a non-linear dual-energy technique in chest radiography. Radiation Physics and Chemistry. 2020;108811.","DOI":"10.1016\/j.radphyschem.2020.108811"},{"key":"467_CR52","unstructured":"Xue H, Zhang L, Cheng Z, Xing Y, Xiao Y. An improved TV minimization algorithm for incomplete data problem in computer tomography. In:\u00a0IEEE Nuclear Science Symposuim & Medical Imaging Conference. IEEE; 2010."},{"issue":"12","key":"467_CR53","doi-asserted-by":"publisher","first-page":"15190","DOI":"10.1364\/OE.22.015190","volume":"22","author":"Y Gao","year":"2014","unstructured":"Gao Y, Bian Z, Huang J, et al. Low-dose X-ray computed tomography image reconstruction with a combined low-mAs and sparse-view protocol.\u00a0Opt Express. 2014;22(12):15190-15210.","journal-title":"Opt Express."},{"key":"467_CR54","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.ejmp.2020.11.021","volume":"80","author":"M Lee","year":"2020","unstructured":"Lee M, Kim H, Kim HJ. Sparse-view CT reconstruction based on multi-level wavelet convolution neural network.\u00a0Phys Med. 2020;80:352-362. https:\/\/doi.org\/10.1016\/j.ejmp.2020.11.021.","journal-title":"Phys Med."},{"issue":"10","key":"467_CR55","doi-asserted-by":"publisher","first-page":"100901","DOI":"10.1118\/1.4820371","volume":"40","author":"K Taguchi","year":"2013","unstructured":"Taguchi K, Iwanczyk JS. Vision 20\/20: Single photon counting x-ray detectors in medical imaging.\u00a0Med Phys. 2013;40(10):100901. https:\/\/doi.org\/10.1118\/1.4820371.","journal-title":"Med Phys."},{"issue":"3","key":"467_CR56","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1148\/rg.2019180115","volume":"39","author":"S Leng","year":"2019","unstructured":"Leng S, Bruesewitz M, Tao S, et al. Photon-counting Detector CT: System Design and Clinical Applications of an Emerging Technology.\u00a0Radiographics. 2019;39(3):729-743. https:\/\/doi.org\/10.1148\/rg.2019180115.","journal-title":"Radiographics."},{"issue":"2","key":"467_CR57","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1148\/radiol.2018172656","volume":"289","author":"MJ Willemink","year":"2018","unstructured":"Willemink MJ, Persson M, Pourmorteza A, Pelc NJ, Fleischmann D. Photon-counting CT: Technical Principles and Clinical Prospects.  Radiology. 2018;289(2):293-312. https:\/\/doi.org\/10.1148\/radiol.2018172656","journal-title":"Radiology."},{"issue":"08","key":"467_CR58","doi-asserted-by":"publisher","first-page":"P08023","DOI":"10.1088\/1748-0221\/14\/08\/P08023","volume":"14","author":"G Mahmoudi","year":"2019","unstructured":"Mahmoudi G, Fouladi MR, Ay MR, Rahmim A, Ghadiri H. Sparse-view statistical image reconstruction with improved total variation regularization for X-ray micro-CT imaging.\u00a0J Instru. 2019;14(08):P08023-P08023.","journal-title":"J Instrum."},{"issue":"21","key":"467_CR59","doi-asserted-by":"publisher","first-page":"6411","DOI":"10.1088\/0031-9155\/55\/21\/005","volume":"55","author":"TP Szczykutowicz","year":"2010","unstructured":"Szczykutowicz, TP, Chen, GH. Dual energy CT using slow kVp switching acquisition and prior image constrained compressed sensing.\u00a0Phys Med Biol\u00a02010; 55.21: 6411.","journal-title":"Phys Med Biol"},{"issue":"11","key":"467_CR60","doi-asserted-by":"publisher","first-page":"111915","DOI":"10.1118\/1.4825096","volume":"40","author":"S Abbas","year":"2013","unstructured":"Abbas, S et al. Effects of sparse sampling schemes on image quality in low\u2010dose CT.\u00a0Medical Phys\u00a02013; 40.11: 111915.","journal-title":"Medical Phys"},{"issue":"5","key":"467_CR61","doi-asserted-by":"publisher","first-page":"2540","DOI":"10.1109\/TNS.2016.2604343","volume":"63","author":"T Lee","year":"2016","unstructured":"Lee, T et al. Moving beam-blocker-based low-dose cone-beam CT.\u00a0IEEE Trans Nuclear Sci\u00a02016; 63.5: 2540\u20132549.","journal-title":"IEEE Trans Nuclear Sci"},{"issue":"12","key":"467_CR62","doi-asserted-by":"publisher","first-page":"123009","DOI":"10.1088\/0266-5611\/25\/12\/123009","volume":"25","author":"X Pan","year":"2009","unstructured":"Pan, X, Sidky, EM, Vannier, M. Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?.\u00a0Inverse Problems\u00a02009; 25.12: 123009.","journal-title":"Inverse Problems"},{"issue":"22","key":"467_CR63","doi-asserted-by":"publisher","first-page":"6575","DOI":"10.1088\/0031-9155\/55\/22\/001","volume":"55","author":"J Bian","year":"2010","unstructured":"Bian, J et al. Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT.\u00a0Phys Med Biol\u00a02010; 55.22: 6575.","journal-title":"Phys Med Biol"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00467-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-021-00467-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00467-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T17:48:24Z","timestamp":1639417704000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-021-00467-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,29]]},"references-count":63,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["467"],"URL":"https:\/\/doi.org\/10.1007\/s10278-021-00467-w","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,29]]},"assertion":[{"value":"23 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}