{"id":11,"date":"2022-05-20T07:53:08","date_gmt":"2022-05-19T23:53:08","guid":{"rendered":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/?page_id=11"},"modified":"2022-05-20T08:07:04","modified_gmt":"2022-05-20T00:07:04","slug":"publications","status":"publish","type":"page","link":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n

Journal<\/h2>\n\n\n\n
  1. Y. Long, J. A. Fessler, and J. M. Balter.Accuracy estimation for projection-to-volume targeting during rotational therapy: A feasibility study<\/a>.Med. Phys., 37(6):2480\u201390, Jun. 2010.<\/li>
  2. Y. Long, J. A. Fessler, and J. M. Balter.3D forward and back-projection for X-ray CT using separable footprints<\/a>.IEEE Trans. Med. Imag., 29(11):1839\u201350, Nov. 2010.<\/li>
  3. Y. Long and J. A. Fessler.Multi-material decomposition using statistical image reconstruction<\/a>.IEEE Trans. Med. Image., 33(8):1\u201313, Aug. 2014.<\/li>
  4. X. Rui, L. Cheng, Y. Long, L. Fu, A. M. Alessio, E. Asma, P. E. Kinahan and B. De Man.Ultra-low dose CT attenuation correction for PET\/CT: analysis of sparse view data acquisition and reconstruction algorithms<\/a>.Phys. Med. Biol., 60 (19): 7439-7462, Sep. 2015.<\/li>
  5. Y. Xue, R. Ruan, X. Hu, Y. Kuang, J. Wang, Y. Long, and T. Niu. Statistical image-domain multi-material decomposition for dual-energy CT<\/a>.Med. Phys.,44(3): 886\u2013901, doi:10.1002\/mp.12096, Mar. 2017.<\/li>
  6. Y. Zan, Y. Long, K. Chen, B. Li, Q. Huang and G. T. Gullberg. “Design of a short non-uniform acquisition protocol for quantitative analysis in dynamic cardiac SPECT imaging – a retrospective animal study.<\/a>“Med. Phys., 44(7):3639-3649, July 2017.<\/li>
  7. X. Zheng, S. Ravishankar, Y. Long and J. Fessler. “PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction<\/a>.” IEEE Trans. Med. Image., 37(6):1498-510, June 2018.<\/li>
  8. Q. Ding, T. Niu, X. Zhang and Y. Long. “Image-domain multi-material decomposition for dual-energy CT based on prior information of material images.<\/a>” Medical Physics, 45(8):3614-3626, August 2018.<\/li><\/ol>\n\n\n\n

    Conference Proceedings Papers<\/h2>\n\n\n\n
    1. Y. Long, J. A. Fessler, and J. M. Balter. “A 3D forward and back-projection method for X-ray CT using separable footprint”.In Proc. Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med, pp. 146\u20139, 2009. Winner of poster award.pdf<\/a><\/li>
    2. Y. Long, J. A. Fessler, and J. M. Balter. “3D forward and back-projection for X-ray CT using separable footprints with trapezoid functions”.In Proc. First Intl. Mtg. on image formation in X-ray CT, pp. 216\u20139, 2010.pdf<\/a><\/li>
    3. Y. Long and J. A. Fessler. “Multi-material decomposition using statistical image reconstruction in X-ray CT”.In Proc. 2nd Intl. Mtg. on image formation in X-ray CT, pp. 413\u20136, 2012. pdf<\/a><\/li>
    4. Y. Long, L. Cheng, X. Rui, B. De Man, A. Alessio, E. Asma and P. Kinahan. “Analysis of Ultra-Low Dose CT Acquisition Protocol and Reconstruction Algorithm Combinations for PET Attenuation Correction”.In Proc. Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med., pp. 400-3, 2013.pdf<\/a><\/li>
    5. X. Rui, Y. Long, A. Alessio, E. Asma, P. Kinahan and B. De Man. “Theoretical Analysis of Optimal CT Spectrum for PET Attenuation Correction<\/a>“.In Proc. IEEE Nuc. Sci. Symp. Med. Im. Conf., pp1-6,2013.<\/li>
    6. Y. Long, H. Gao, M. Wu, J. D. Pack, H. Xu, K. Tao, P. F. Fitzgerald and B. De Man. “Physics- based modeling of X-Ray CT measurements with energy-integrating detectors<\/a>“.Proc. SPIE 9033 Medical Imaging 2014: Phys. Med. Im.,pp.90334S, 2014.<\/li>
    7. J. Wang, Y. Long, L. Fu, X. Rui and B. De Man. “Sinogram rebinning and frequency boosting for high resolution iterative CT reconstruction with focal spot wobbling<\/a>“.Proc. SPIE 9033 Medical Imaging 2014: Phys. Med. Im.,pp. 903333, 2014.<\/li>
    8. H. Xu, K. Tao, P. GK, M. Wu, X. Cao, Y. Long, M. Yan, Y. Yao and B. De Man. “Hybrid model for computed tomography simulations and post-patient collimator design<\/a>“.Proc. SPIE 9033 Medical Imaging 2014: Phys. Med. Im.,pp. 90334R, 2014.<\/li>
    9. X. Zheng, Z. Lu, S. Ravishankar, Y. Long, J. A. Fessler. “Low dose CT image reconstruction with trained sparsifying transform<\/a>“.Proc. IEEE Wkshp. on Image, Video, Multidim. Signal Proc., pp. 1-5, July 11-12 2016, Bordeaux, France.<\/li>
    10. M. Yang, Y. Long and T. Niu. “Statistical Image-Domain Multi-Material Decomposition for Dual-Energy CT”.In Proc. 4th Intl. Mtg. on image formation in X-ray CT, pp. 65-8, July 18-22, 2016, Bamberg, Germany. pdf<\/a><\/li>
    11. Q. Ding, Y. Long, X. Zhang and J. A. Fessler. “Modeling Mixed Poisson-Gaussian Noise in Statistical Image Reconstruction for X-Ray CT”.In Proc. 4th Intl. Mtg. on image formation in X-ray CT, pp. 399-402, July 18-22, 2016, Bamberg, Germany.pdf<\/a><\/li>
    12. X. Xie, M. G. McGaffin, Y. Long, J. A. Fessler, M. Wen, J. Lin. “Accelerating separable footprint (SF) forward and back projection on GPU<\/a>“.Proc. SPIE 10132 Medical Imaging 2017: Phys. Med. Im., 101322S, Feb. 11-16, 2017, Orlando, Florida, USA.<\/li>
    13. X. Zheng, S. Ravishankar, Y. Long and J. A. Fessler. \u201cUnion of Learned Sparsifying Transforms Based Low-Dose 3D CT Image Reconstruction.\u201d Proc. Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med., page 69-72, June 18-23, 2017, Xi\u2019an, China.pdf<\/a><\/li>
    14. I. Y. Chun, X. Zheng, Y. Long, and J. A. Fessler. \u201cEfficient Sparse-View X-Ray CT Reconstruction Using L1 Regularization with Learned Sparsifying Transform.\u201d Proc. Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med., page 115-119, June 18-23, 2017. Xi\u2019an, China.pdf<\/a><\/li>
    15. Q. Ding, T. Niu, X. Zhang and Y. Long. \u201cImage-domain multile-material decomposition for dual-energy CT via total nuclear norm and L0 norm.\u201d Proc. Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med., page 154-159, June 18-23, 2017. Xi\u2019an, China.pdf<\/a><\/li>
    16. S. Ye, S. Ravishankar, Y. Long, and J. A. Fessler. “Adaptive sparse modeling and shifted-Poisson likelihood based approach for low-dose CT image reconstruction<\/a>.” In Proc. IEEE Wkshp. Machine Learning for Signal Proc., pages 154-159, September 25-28, 2017, Roppongi, Tokyo, Japan.<\/li>
    17. Z. Li, S. Ravishankar, Y. Long, and J. A. Fessler. “Image-Domain Material Decomposition Using Data-Driven Sparsity Models for Dual-Energy CT<\/a>.” In Proc. IEEE Intl. Symp. Biomed. Image., pages 52-6, April 4-7, 2018, Washington D.C., USA. Award for best student paper.<\/li>
    18. Z. Li, S. Ravishankar, Y. Long, and J. A. Fessler. “Learned missed material models for efficient clustering based dual-energy CT image decomposition.” In Proc. IEEE GlobalSIP, November 26-29, 2018, Anaheim, California, USA.<\/li><\/ol>\n\n\n\n

      Conference Abstracts<\/h2>\n\n\n\n
      1. Y. Long, J. A. Fessler, and J. M. Balter. “Accuracy limits for projection-to-volume targeting during arc therapy”.Proc. Amer. Assoc. Phys. Med., pp. 3154, 2010.<\/li>
      2. Y. Long, J. A. Fessler, and J. M. Balter. “Two-material decomposition from a single CT scan using statistical image reconstruction”.Proc. Amer. Assoc. Phys. Med., pp. 3810, 2011.<\/li>
      3. J. M. Balter, Y. Long, M. M. Folkerts, G. C. Sharp, T. R. Bortfeld and J. A. Fessler. “An open platform for 2D-3D image registration experiments”.Proc. Amer. Assoc. Phys. Med., pp. 3450, 2011.<\/li>
      4. X. Zheng, S. Ravishankar, Z. Lu, Y. Long, J. A. Fessler. “Image reconstruction for low dose X-ray CT using learned overcomplete sparsifying transforms”. Gordon Res. Conf. on Imaging Science, 2016.<\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"

        Journal Y. Long, J. A. Fessler, and J. M. Balter.Accuracy estimation for projection-to-volume targeting during rotational therapy: A feasibility study.Med. Phys., 37(6):2480\u201390, Jun. 2010. Y. Long, J. A. Fessler, and J. M. Balter.3D forward and back-projection for X-ray CT using separable footprints.IEEE Trans. Med. Imag., 29(11):1839\u201350, Nov. 2010. Y. Long and J. A. Fessler.Multi-material decomposition […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-11","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/wp-json\/wp\/v2\/pages\/11","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/wp-json\/wp\/v2\/comments?post=11"}],"version-history":[{"count":1,"href":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/wp-json\/wp\/v2\/pages\/11\/revisions"}],"predecessor-version":[{"id":12,"href":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/wp-json\/wp\/v2\/pages\/11\/revisions\/12"}],"wp:attachment":[{"href":"https:\/\/sites.gc.sjtu.edu.cn\/ylong\/wp-json\/wp\/v2\/media?parent=11"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}