{"_id":"CMnSkcY9f9q7R2Zr6","bibbaseid":"zeng-lin-li-lu-skopec-fessler-myers-performanceofadeeplearningbasedctimagedenoisingmethodgeneralizabilityoverdosereconstructionkernelandslicethickness-2022","author_short":["Zeng, R.","Lin, C. Y.","Li, Q.","Lu, J.","Skopec, M.","Fessler, J. A.","Myers, K. J."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["R."],"propositions":[],"lastnames":["Zeng"],"suffixes":[]},{"firstnames":["C.","Y."],"propositions":[],"lastnames":["Lin"],"suffixes":[]},{"firstnames":["Q."],"propositions":[],"lastnames":["Li"],"suffixes":[]},{"firstnames":["J."],"propositions":[],"lastnames":["Lu"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Skopec"],"suffixes":[]},{"firstnames":["J.","A."],"propositions":[],"lastnames":["Fessler"],"suffixes":[]},{"firstnames":["K.","J."],"propositions":[],"lastnames":["Myers"],"suffixes":[]}],"title":"Performance of a deep learning-based CT image denoising method: Generalizability over dose, reconstruction kernel and slice thickness","journal":"Med. Phys.","volume":"49","number":"2","pages":"836–53","month":"February","doi":"10.1002/mp.15430","year":"2022","bibtex":"@ARTICLE{zeng:22:poa,\n author = {R. Zeng and C. Y. Lin and Q. Li and J. Lu and M. Skopec and J. A. Fessler and K. J. Myers},\n title = {Performance of a deep learning-based {CT} image denoising method: {Generalizability} over dose, reconstruction kernel and slice thickness},\n journal = {{Med. Phys.}},\n volume = 49,\n number = 2,\n pages = {{836--53}},\n month = feb,\n doi = {10.1002/mp.15430},\n year = 2022\n}\n\n","author_short":["Zeng, R.","Lin, C. Y.","Li, Q.","Lu, J.","Skopec, M.","Fessler, J. A.","Myers, K. J."],"key":"zeng:22:poa","id":"zeng:22:poa","bibbaseid":"zeng-lin-li-lu-skopec-fessler-myers-performanceofadeeplearningbasedctimagedenoisingmethodgeneralizabilityoverdosereconstructionkernelandslicethickness-2022","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"http://web.eecs.umich.edu/~fessler/papers/lists/t,jour.bib","dataSources":["kd6C5knneZoMrc2zM"],"keywords":[],"search_terms":["performance","deep","learning","based","image","denoising","method","generalizability","over","dose","reconstruction","kernel","slice","thickness","zeng","lin","li","lu","skopec","fessler","myers"],"title":"Performance of a deep learning-based CT image denoising method: Generalizability over dose, reconstruction kernel and slice thickness","year":2022}