A review of PET attenuation correction methods for PET-MR

[1]  Sayash Kapoor,et al.  Leakage and the reproducibility crisis in machine-learning-based science , 2023, Patterns.

[2]  I. Law,et al.  DeepDixon synthetic CT for [18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants , 2023, Frontiers in Neuroscience.

[3]  Seyed Iman Zare Estakhraji,et al.  On the effect of training database size for MR-based synthetic CT generation in the head , 2023, Comput. Medical Imaging Graph..

[4]  John A. Onofrey,et al.  Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application , 2022, Physics in medicine and biology.

[5]  V. Treyer,et al.  Attenuation Correction Using Template PET Registration for Brain PET: A Proof-of-Concept Study , 2022, J. Imaging.

[6]  A. Rahmim,et al.  Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning , 2022, European Journal of Nuclear Medicine and Molecular Imaging.

[7]  A. Rominger,et al.  Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction , 2022, Nature Communications.

[8]  A. Hansen,et al.  A deep learning-based whole-body solution for PET/MRI attenuation correction , 2022, EJNMMI Physics.

[9]  Huafeng Liu,et al.  DeTransUnet: attenuation correction of gated cardiac images without structural information , 2022, Physics in medicine and biology.

[10]  M. Conti,et al.  Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners , 2022, European Journal of Nuclear Medicine and Molecular Imaging.

[11]  A. Narayanan,et al.  Leakage and the Reproducibility Crisis in ML-based Science , 2022, ArXiv.

[12]  Paul Kinahan,et al.  Synthetic PET via Domain Translation of 3-D MRI , 2022, IEEE Transactions on Radiation and Plasma Medical Sciences.

[13]  Huafeng Liu,et al.  Invertible AC-flow: Direct Attenuation Correction Of Pet Images Without Ct Or Mr Images , 2022, 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI).

[14]  M. Lubberink,et al.  Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR. , 2022, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[15]  B. Jakoby,et al.  Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients , 2022, EJNMMI Physics.

[16]  John A. Onofrey,et al.  Deep learning–based attenuation correction for whole-body PET — a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine , 2022, European Journal of Nuclear Medicine and Molecular Imaging.

[17]  M. Figl,et al.  Technical note: A PET/MR coil with an integrated, orbiting 511 keV transmission source for PET/MR imaging validated in an animal study , 2022, Medical physics.

[18]  Bao Yang,et al.  Delayed PET imaging using image synthesis network and nonrigid registration without additional CT scan. , 2022, Medical physics.

[19]  H. Zaidi,et al.  Robust-Deep: A Method for Increasing Brain Imaging Datasets to Improve Deep Learning Models’ Performance and Robustness , 2022, Journal of Digital Imaging.

[20]  D. Visvikis,et al.  PET respiratory motion correction: quo vadis? , 2021, Physics in medicine and biology.

[21]  Hongyoon Choi,et al.  Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography , 2021, European Journal of Nuclear Medicine and Molecular Imaging.

[22]  R. Boellaard,et al.  EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3 , 2021, European Journal of Nuclear Medicine and Molecular Imaging.

[23]  M. Conti,et al.  A CT‐less approach to quantitative PET imaging using the LSO intrinsic radiation for long‐axial FOV PET scanners , 2021, Medical physics.

[24]  A. Rominger,et al.  Development of a deep learning method for CT-free correction for an ultra-long axial field of view PET scanner , 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[25]  A. Rahmim,et al.  PET-QA-Net: Towards Routine PET Image Artifact Detection and Correction using Deep Convolutional Neural Networks , 2021, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[26]  A. Rahmim,et al.  Deep Active Learning Model for Adaptive PET Attenuation and Scatter Correction in Multi-Centric Studies , 2021, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[27]  C. Levin,et al.  Evaluation of a Generative Adversarial Network for MR-Based PET Attenuation Correction in PET/MR , 2021, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[28]  H. Zaidi,et al.  Deep Learning-assisted simultaneous MRI-based Attenuation Correction and Full-Dose Synthesis from Non-Attenuated Low-Dose PET Images , 2021, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[29]  Maike E Lindemann,et al.  CAD-based hardware attenuation correction in PET/MRI: first methodical investigations and clinical application of a 16-channel RF breast coil. , 2021, Medical physics.

[30]  P. Yi,et al.  Limited generalizability of deep learning algorithm for pediatric pneumonia classification on external data , 2021, Emergency Radiology.

[31]  J. Pfeuffer,et al.  Accelerated Stack-of-Spirals Free-Breathing Three-Dimensional Ultrashort Echo Time Lung Magnetic Resonance Imaging: A Feasibility Study in Patients With Breast Cancer , 2021, Frontiers in Oncology.

[32]  J. Lagendijk,et al.  Minimizing the need for coil attenuation correction in integrated PET/MRI at 1.5 T using low-density MR-linac receive arrays , 2021, Physics in medicine and biology.

[33]  H. Zaidi,et al.  MRI‐guided attenuation correction in torso PET/MRI: Assessment of segmentation‐, atlas‐, and deep learning‐based approaches in the presence of outliers , 2021, Magnetic resonance in medicine.

[34]  J. Nuyts,et al.  2-D Feasibility Study of Joint Reconstruction of Attenuation and Activity in Limited Angle TOF-PET , 2021, IEEE Transactions on Radiation and Plasma Medical Sciences.

[35]  Seongho Seo,et al.  Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network , 2021, Electronics.

[36]  N. Malpica,et al.  Evaluation of Deep Learning–Based Approaches to Segment Bowel Air Pockets and Generate Pelvic Attenuation Maps from CAIPIRINHA-Accelerated Dixon MR Images , 2021, The Journal of Nuclear Medicine.

[37]  I. Law,et al.  Deep-learning-based attenuation correction in dynamic [15O]H2O studies using PET/MRI in healthy volunteers , 2021, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[38]  Zeynep Akata,et al.  Uncertainty-Guided Progressive GANs for Medical Image Translation , 2021, MICCAI.

[39]  Ciprian Catana,et al.  Direct Reconstruction of Linear Parametric Images From Dynamic PET Using Nonlocal Deep Image Prior , 2021, IEEE Transactions on Medical Imaging.

[40]  A. Hansen,et al.  Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients , 2021, Acta oncologica.

[41]  D. Liang,et al.  Synthesizing PET/MR (T1-weighted) images from non-attenuation-corrected PET images , 2021, Physics in medicine and biology.

[42]  René M. Botnar,et al.  MRI-Guided Motion-Corrected PET Image Reconstruction for Cardiac PET/MRI , 2021, The Journal of Nuclear Medicine.

[43]  Jianhua Yan,et al.  Deep learning for whole-body medical image generation , 2021, European Journal of Nuclear Medicine and Molecular Imaging.

[44]  Ching-Ching Yang,et al.  Applying MRI Intensity Normalization on Non-Bone Tissues to Facilitate Pseudo-CT Synthesis from MRI , 2021, Diagnostics.

[45]  Seongho Seo,et al.  Data-driven respiratory phase-matched PET attenuation correction without CT , 2021, Physics in medicine and biology.

[46]  T. Grosges,et al.  Towards a Whole Body [18F] FDG Positron Emission Tomography Attenuation Correction Map Synthesizing using Deep Neural Networks , 2021, J. Comput. Sci. Technol..

[47]  David Atkinson,et al.  Imitation learning for improved 3D PET/MR attenuation correction , 2021, Medical Image Anal..

[48]  Y. Ouchi,et al.  Deep learning-based attenuation correction for brain PET with various radiotracers , 2021, Annals of Nuclear Medicine.

[49]  Wei Wu,et al.  A deep learning-based approach for direct PET attenuation correction using Wasserstein generative adversarial network , 2021 .

[50]  Quanzheng Li,et al.  MR-Based Attenuation Correction for Brain PET Using 3-D Cycle-Consistent Adversarial Network , 2021, IEEE Transactions on Radiation and Plasma Medical Sciences.

[51]  Meher R. Juttukonda,et al.  Deep learning‐based T1‐enhanced selection of linear attenuation coefficients (DL‐TESLA) for PET/MR attenuation correction in dementia neuroimaging , 2021, Magnetic resonance in medicine.

[52]  M. Modat,et al.  A multi-channel uncertainty-aware multi-resolution network for MR to CT synthesis , 2021, Applied sciences.

[53]  T. Baum,et al.  CT-like images based on T1 spoiled gradient-echo and ultra-short echo time MRI sequences for the assessment of vertebral fractures and degenerative bone changes of the spine , 2021, European Radiology.

[54]  Atallah Baydoun,et al.  Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network , 2021, IEEE Access.

[55]  Francesca De Luca,et al.  Validation of PET/MRI attenuation correction methodology in the study of brain tumours , 2020, BMC Medical Imaging.

[56]  J. Axelsson,et al.  Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images , 2020, EJNMMI Physics.

[57]  T. Deller,et al.  PET Image Quality Improvement for Simultaneous PET/MRI with a Lightweight MRI Surface Coil. , 2020, Radiology.

[58]  Keith A. Johnson,et al.  Attenuation Correction Using Deep Learning and Integrated UTE/Multi-Echo Dixon Sequence: Evaluation in Amyloid and Tau PET Imaging , 2020, European Journal of Nuclear Medicine and Molecular Imaging.

[59]  M. Subesinghe,et al.  Artificial Intelligence for the Characterization of Pulmonary Nodules, Lung Tumors and Mediastinal Nodes on PET/CT. , 2020, Seminars in nuclear medicine.

[60]  Maysam F. Abbod,et al.  Brain MR Imaging Segmentation Using Convolutional Auto Encoder Network for PET Attenuation Correction , 2020, IntelliSys.

[61]  Yong Wang,et al.  Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis , 2020, IEEE Transactions on Medical Imaging.

[62]  Liselotte Højgaard,et al.  AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size , 2020, NeuroImage.

[63]  Yang Lei,et al.  Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods. , 2020, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[64]  R. A. Heckemann,et al.  Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging , 2020, EJNMMI Physics.

[65]  T. Fuchs,et al.  Myocardial creep-induced misalignment artifacts in PET/MR myocardial perfusion imaging , 2020, European Journal of Nuclear Medicine and Molecular Imaging.

[66]  Zhanli Hu,et al.  Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks , 2020, Physics in medicine and biology.

[67]  Maysam Abbod,et al.  MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation , 2020, Journal of Digital Imaging.

[68]  K. Nikolaou,et al.  Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks , 2020, EJNMMI Research.

[69]  Habib Zaidi,et al.  Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data , 2020, Medical Image Anal..

[70]  Zhibin Huang,et al.  Generation of Pseudo-CT using High-Degree Polynomial Regression on Dual-Contrast Pelvic MRI Data , 2020, Scientific Reports.

[71]  N. Paragios,et al.  Dosimetry-driven quality measure of brain pseudo Computed Tomography generated from deep learning for MRI-only radiotherapy treatment planning. , 2020, International Journal of Radiation Oncology, Biology, Physics.

[72]  Zhaolin Chen,et al.  Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging , 2020, European Journal of Nuclear Medicine and Molecular Imaging.

[73]  J. E. Mackewn,et al.  Practical issues and limitations of brain attenuation correction on a simultaneous PET-MR scanner , 2020, EJNMMI Physics.

[74]  T. Murakami,et al.  Diagnostic performance of zero-TE lung MR imaging in FDG PET/MRI for pulmonary malignancies , 2020, European Radiology.

[75]  Keith A. Johnson,et al.  MR-Based PET Attenuation Correction using a Combined Ultrashort Echo Time/Multi-Echo Dixon Acquisition. , 2020, Medical physics.

[76]  Georg Schramm,et al.  Estimation of Crystal Timing Properties and Efficiencies for the Improvement of (Joint) Maximum-Likelihood Reconstructions in TOF-PET , 2020, IEEE Transactions on Medical Imaging.

[77]  Wei Yang,et al.  Flexible Prediction of CT Images From MRI Data Through Improved Neighborhood Anchored Regression for PET Attenuation Correction , 2020, IEEE Journal of Biomedical and Health Informatics.

[78]  Atallah Baydoun,et al.  Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[79]  H. Iida,et al.  Magnetic Resonance-Based Attenuation Correction and Scatter Correction in Neurological Positron Emission Tomography/Magnetic Resonance Imaging—Current Status With Emerging Applications , 2020, Frontiers in Physics.

[80]  Florian Wiesinger,et al.  Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation , 2020, IEEE Transactions on Radiation and Plasma Medical Sciences.

[81]  F. Wiesinger,et al.  In‐phase zero TE musculoskeletal imaging , 2020, Magnetic resonance in medicine.

[82]  Yang Lei,et al.  Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging , 2019, Physics in medicine and biology.

[83]  S. Ourselin,et al.  PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques , 2019, Medical physics.

[84]  G. Delso,et al.  ZTE MR-based attenuation correction in brain FDG-PET/MR: performance in patients with cognitive impairment , 2019, European Radiology.

[85]  Yang Lei,et al.  Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging , 2019, Physics in medicine and biology.

[86]  Z. Fayad,et al.  Hybrid PET- and MR-driven attenuation correction for enhanced 18F-NaF and 18F-FDG quantification in cardiovascular PET/MR imaging , 2019, Journal of Nuclear Cardiology.

[87]  Florian Wiesinger,et al.  Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction , 2019, PloS one.

[88]  Young T. Hong,et al.  Brain MRI Coil Attenuation Map Processing for the GE SIGNA PET/MR: Impact on PET Image Quantification and Uniformity , 2019, 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[89]  Luyao Shi,et al.  A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation using Deep Learning , 2019, MICCAI.

[90]  Brian F. Hutton,et al.  Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning , 2019, SASHIMI@MICCAI.

[91]  Pengjiang Qian,et al.  UTE-mDixon-based thorax synthetic CT generation. , 2019, Medical physics.

[92]  Wolfgang Birkfellner,et al.  Design, Implementation, and Evaluation of a Head and Neck MRI RF Array Integrated with a 511 keV Transmission Source for Attenuation Correction in PET/MR , 2019, Sensors.

[93]  Ciprian Catana,et al.  PET Image Reconstruction Using Deep Image Prior , 2019, IEEE Transactions on Medical Imaging.

[94]  Guoyan Zheng,et al.  Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[95]  Kevin H. Leung,et al.  Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC) , 2019, European Radiology.

[96]  Maike E Lindemann,et al.  Impact of improved attenuation correction on 18F-FDG PET/MR hybrid imaging of the heart , 2019, PloS one.

[97]  Jae Sung Lee,et al.  Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps , 2019, The Journal of Nuclear Medicine.

[98]  L. Marner,et al.  Deep Learning Based Attenuation Correction of PET/MRI in Pediatric Brain Tumor Patients: Evaluation in a Clinical Setting , 2019, Front. Neurosci..

[99]  Yang Lei,et al.  MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning , 2019, Physics in medicine and biology.

[100]  K. Jeon,et al.  Comparison of lung imaging using three-dimensional ultrashort echo time and zero echo time sequences: preliminary study , 2018, European Radiology.

[101]  T. Bathen,et al.  The Effect of Including Bone in Dixon-Based Attenuation Correction for 18F-Fluciclovine PET/MRI of Prostate Cancer , 2018, The Journal of Nuclear Medicine.

[102]  Richard Kijowski,et al.  A deep learning approach for 18F-FDG PET attenuation correction , 2018, EJNMMI Physics.

[103]  M. Figl,et al.  A head coil system with an integrated orbiting transmission point source mechanism for attenuation correction in PET/MRI , 2018, Physics in medicine and biology.

[104]  Francesco Sforazzini,et al.  Accurate hybrid template–based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications , 2018, BMC Medical Imaging.

[105]  Jicun Hu,et al.  Extension of the SSS PET scatter correction algorithm to include double scatter , 2018, 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC).

[106]  E. Larsson,et al.  Evaluation of zero-echo-time attenuation correction for integrated PET/MR brain imaging—comparison to head atlas and 68Ge-transmission-based attenuation correction , 2018, EJNMMI Physics.

[107]  Fernando Boada,et al.  Joint Reconstruction of Activity and Attenuation in Time-of-Flight PET: A Quantitative Analysis , 2018, The Journal of Nuclear Medicine.

[108]  Tian Liu,et al.  MRI-based pseudo CT synthesis using anatomical signature and alternating random forest with iterative refinement model , 2018, Journal of medical imaging.

[109]  Jae Sung Lee,et al.  Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning , 2018, The Journal of Nuclear Medicine.

[110]  Alexander Hammers,et al.  A dual‐tuned 13C/1H head coil for PET/MR hybrid neuroimaging: Development, attenuation correction, and first evaluation , 2018, Medical physics.

[111]  Aaron Carass,et al.  Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN , 2018, DLMIA/ML-CDS@MICCAI.

[112]  F. Wiesinger,et al.  Developing an efficient phase-matched attenuation correction method for quiescent period PET in abdominal PET/MRI , 2018, Physics in medicine and biology.

[113]  A. McMillan,et al.  Feasibility of Deep Learning–Based PET/MR Attenuation Correction in the Pelvis Using Only Diagnostic MR Images , 2018, Tomography.

[114]  Christine DeLorenzo,et al.  Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network , 2018, The Journal of Nuclear Medicine.

[115]  A. Soricelli,et al.  Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction , 2018, The Journal of Nuclear Medicine.

[116]  H. S. Rad,et al.  Capturing Bone Signal in MRI of Pelvis, as a Large FOV Region, Using TWIST Sequence and Generating a 5-Class Attenuation Map for Prostate PET/MRI Imaging , 2018, Molecular imaging.

[117]  Fang Liu,et al.  Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging , 2018, Medical physics.

[118]  M. Ay,et al.  Reconstruction/segmentation of attenuation map in TOF-PET based on mixture models , 2018, Annals of Nuclear Medicine.

[119]  Mehdi Rezaei,et al.  MR contingency supplement prior for joint estimation of activity and attenuation in non‐time‐of‐flight positron emission tomography/MR , 2018, Electronics Letters.

[120]  J. Nuyts,et al.  Regional Accuracy of ZTE-Based Attenuation Correction in Static [18F]FDG and Dynamic [18F]PE2I Brain PET/MR , 2018, Front. Phys..

[121]  I. Burger,et al.  Impact of time-of-flight PET on quantification accuracy and lesion detection in simultaneous 18F-choline PET/MRI for prostate cancer , 2018, EJNMMI Research.

[122]  Jun Yu,et al.  Statistical learning in computed tomography image estimation , 2018, Medical physics.

[123]  H. Quick,et al.  Hybrid cardiac imaging using PET/MRI: a joint position statement by the European Society of Cardiovascular Radiology (ESCR) and the European Association of Nuclear Medicine (EANM) , 2018, European Radiology.

[124]  Maike E Lindemann,et al.  Impact of improved attenuation correction featuring a bone atlas and truncation correction on PET quantification in whole-body PET/MR , 2018, European Journal of Nuclear Medicine and Molecular Imaging.

[125]  R. Nourine,et al.  New Pseudo-CT Generation Approach from Magnetic Resonance Imaging using a Local Texture Descriptor , 2018, Journal of biomedical physics & engineering.

[126]  Florian Wiesinger,et al.  Joint estimation of activity and attenuation for PET using pragmatic MR-based prior: application to clinical TOF PET/MR whole-body data for FDG and non-FDG tracers , 2018, Physics in medicine and biology.

[127]  Jürgen Scheins,et al.  PET attenuation correction for rigid MR Tx/Rx coils from 176Lu background activity , 2018, Physics in medicine and biology.

[128]  K. Bolwin,et al.  PET attenuation correction for flexible MRI surface coils in hybrid PET/MRI using a 3D depth camera , 2018, Physics in medicine and biology.

[129]  Qianjin Feng,et al.  Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors , 2018, IEEE Transactions on Medical Imaging.

[130]  Quanzheng Li,et al.  Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images , 2017, Physics in medicine and biology.

[131]  T. Beyer,et al.  Assessment of attenuation correction for myocardial PET imaging using combined PET/MRI , 2017, Journal of Nuclear Cardiology.

[132]  Habib Zaidi,et al.  MR-guided joint reconstruction of activity and attenuation in brain PET-MR , 2017, NeuroImage.

[133]  T. Allkemper,et al.  An artefact of PET attenuation correction caused by iron overload of the liver in clinical PET-MRI , 2017, European Journal of Hybrid Imaging.

[134]  Thomas A. Hope,et al.  PET/MRI: Where might it replace PET/CT? , 2017, Journal of magnetic resonance imaging : JMRI.

[135]  Andrew P. Leynes,et al.  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 , 2017, The Journal of Nuclear Medicine.

[136]  M. Soussan,et al.  Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately? , 2017, Physics in medicine and biology.

[137]  A. McMillan,et al.  Deep learning Mr imaging–based attenuation correction for PeT/Mr imaging 1 , 2017 .

[138]  Snehashis Roy,et al.  Synthesizing CT from Ultrashort Echo-Time MR Images via Convolutional Neural Networks , 2017, SASHIMI@MICCAI.

[139]  Nassir Navab,et al.  Individual refinement of attenuation correction maps for hybrid PET/MR based on multi-resolution regional learning , 2017, Comput. Medical Imaging Graph..

[140]  Jelmer M. Wolterink,et al.  Deep MR to CT Synthesis Using Unpaired Data , 2017, SASHIMI@MICCAI.

[141]  Sébastien Ourselin,et al.  On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task , 2017, IPMI.

[142]  Yannick Berker,et al.  Numerical Algorithms for Scatter-to-Attenuation Reconstruction in PET: Empirical Comparison of Convergence, Acceleration, and the Effect of Subsets , 2017, IEEE Transactions on Radiation and Plasma Medical Sciences.

[143]  Arman Rahmim,et al.  The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies , 2017, European Radiology.

[144]  S. Jan,et al.  Using 31P-MRI of hydroxyapatite for bone attenuation correction in PET-MRI: proof of concept in the rodent brain , 2017, EJNMMI Physics.

[145]  Tong Zhu,et al.  Integration of PET/MR Hybrid Imaging into Radiation Therapy Treatment. , 2017, Magnetic resonance imaging clinics of North America.

[146]  Michael E Casey,et al.  Evaluation of MLACF based calculated attenuation brain PET imaging for FDG patient studies , 2017, Physics in medicine and biology.

[147]  Xiao Han,et al.  MR‐based synthetic CT generation using a deep convolutional neural network method , 2017, Medical physics.

[148]  N. Costes,et al.  Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR , 2017, Physics in medicine and biology.

[149]  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.

[150]  Yannick Berker,et al.  MLAA-based attenuation correction of flexible hardware components in hybrid PET/MR imaging , 2017, EJNMMI Physics.

[151]  Yang Lei,et al.  Pseudo CT estimation from MRI using patch-based random forest , 2017, Medical Imaging.

[152]  Hamidreza Saligheh Rad,et al.  Single STE-MR Acquisition in MR-Based Attenuation Correction of Brain PET Imaging Employing a Fully Automated and Reproducible Level-Set Segmentation Approach , 2017, Molecular Imaging and Biology.

[153]  Su Ruan,et al.  Medical Image Synthesis with Context-Aware Generative Adversarial Networks , 2016, MICCAI.

[154]  Johan Nuyts,et al.  Optimized MLAA for quantitative non-TOF PET/MR of the brain , 2016, Physics in medicine and biology.

[155]  Yaozong Gao,et al.  Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks , 2016, LABELS/DLMIA@MICCAI.

[156]  Ninon Burgos,et al.  A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients , 2016, NeuroImage.

[157]  M. Defrise,et al.  Pitfalls in MLAA and MLACF , 2016, 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD).

[158]  B. Hutton,et al.  Joint reconstruction of activity and attenuation in dynamic PET , 2016, 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD).

[159]  Ciprian Catana,et al.  Transmission imaging for integrated PET-MR systems , 2016, Physics in medicine and biology.

[160]  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.

[161]  Habib Zaidi,et al.  One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI , 2016, European Journal of Nuclear Medicine and Molecular Imaging.

[162]  A. Lalande,et al.  A novel alternative to classify tissues from T1 and T2 relaxation times for prostate MRI , 2016, Magnetic Resonance Materials in Physics, Biology and Medicine.

[163]  Igor Yakushev,et al.  Comparison between MRI-based attenuation correction methods for brain PET in dementia patients , 2016, European Journal of Nuclear Medicine and Molecular Imaging.

[164]  S. Baba,et al.  An improved MR sequence for attenuation correction in PET/MR hybrid imaging. , 2016, Magnetic resonance imaging.

[165]  Z. Fayad,et al.  Attenuation Correction for Magnetic Resonance Coils in Combined PET/MR Imaging: A Review. , 2016, PET clinics.

[166]  J. S. Lee,et al.  MRI-Based Attenuation Correction for PET/MRI Using Multiphase Level-Set Method , 2016, The Journal of Nuclear Medicine.

[167]  Thomas Beyer,et al.  MR–Consistent Simultaneous Reconstruction of Attenuation and Activity for Non–TOF PET/MR , 2016, IEEE Transactions on Nuclear Science.

[168]  J. Johansson,et al.  Tissue Probability-Based Attenuation Correction for Brain PET/MR by Using SPM8 , 2016, IEEE Transactions on Nuclear Science.

[169]  Kris Thielemans,et al.  The effect of respiratory induced density variations on non-TOF PET quantitation in the lung , 2016, Physics in medicine and biology.

[170]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[171]  Martin Krämer,et al.  Time Efficient 3D Radial UTE Sampling with Fully Automatic Delay Compensation on a Clinical 3T MR Scanner , 2016, PloS one.

[172]  H. Zaidi,et al.  Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities. , 2016, Medical physics.

[173]  Johan Nuyts,et al.  Simultaneous reconstruction of the activity image and registration of the CT image in TOF-PET , 2016, 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC).

[174]  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.

[175]  Young Han Lee,et al.  Ultrashort echo (UTE) versus pointwise encoding time reduction with radial acquisition (PETRA) sequences at 3 Tesla for knee meniscus: A comparative study. , 2016, Magnetic resonance imaging.

[176]  Yusheng Li,et al.  Attenuation correction in emission tomography using the emission data--A review. , 2016, Medical physics.

[177]  R. Boellaard,et al.  Investigating the state-of-the-art in whole-body MR-based attenuation correction: an intra-individual, inter-system, inventory study on three clinical PET/MR systems , 2016, Magnetic Resonance Materials in Physics, Biology and Medicine.

[178]  N. Schwenzer,et al.  Comparison of Positron Emission Tomography Quantification Using Magnetic Resonance– and Computed Tomography–Based Attenuation Correction in Physiological Tissues and Lesions: A Whole-Body Positron Emission Tomography/Magnetic Resonance Study in 66 Patients , 2016, Investigative radiology.

[179]  J. L. Herraiz,et al.  Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies , 2016, The Journal of Nuclear Medicine.

[180]  J L Ackerman,et al.  Continuous MR bone density measurement using water- and fat-suppressed projection imaging (WASPI) for PET attenuation correction in PET-MR , 2015, Physics in medicine and biology.

[181]  M. Zaitsev,et al.  Motion artifacts in MRI: A complex problem with many partial solutions , 2015, Journal of magnetic resonance imaging : JMRI.

[182]  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.

[183]  Hans Herzog,et al.  Comparison of Template-Based Versus CT-Based Attenuation Correction for Hybrid MR/PET Scanners , 2015, IEEE Transactions on Nuclear Science.

[184]  Melanie Traughber,et al.  Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering. , 2015, Medical physics.

[185]  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.

[186]  N. Schwenzer,et al.  Quantitative Evaluation of Segmentation- and Atlas-Based Attenuation Correction for PET/MR on Pediatric Patients , 2015, The Journal of Nuclear Medicine.

[187]  Ninon Burgos,et al.  Multi-contrast attenuation map synthesis for PET/MR scanners: assessment on FDG and Florbetapir PET tracers , 2015, European Journal of Nuclear Medicine and Molecular Imaging.

[188]  Habib Zaidi,et al.  Emission-based estimation of lung attenuation coefficients for attenuation correction in time-of-flight PET/MR , 2015, Physics in medicine and biology.

[189]  Habib Zaidi,et al.  Clinical Assessment of Emission- and Segmentation-Based MR-Guided Attenuation Correction in Whole-Body Time-of-Flight PET/MR Imaging , 2015, The Journal of Nuclear Medicine.

[190]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[191]  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.

[192]  Meher R. Juttukonda,et al.  Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction. , 2015, Radiology.

[193]  Hamidreza Saligheh Rad,et al.  Generation of a Four-Class Attenuation Map for MRI-Based Attenuation Correction of PET Data in the Head Area Using a Novel Combination of STE/Dixon-MRI and FCM Clustering , 2015, Molecular Imaging and Biology.

[194]  Live Eikenes,et al.  PET/MR brain imaging: evaluation of clinical UTE-based attenuation correction , 2015, European Journal of Nuclear Medicine and Molecular Imaging.

[195]  Alexander Hammers,et al.  Evaluation of several multi-atlas methods for PSEUDO-CT generation in brain MRI-PET attenuation correction , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[196]  Flemming L. Andersen,et al.  Automatic correction of dental artifacts in PET/MRI , 2015, Journal of medical imaging.

[197]  Habib Zaidi,et al.  Clinical Assessment of MR-Guided 3-Class and 4-Class Attenuation Correction in PET/MR , 2015, Molecular Imaging and Biology.

[198]  Habib Zaidi,et al.  Joint Estimation of Activity and Attenuation in Whole-Body TOF PET/MRI Using Constrained Gaussian Mixture Models , 2015, IEEE Transactions on Medical Imaging.

[199]  Stefan Förster,et al.  MR-Based Attenuation Correction Using Ultrashort-Echo-Time Pulse Sequences in Dementia Patients , 2015, The Journal of Nuclear Medicine.

[200]  G. Delso,et al.  Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull , 2015, The Journal of Nuclear Medicine.

[201]  D Forsberg,et al.  Generating patient specific pseudo-CT of the head from MR using atlas-based regression , 2015, Physics in medicine and biology.

[202]  F. Prato,et al.  Feasibility of simultaneous whole-brain imaging on an integrated PET-MRI system using an enhanced 2-point Dixon attenuation correction method , 2015, Front. Neurosci..

[203]  G. Delso,et al.  Hybrid PET/MR Imaging: An Algorithm to Reduce Metal Artifacts from Dental Implants in Dixon-Based Attenuation Map Generation Using a Multiacquisition Variable-Resonance Image Combination Sequence , 2015, The Journal of Nuclear Medicine.

[204]  Mert R. Sabuncu,et al.  Multi-atlas segmentation of biomedical images: A survey , 2014, Medical Image Anal..

[205]  Jerry L Prince,et al.  PET Attenuation Correction Using Synthetic CT from Ultrashort Echo-Time MR Imaging , 2014, The Journal of Nuclear Medicine.

[206]  Kevin T. Chen,et al.  An SPM8-Based Approach for Attenuation Correction Combining Segmentation and Nonrigid Template Formation: Application to Simultaneous PET/MR Brain Imaging , 2014, The Journal of Nuclear Medicine.

[207]  Habib Zaidi,et al.  MRI-based pseudo-CT generation using sorted atlas images in whole-body PET/MRI , 2014, 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[208]  S. Pistorius,et al.  Feasibility of scatter based electron density reconstruction for attenuation correction in positron emission tomography , 2014, 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[209]  Stefaan Vandenberghe,et al.  Comparison of transmission- and emission-based attenuation correction for TOF-PET/MRI , 2014, 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[210]  Jae Sung Lee,et al.  Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of 18F-FP-CIT PET/MR for Patients with Parkinsonism , 2014, The Journal of Nuclear Medicine.

[211]  Melanie Traughber,et al.  k-space sampling optimization for ultrashort TE imaging of cortical bone: applications in radiation therapy planning and MR-based PET attenuation correction. , 2014, Medical physics.

[212]  Adam Johansson,et al.  CT substitutes derived from MR images reconstructed with parallel imaging. , 2014, Medical physics.

[213]  Ninon Burgos,et al.  Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies , 2014, IEEE Transactions on Medical Imaging.

[214]  Harald H Quick,et al.  Towards integration of PET/MR hybrid imaging into radiation therapy treatment planning. , 2014, Medical physics.

[215]  F Hofheinz,et al.  Evaluation and automatic correction of metal-implant-induced artifacts in MR-based attenuation correction in whole-body PET/MR imaging , 2014, Physics in medicine and biology.

[216]  Yi Su,et al.  Attenuation Effects of MR Headphones During Brain PET/MR Studies , 2014, The Journal of Nuclear Medicine Technology.

[217]  Gaspar Delso,et al.  Anatomic Evaluation of 3-Dimensional Ultrashort-Echo-Time Bone Maps for PET/MR Attenuation Correction , 2014, The Journal of Nuclear Medicine.

[218]  Johan Nuyts,et al.  ML-reconstruction for TOF-PET with simultaneous estimation of the attenuation factors , 2014, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[219]  David Izquierdo-Garcia,et al.  Comparison of MR-based attenuation correction and CT-based attenuation correction of whole-body PET/MR imaging , 2014, European Journal of Nuclear Medicine and Molecular Imaging.

[220]  Kevin T. Chen,et al.  Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners. , 2014, American journal of nuclear medicine and molecular imaging.

[221]  M. Defrise,et al.  Transmission-less attenuation correction in time-of-flight PET: analysis of a discrete iterative algorithm , 2014, Physics in medicine and biology.

[222]  Philip M Robson,et al.  Attenuation Correction for Flexible Magnetic Resonance Coils in Combined Magnetic Resonance/Positron Emission Tomography Imaging , 2014, Investigative radiology.

[223]  H. Quick,et al.  Field of view extension and truncation correction for MR-based human attenuation correction in simultaneous MR/PET imaging. , 2014, Medical physics.

[224]  Z. Fayad,et al.  Improvement of Attenuation Correction in Time-of-Flight PET/MR Imaging with a Positron-Emitting Source , 2014, The Journal of Nuclear Medicine.

[225]  D. Loeffelbein,et al.  Performance of Whole-Body Integrated 18F-FDG PET/MR in Comparison to PET/CT for Evaluation of Malignant Bone Lesions , 2014, The Journal of Nuclear Medicine.

[226]  R. Boellaard,et al.  Accurate PET/MR Quantification Using Time of Flight MLAA Image Reconstruction , 2014, Molecular Imaging and Biology.

[227]  Anna Barnes,et al.  A comparison of CT- and MR-based attenuation correction in neurological PET , 2014, European Journal of Nuclear Medicine and Molecular Imaging.

[228]  E. R. Kops,et al.  Hybrid approach for attenuation correction in PET/MR scanners , 2014 .

[229]  Eiji Yoshida,et al.  A proposal for PET/MRI attenuation correction with μ-values measured using a fixed-position radiation source and MRI segmentation , 2014 .

[230]  Ian Law,et al.  Combined PET/MR imaging in neurology: MR-based attenuation correction implies a strong spatial bias when ignoring bone , 2014, NeuroImage.

[231]  Stuart Crozier,et al.  Automated Classification of Bone and Air Volumes for Hybrid PET-MRI Brain Imaging , 2013, 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[232]  C Prieto,et al.  Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring. , 2013, Medical physics.

[233]  Mary Feng,et al.  Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy , 2013, Physics in medicine and biology.

[234]  Kevin M. Johnson,et al.  Optimized 3D ultrashort echo time pulmonary MRI , 2013, Magnetic resonance in medicine.

[235]  H H Quick,et al.  Towards improved hardware component attenuation correction in PET/MR hybrid imaging , 2013, Physics in medicine and biology.

[236]  Vladimir Panin,et al.  LSO background radiation as a transmission source using time of flight , 2013, 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC).

[237]  N. Alpert,et al.  Bias Atlases for Segmentation-Based PET Attenuation Correction Using PET-CT and MR , 2013, IEEE Transactions on Nuclear Science.

[238]  K. Scheffler,et al.  MR‐based field‐of‐view extension in MR/PET: B0 homogenization using gradient enhancement (HUGE) , 2013, Magnetic resonance in medicine.

[239]  Ninon Burgos,et al.  Attenuation Correction Synthesis for Hybrid PET-MR Scanners , 2013, MICCAI.

[240]  J. Théberge,et al.  Description and assessment of a registration-based approach to include bones for attenuation correction of whole-body PET/MRI. , 2013, Medical physics.

[241]  H. Quick,et al.  Integrated PET/MR imaging: automatic attenuation correction of flexible RF coils. , 2013, Medical physics.

[242]  John W. Clark,et al.  Investigating the use of nonattenuation corrected PET images for the attenuation correction of PET data. , 2013, Medical physics.

[243]  H. Quick,et al.  Magnetic Resonance–Based Attenuation Correction for PET/MR Hybrid Imaging Using Continuous Valued Attenuation Maps , 2013, Investigative radiology.

[244]  Hans Herzog,et al.  Skull segmentation of UTE MR images by probabilistic neural network for attenuation correction in PET/MR , 2013 .

[245]  A. Ahmadian,et al.  MRI-guided attenuation correction in whole-body PET/MR: assessment of the effect of bone attenuation , 2013, Annals of Nuclear Medicine.

[246]  Adam Johansson,et al.  Evaluation of an attenuation correction method for PET/MR imaging of the head based on substitute CT images , 2013, Magnetic Resonance Materials in Physics, Biology and Medicine.

[247]  Thomas Beyer,et al.  PET/MR imaging of the pelvis in the presence of endoprostheses: reducing image artifacts and increasing accuracy through inpainting , 2013, European Journal of Nuclear Medicine and Molecular Imaging.

[248]  Gudrun Wagenknecht,et al.  MRI for attenuation correction in PET: methods and challenges , 2012, Magnetic Resonance Materials in Physics, Biology and Medicine.

[249]  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.

[250]  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).

[251]  S. D. Wollenweber,et al.  Estimation of mean lung attenuation for use in generating PET attenuation maps , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[252]  Yannick Berker,et al.  Lung attenuation coefficient estimation using Maximum Likelihood reconstruction of attenuation and activity for PET/MR attenuation correction , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[253]  Maurizio Conti,et al.  Simultaneous Reconstruction of Activity and Attenuation in Time-of-Flight PET , 2012, IEEE Transactions on Medical Imaging.

[254]  Ciprian Catana,et al.  MRI-Based Nonrigid Motion Correction in Simultaneous PET/MRI , 2012, The Journal of Nuclear Medicine.

[255]  Harald H Quick,et al.  Simultaneous PET/MR imaging: MR-based attenuation correction of local radiofrequency surface coils. , 2012, Medical physics.

[256]  T Chang,et al.  SU-E-I-84: A Novel Approach for the Attenuation Correction of PET Data in PET/MR Systems. , 2012, Medical physics.

[257]  C. Kuhl,et al.  MRI-Based Attenuation Correction for Hybrid PET/MRI Systems: A 4-Class Tissue Segmentation Technique Using a Combined Ultrashort-Echo-Time/Dixon MRI Sequence , 2012, The Journal of Nuclear Medicine.

[258]  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.

[259]  M. Defrise,et al.  Time-of-flight PET data determine the attenuation sinogram up to a constant , 2012, Physics in medicine and biology.

[260]  P. Jakob,et al.  Ultrashort echo time imaging using pointwise encoding time reduction with radial acquisition (PETRA) , 2012, Magnetic resonance in medicine.

[261]  Brian M Dale,et al.  Free-breathing 3D T1-weighted gradient-echo sequence with radial data sampling in abdominal MRI: preliminary observations. , 2011, AJR. American journal of roentgenology.

[262]  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.

[263]  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.

[264]  Adam Johansson,et al.  CT substitute derived from MRI sequences with ultrashort echo time. , 2011, Medical physics.

[265]  Til Aach,et al.  Simultaneous Reconstruction of Activity and Attenuation for PET/MR , 2011, IEEE Transactions on Medical Imaging.

[266]  Bernhard Schölkopf,et al.  The effect of patient positioning aids on PET quantification in PET/MR imaging , 2011, European Journal of Nuclear Medicine and Molecular Imaging.

[267]  H. Eggers,et al.  Dual‐echo Dixon imaging with flexible choice of echo times , 2011, Magnetic resonance in medicine.

[268]  Ciprian Catana,et al.  Toward Implementing an MRI-Based PET Attenuation-Correction Method for Neurologic Studies on the MR-PET Brain Prototype , 2010, The Journal of Nuclear Medicine.

[269]  G. Delso,et al.  Evaluation of the attenuation properties of MR equipment for its use in a whole-body PET/MR scanner , 2010, Physics in medicine and biology.

[270]  S. Vandenberghe,et al.  MRI-Based Attenuation Correction for PET/MRI Using Ultrashort Echo Time Sequences , 2010, Journal of Nuclear Medicine.

[271]  Eduard Schreibmann,et al.  MR-based attenuation correction for hybrid PET-MR brain imaging systems using deformable image registration. , 2010, Medical physics.

[272]  Jun Zhang,et al.  Three-region MRI-based whole-body attenuation correction for automated PET reconstruction. , 2010, Nuclear medicine and biology.

[273]  V. Schulz,et al.  MR-based attenuation correction for a whole-body sequential PET/MR system , 2009, 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC).

[274]  James Hamill,et al.  Comparison of low-pitch and respiratory-averaged CT protocols for attenuation correction of cardiac PET studies. , 2009, Medical physics.

[275]  Nassir Navab,et al.  Tissue Classification as a Potential Approach for Attenuation Correction in Whole-Body PET/MRI: Evaluation with PET/CT Data , 2009, Journal of Nuclear Medicine.

[276]  B. Schölkopf,et al.  Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques , 2009, European Journal of Nuclear Medicine and Molecular Imaging.

[277]  Xiaofeng Yang,et al.  An MRI-based attenuation correction method for combined PET/MRI applications , 2009, Medical Imaging.

[278]  M. Brady,et al.  MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration , 2008, Journal of Nuclear Medicine.

[279]  Christoph Palm,et al.  MR-based attenuation correction for torso-PET/MR imaging: pitfalls in mapping MR to CT data , 2008, European Journal of Nuclear Medicine and Molecular Imaging.

[280]  H. Herzog,et al.  Alternative methods for attenuation correction for PET images in MR-PET scanners , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[281]  Elkan F Halpern,et al.  Optimal CT breathing protocol for combined thoracic PET/CT. , 2006, AJR. American journal of roentgenology.

[282]  D. Townsend,et al.  Method for transforming CT images for attenuation correction in PET/CT imaging. , 2006, Medical physics.

[283]  Peter Hunold,et al.  Parallel acquisition techniques for accelerated volumetric interpolated breath‐hold examination magnetic resonance imaging of the upper abdomen: Assessment of image quality and lesion conspicuity , 2005, Journal of magnetic resonance imaging : JMRI.

[284]  Habib Zaidi,et al.  Atlas-guided non-uniform attenuation correction in cerebral 3D PET imaging , 2005, NeuroImage.

[285]  Mark Bydder,et al.  Magnetic Resonance: An Introduction to Ultrashort TE (UTE) Imaging , 2003, Journal of computer assisted tomography.

[286]  M Wilke,et al.  Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data , 2003, Magnetic resonance in medicine.

[287]  H. Zaidi,et al.  Magnetic resonance imaging-guided attenuation and scatter corrections in three-dimensional brain positron emission tomography. , 2003, Medical physics.

[288]  G Glatting,et al.  Simultaneous iterative reconstruction of emission and attenuation images in positron emission tomography from emission data only. , 2002, Medical physics.

[289]  G Glatting,et al.  Simultaneous iterative reconstruction for emission and attenuation images in positron emission tomography. , 2000, Medical physics.

[290]  A. V. Bronnikov,et al.  Reconstruction of attenuation map using discrete consistency conditions , 2000, IEEE Transactions on Medical Imaging.

[291]  Mark T. Madsen,et al.  Emission based attenuation correction of PET images of the thorax , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[292]  Jeffrey A. Fessler,et al.  Joint estimation of attenuation and emission images from PET scans , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[293]  N. Rofsky,et al.  Abdominal MR imaging with a volumetric interpolated breath-hold examination. , 1999, Radiology.

[294]  Patrick Dupont,et al.  Simultaneous maximum a posteriori reconstruction of attenuation and activity distributions from emission sinograms , 1999, IEEE Transactions on Medical Imaging.

[295]  Rolf Clackdoyle,et al.  Attenuation correction in PET using consistency information , 1998 .

[296]  D. Madio,et al.  Ultra‐fast imaging using low flip angles and fids , 1995, Magnetic resonance in medicine.

[297]  Frank Natterer,et al.  Determination of tissue attenuation in emission tomography of optically dense media , 1993 .

[298]  S. Siegel,et al.  Implementation and evaluation of a calculated attenuation correction for PET , 1991, Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference.

[299]  Alan C. Evans,et al.  Anatomical-Functional Correlation Using an Adjustable MRI-Based Region of Interest Atlas with Positron Emission Tomography , 1988, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[300]  W. T. Dixon Simple proton spectroscopic imaging. , 1984, Radiology.

[301]  E. Rota Kops,et al.  PET attenuation correction for rigid MR Tx/Rx coils from 176Lu background activity. , 2018, Physics in medicine and biology.

[302]  Tetsuro Sekine,et al.  Effect of Time-of-Flight Information on PET/MR Reconstruction Artifacts: Comparison of Free-breathing versus Breath-hold MR-based Attenuation Correction. , 2017, Radiology.

[303]  Yaozong Gao,et al.  Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model , 2016, IEEE Transactions on Medical Imaging.

[304]  Kevin M. Johnson,et al.  Detection of Small Pulmonary Nodules with Ultrashort Echo Time Sequences in Oncology Patients by Using a PET/MR System. , 2016, Radiology.

[305]  Meher R. Juttukonda,et al.  A multi-centre evaluation of eleven clinically feasible brain PET / MRI attenuation correction techniques using a large cohort of patients , 2016 .

[306]  B. Fei,et al.  Research and applications: Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET , 2013, J. Am. Medical Informatics Assoc..

[307]  Quanzheng Li,et al.  Magnetic resonance-based motion correction for positron emission tomography imaging. , 2013, Seminars in nuclear medicine.

[308]  R. Günther,et al.  Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data , 2010, European Journal of Nuclear Medicine and Molecular Imaging.

[309]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[310]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[311]  T. Greitz,et al.  Applications of a computerized adjustable brain atlas in positron emission tomography. , 1986, Acta radiologica. Supplementum.