数据冗余信息引导的低剂量心肌灌注CT成像方法
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摘要
目的心提出一种数据冗余信息引导的低剂量心肌灌注CT成像方法。方法考虑到心肌灌注CT图像帧内含有丰富的结构冗余性,且帧间具有高度的相似性,本文提出基于非局部均值滤波(NLM)和全变分(TV)混合框架的惩罚加权最小二乘(PWLS)图像重建模型,简称为PWLS-avi NLM-TV。该模型利用了帧间结构相似性和帧内数据冗余性,能有效消除重建图像中的噪声和伪影,提高灌注序列图像帧内空间分辨率与帧间时间分辨率。结果 PWLS-avi NLM-TV相比PWLS-TV和PWLSavi NLM能更好地去除心肌灌注图像中的噪声和伪影,同时较好保持图像边缘和细节信息,进而有效区分缺血心肌与正常心肌。结论数据冗余信息引导的重建算法可有效改善低剂量心肌灌注CT成像质量,更好地为临床影像诊断服务。
Objective In the clinic, myocardial perfusion computed tomography(MPCT) imaging is commonly used to detect and assess myocardial ischemia quantitatively. However, repeated scanning on the myocardial region in the cine mode will increase the radiation dose for patients. With lowering radiation dose, the quality of images are degraded by noise induced artifact, which hampers the diagnostic accuracy. Therefore, in this paper, we propose a redundancy information induced iterative reconstruction framework for high quality MPCT images at the case of low dose. Methods MPCT images have redundant structural information within frames and highly similarity between adjacent frames. Inspired by the two properties,in this work we propose a penalized weighted least-squares(PWLS) model incorporating NLM and TV based hybrid constraints, which is referred to as PWLS-avi NLM-TV for simplicity. The proposed algorithm can effectively eliminate noise and artifacts by taking into account the similarity between adjacent frames and redundancy information within frames, which also can improve spatial resolution within frames and maintain temporal resolution. Results The experimental results on the4 D extended cardiac-torso(XCAT) phantom and preclinical porcine dataset demonstrates that the PWLS-avi NLM-TV algorithm obtains better performance in terms of noise reduction and artifacts suppression than the PWLS-TV and PWLSavi NLM algorithm. Moreover, the proposed algorithm can preserve the edges and detail information thereby efficiently differentiate ischemia from myocardium. Conclusion The present redundancy information induced reconstruction algorithm can reconstruct high-quality images from low-dose MPCT for better clinical imaging diagnosis.
Objective In the clinic, myocardial perfusion computed tomography(MPCT) imaging is commonly used to detect and assess myocardial ischemia quantitatively. However, repeated scanning on the myocardial region in the cine mode will increase the radiation dose for patients. With lowering radiation dose, the quality of images are degraded by noise induced artifact, which hampers the diagnostic accuracy. Therefore, in this paper, we propose a redundancy information induced iterative reconstruction framework for high quality MPCT images at the case of low dose. Methods MPCT images have redundant structural information within frames and highly similarity between adjacent frames. Inspired by the two properties,in this work we propose a penalized weighted least-squares(PWLS) model incorporating NLM and TV based hybrid constraints, which is referred to as PWLS-avi NLM-TV for simplicity. The proposed algorithm can effectively eliminate noise and artifacts by taking into account the similarity between adjacent frames and redundancy information within frames, which also can improve spatial resolution within frames and maintain temporal resolution. Results The experimental results on the4 D extended cardiac-torso(XCAT) phantom and preclinical porcine dataset demonstrates that the PWLS-avi NLM-TV algorithm obtains better performance in terms of noise reduction and artifacts suppression than the PWLS-TV and PWLSavi NLM algorithm. Moreover, the proposed algorithm can preserve the edges and detail information thereby efficiently differentiate ischemia from myocardium. Conclusion The present redundancy information induced reconstruction algorithm can reconstruct high-quality images from low-dose MPCT for better clinical imaging diagnosis.
引文
[1]Writing Group Members,Mozaffarian D,Benjamin EJ,et al.Executive summary:heart disease and stroke statistics--2016update:a report from the American heart association[J].Circulation,2016,133(4):447-54.
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[6]Caruso D,Eid M,Schoepf UJ,et al.Dynamic CT myocardial perfusion imaging[J].Eur J Radiol,2016,85(10):1893-9.
[7]Mayo JR,Leipsic JA.Radiation dose in cardiac CT[J].AJR Am J Roentgenol,2009,192(3):646.
[8]Othman AE,Brockmann C,Yang Z,et al.Impact of image denoising on image quality,quantitative parameters and sensitivity of ultralow-dose volume perfusion CT imaging[J].Eur Radiol,2016,26(1):167-74.
[9]Rossi A,Merkus D,Klotz E,et al.Stress myocardial perfusion:imaging with multidetector CT[J].Radiology,2014,270(1):25-46.
[10]Kim SM,Cho YK,Choe YH.Adenosine-stress dynamic myocardial perfusion imaging using 128-slice dual-source CT in patients with normal body mass indices:effect of tube voltage,tube current,and Iodine concentration on image quality and radiation dose[J].Int J Cardiovas Imaging,2014,30(2):95-103.
[11]Li Y,Niu K,Chen GH.Statistical model based iterative Reconstruction in myocardial CT perfusion:exploitation of the low dimensionality of the spatial-temporal image matrix[C]//Proc SPIE,2015,9412(4):785-8.
[12]Lin Y,Samei E.An efficient polyenergetic SART(p SART)Reconstruction algorithm for quantitative myocardial CT perfusion[J].Med Phys,2014,41(2):021911-4.
[13]Bian ZY,Zeng D,Zhang Z,et al.Low-dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior[J].Med Phys,2017,44(9):e188-201.
[14]Tao Y,Chen GH,Hacker TA,et al.Low dose dynamic CT myocardial perfusion imaging using a statistical iterative Reconstruction method[J].Med Phys,2014,41(7):071914.
[15]Zeng D,Gong CF,Bian ZY,et al.Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization:a preclinical study[J].Phys Med Biol,2016,61(22):8135.
[16]Ma JH,Liang ZR,Fan Y,et al.Variance analysis of x-ray CT sinograms in the presence of electronic noise background[J].Med Phys,2012,39(7):4051-65.
[17]Buades A,Coll B,Morel JM.A non-local algorithm for image denoising[C]//Computer Vision and Pattern Recognition,2005.CVPR 2005.IEEE Computer Society Conference on,2005:60-5.
[18]Bougleux S,Cohen LD,PeyréG.Non-local regularization of inverse problems[J].Inver Problems Imaging,2017,5(2):511-30.
[19]Zhang H,Huang J,Ma J,et al.Iterative Reconstruction for x-ray computed tomography using prior-image induced nonlocal regularization[J].IEEE Trans Biomed Eng,2014,61(9):2367-78.
[20]Ma J,Huang J,Feng Q,et al.Low-dose computed tomography image restoration using previous normal-dose scan[J].Med Phys,2011,38(10):5713-31.
[21]Zeng D,Huang J,Zhang H,et al.Spectral CT image restoration via an average Image-Induced nonlocal means filter[J].IEEE Trans Biomed Eng,2016,63(5):1044-57.
[22]Zhang HJ,Zeng D,Lin JH,et al.Iterative Reconstruction for dual energy CT with an average image-induced nonlocal means regularization[J].Phys Med Biol,2017,62(13):5556-74.
[23]Norris H,Zhang Y,Bond J,et al.A set of 4D pediatric XCAT reference phantoms for multimodality research[J].Med Phys,2014,41(3):033701-11.
[24]Segars WP,Sturgeon G,Mendonca S,et al.4D XCAT phantom for multimodality imaging research[J].Med Phys,2010,37(9):4902-15.
[25]Bindschadler M,Modgil D,Branch KR,et al.Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT[J].Phys Med Biol,2014,59(7):1533-56.
[26]Bindschadler M,Modgil D,Branch KR,et al.Simulation evaluation of quantitative myocardial perfusion assessment from cardiac CT[J].Proc SPIE Int Soc Opt Eng,2016,9033(9033):903303.
[27]Zeng D,Huang J,Bian ZY,et al.A simple Low-Dose X-Ray CT simulation from High-Dose scan[J].IEEE Trans Nucl Sci,2015,62(5,2):2226-33.
[28]Wu O,?stergaard L,Weisskoff RM,et al.Tracer arrival timinginsensitive technique for estimating flow in Mr perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix[J].Magn Reson Med,2003,50(1):164-74.
[29]Bamberg F,Becker A,Schwarz F,et al.Detection of hemodynamically significant coronary artery stenosis:incremental diagnostic value of dynamic CT-based myocardial perfusion imaging[J].Radiology,2011,260(3):689-98.
[30]George RT,Jerosch-Herold M,Silva C,et al.Quantification of myocardial perfusion using dynamic 64-detector computed tomography[J].Invest Radiol,2007,42(12):815-22.
[31]Wang Z,Bovik AC.A Universal image quality index[J].IEEE Signal Process Lett,2002,9(3):81-4.
[32]Li YS,Niu K,Chen GH.Statistical model based iterative reconstruction in myocardial CT perfusion:exploitation of the low dimensionality of the Spatial-Temporal image matrix[C]//Medical Imaging 2015:Physics of Medical Imaging,2015,9412(4):785-8.
[2]Williams MC,Newby DE.CT myocardial perfusion imaging:current status and future directions[J].Clin Radiol,2016,71(8,SI):739-49.
[3]Steg PG,Ducrocq G.Future of the prevention and treatment of coronary artery disease[J].Circ J,2016,80(5):1067-72.
[4]Varga-Szemes A,Meinel FG,De Cecco CN,et al.CT myocardial perfusion imaging[J].AJR Am J Roentgenol,2015,204(3):487-97.
[5]Fahmi R,Eck BL,Levi J,et al.Quantitative myocardial perfusion imaging in a porcine ischemia model using a prototype spectral detector CT system[J].Phys Med Biol,2016,61(6):2407-31.
[6]Caruso D,Eid M,Schoepf UJ,et al.Dynamic CT myocardial perfusion imaging[J].Eur J Radiol,2016,85(10):1893-9.
[7]Mayo JR,Leipsic JA.Radiation dose in cardiac CT[J].AJR Am J Roentgenol,2009,192(3):646.
[8]Othman AE,Brockmann C,Yang Z,et al.Impact of image denoising on image quality,quantitative parameters and sensitivity of ultralow-dose volume perfusion CT imaging[J].Eur Radiol,2016,26(1):167-74.
[9]Rossi A,Merkus D,Klotz E,et al.Stress myocardial perfusion:imaging with multidetector CT[J].Radiology,2014,270(1):25-46.
[10]Kim SM,Cho YK,Choe YH.Adenosine-stress dynamic myocardial perfusion imaging using 128-slice dual-source CT in patients with normal body mass indices:effect of tube voltage,tube current,and Iodine concentration on image quality and radiation dose[J].Int J Cardiovas Imaging,2014,30(2):95-103.
[11]Li Y,Niu K,Chen GH.Statistical model based iterative Reconstruction in myocardial CT perfusion:exploitation of the low dimensionality of the spatial-temporal image matrix[C]//Proc SPIE,2015,9412(4):785-8.
[12]Lin Y,Samei E.An efficient polyenergetic SART(p SART)Reconstruction algorithm for quantitative myocardial CT perfusion[J].Med Phys,2014,41(2):021911-4.
[13]Bian ZY,Zeng D,Zhang Z,et al.Low-dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior[J].Med Phys,2017,44(9):e188-201.
[14]Tao Y,Chen GH,Hacker TA,et al.Low dose dynamic CT myocardial perfusion imaging using a statistical iterative Reconstruction method[J].Med Phys,2014,41(7):071914.
[15]Zeng D,Gong CF,Bian ZY,et al.Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization:a preclinical study[J].Phys Med Biol,2016,61(22):8135.
[16]Ma JH,Liang ZR,Fan Y,et al.Variance analysis of x-ray CT sinograms in the presence of electronic noise background[J].Med Phys,2012,39(7):4051-65.
[17]Buades A,Coll B,Morel JM.A non-local algorithm for image denoising[C]//Computer Vision and Pattern Recognition,2005.CVPR 2005.IEEE Computer Society Conference on,2005:60-5.
[18]Bougleux S,Cohen LD,PeyréG.Non-local regularization of inverse problems[J].Inver Problems Imaging,2017,5(2):511-30.
[19]Zhang H,Huang J,Ma J,et al.Iterative Reconstruction for x-ray computed tomography using prior-image induced nonlocal regularization[J].IEEE Trans Biomed Eng,2014,61(9):2367-78.
[20]Ma J,Huang J,Feng Q,et al.Low-dose computed tomography image restoration using previous normal-dose scan[J].Med Phys,2011,38(10):5713-31.
[21]Zeng D,Huang J,Zhang H,et al.Spectral CT image restoration via an average Image-Induced nonlocal means filter[J].IEEE Trans Biomed Eng,2016,63(5):1044-57.
[22]Zhang HJ,Zeng D,Lin JH,et al.Iterative Reconstruction for dual energy CT with an average image-induced nonlocal means regularization[J].Phys Med Biol,2017,62(13):5556-74.
[23]Norris H,Zhang Y,Bond J,et al.A set of 4D pediatric XCAT reference phantoms for multimodality research[J].Med Phys,2014,41(3):033701-11.
[24]Segars WP,Sturgeon G,Mendonca S,et al.4D XCAT phantom for multimodality imaging research[J].Med Phys,2010,37(9):4902-15.
[25]Bindschadler M,Modgil D,Branch KR,et al.Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT[J].Phys Med Biol,2014,59(7):1533-56.
[26]Bindschadler M,Modgil D,Branch KR,et al.Simulation evaluation of quantitative myocardial perfusion assessment from cardiac CT[J].Proc SPIE Int Soc Opt Eng,2016,9033(9033):903303.
[27]Zeng D,Huang J,Bian ZY,et al.A simple Low-Dose X-Ray CT simulation from High-Dose scan[J].IEEE Trans Nucl Sci,2015,62(5,2):2226-33.
[28]Wu O,?stergaard L,Weisskoff RM,et al.Tracer arrival timinginsensitive technique for estimating flow in Mr perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix[J].Magn Reson Med,2003,50(1):164-74.
[29]Bamberg F,Becker A,Schwarz F,et al.Detection of hemodynamically significant coronary artery stenosis:incremental diagnostic value of dynamic CT-based myocardial perfusion imaging[J].Radiology,2011,260(3):689-98.
[30]George RT,Jerosch-Herold M,Silva C,et al.Quantification of myocardial perfusion using dynamic 64-detector computed tomography[J].Invest Radiol,2007,42(12):815-22.
[31]Wang Z,Bovik AC.A Universal image quality index[J].IEEE Signal Process Lett,2002,9(3):81-4.
[32]Li YS,Niu K,Chen GH.Statistical model based iterative reconstruction in myocardial CT perfusion:exploitation of the low dimensionality of the Spatial-Temporal image matrix[C]//Medical Imaging 2015:Physics of Medical Imaging,2015,9412(4):785-8.