Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI
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Andrew P. Leynes | F. Wiesinger | Youngho Seo | S. Kaushik | T. Hope | D. Shanbhag | Jaewon Yang | P. Larson
[1] H. Zaidi,et al. Impact of Time-of-Flight PET on Quantification Errors in MR Imaging–Based Attenuation Correction , 2015, The Journal of Nuclear Medicine.
[2] H. Quick,et al. Magnetic Resonance–Based Attenuation Correction for PET/MR Hybrid Imaging Using Continuous Valued Attenuation Maps , 2013, Investigative radiology.
[3] G. Hermosillo,et al. Dixon Sequence with Superimposed Model-Based Bone Compartment Provides Highly Accurate PET/MR Attenuation Correction of the Brain , 2016, The Journal of Nuclear Medicine.
[4] R. Wahl,et al. From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors , 2009, Journal of Nuclear Medicine.
[5] G. Delso,et al. Evaluation of an Atlas-Based PET Head Attenuation Correction Using PET/CT & MR Patient Data , 2012, IEEE Transactions on Nuclear Science.
[6] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] N. Alpert,et al. Bias Atlases for Segmentation-Based PET Attenuation Correction Using PET-CT and MR , 2013, IEEE Transactions on Nuclear Science.
[8] A. Buck,et al. Evaluation of Atlas-Based Attenuation Correction for Integrated PET/MR in Human Brain: Application of a Head Atlas and Comparison to True CT-Based Attenuation Correction , 2016, Journal of Nuclear Medicine.
[9] E. R. Kops,et al. Hybrid approach for attenuation correction in PET/MR scanners , 2014 .
[10] Xiao Han,et al. MR‐based synthetic CT generation using a deep convolutional neural network method , 2017, Medical physics.
[11] Yasheng Chen,et al. MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units , 2015, NeuroImage.
[12] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[13] G. Hermosillo,et al. Whole-Body PET/MR Imaging: Quantitative Evaluation of a Novel Model-Based MR Attenuation Correction Method Including Bone , 2015, The Journal of Nuclear Medicine.
[14] S. Majumdar,et al. Ultrashort echo time MRI of cortical bone at 7 tesla field strength: A feasibility study , 2011, Journal of magnetic resonance imaging : JMRI.
[15] S. Thiruvenkadam,et al. Comparison of 4-class and continuous fat/water methods for whole-body, MR-based PET attenuation correction , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).
[16] S. Holm,et al. Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging , 2015, Physics in medicine and biology.
[17] Paul Kinahan,et al. Attenuation correction for a combined 3D PET/CT scanner. , 1998, Medical physics.
[18] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[19] I. Burger,et al. PET/MR imaging of bone lesions – implications for PET quantification from imperfect attenuation correction , 2012, European Journal of Nuclear Medicine and Molecular Imaging.
[20] G. Delso,et al. Zero TE MR bone imaging in the head , 2016, Magnetic resonance in medicine.
[21] A. Buck,et al. Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain 18F-FDG PET/MRI: Comparison with Atlas Attenuation Correction , 2016, The Journal of Nuclear Medicine.
[22] Paul Kinahan,et al. Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. , 2010, Seminars in ultrasound, CT, and MR.
[23] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Stefan Förster,et al. MR-Based Attenuation Correction Using Ultrashort-Echo-Time Pulse Sequences in Dementia Patients , 2015, The Journal of Nuclear Medicine.
[25] Gaspar Delso,et al. Design Features and Mutual Compatibility Studies of the Time-of-Flight PET Capable GE SIGNA PET/MR System , 2016, IEEE Transactions on Medical Imaging.
[26] Jeffry S Nyman,et al. Characterization of 1H NMR signal in human cortical bone for magnetic resonance imaging , 2010, Magnetic resonance in medicine.
[27] Andrew P. Leynes,et al. Hybrid ZTE/Dixon MR‐based attenuation correction for quantitative uptake estimation of pelvic lesions in PET/MRI , 2017, Medical physics.
[28] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[29] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[30] Mark Bydder,et al. Qualitative and quantitative ultrashort echo time (UTE) imaging of cortical bone. , 2010, Journal of magnetic resonance.
[31] Ilja Bezrukov,et al. MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods , 2011, The Journal of Nuclear Medicine.
[32] S. Vandenberghe,et al. MRI-Based Attenuation Correction for PET/MRI Using Ultrashort Echo Time Sequences , 2010, Journal of Nuclear Medicine.
[33] Habib Zaidi,et al. Atlas-guided non-uniform attenuation correction in cerebral 3D PET imaging , 2005, NeuroImage.
[34] Christoph Meinel,et al. Deep Learning for Medical Image Analysis , 2018, Journal of Pathology Informatics.
[35] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[36] F. Wiesinger,et al. Evaluation of Sinus/Edge-Corrected Zero-Echo-Time–Based Attenuation Correction in Brain PET/MRI , 2017, The Journal of Nuclear Medicine.
[37] Guy B. Williams,et al. Attenuation Correction Methods Suitable for Brain Imaging with a PET/MRI Scanner: A Comparison of Tissue Atlas and Template Attenuation Map Approaches , 2011, The Journal of Nuclear Medicine.
[38] Yaozong Gao,et al. Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks , 2016, LABELS/DLMIA@MICCAI.
[39] J. Gee,et al. The Insight ToolKit image registration framework , 2014, Front. Neuroinform..