浏览全部资源
扫码关注微信
Received：27 February 2022，
Revised：2022-3-3，
Accepted：10 March 2022，
Published：16 June 2022
移动端阅览
现代医学成像技术是脑科学研究和脑疾病诊断的利器，不同模态的成像技术提供不同的信息可协同表征脑部结构和功能。其中定量成像技术着眼于和生理、物理相关的内在参量，旨在提供更精准的信息。本文以正电子发射扫描成像(positron emission tomography，PET)和磁共振成像(magnetic resonance imaging，MRI)两种生物医学成像模态为例
针对性地讨论它们在定量刻画大脑微观结构和功能领域的发展状况，目前尚存的关键技术问题和未来的可能发展方向。围绕定量MRI，从表观参数定量开始，介绍其中的单参数定量的现状和不足，以及目前多参数同时定量的发展动态；围绕微观参数定量，介绍针对髓鞘成像的两大方法，包括多组分T2定量和基于超短回波时间髓鞘直接成像，介绍磁共振定量成像特别是磁共振扩散成像的可比较性和可重复性研究。围绕定量PET，从最广泛的代谢动力学模型——房室模型开始介绍，对生理参数与示踪剂摄取量的关系进行了详细描述，展开到定量的误差来源包括模型选择、图像质量以及输入函数测量误差3个方面进行分析，介绍最新进展包括硬件设备、图像重建方法以及定量分析方法。最后对MRI定量、PET定量以及PET/MRI定量领域进行了展望。
Advanced medical imaging technology facilitates human brain recognition and its disease diagnosis research like positron emission tomography (PET) and magnetic resonance imaging (MRI). The changes of structure
function
metabolism
and signaling pathways yield richer multimodal image data for disease diagnosis research. Traditional clinical imaging techniques are mostly based on qualitative interpretation. The signal intensity of acquired images are differentiated for normal tissues
resulting in uncertainty in image contrast due to the microscopic scaled structure and function changes in tissue disease pathology. It is an effective way to obtain accurate and reliable detection of lesion features while the tissue contrast changes intensively higher than the noise level. In comparison to qualitative medical imaging
the current measurement focuses on physiology and physics related parameters to generate its quantitative parameter map. Quantitative parameters have their own physical units in common and their quantitative values reflect the physiological and physical information of the object mathematically. Quantitative measurement of tissue is essential to physiopathological modeling. The relationship between image nuances and pathology
realize in-depth clinical data mining for accurate diagnosis based on the integration of effective model analysis. The quantitative integration of cross-modalities and multiple imaging mechanisms medical imaging has been developed in brain tumors and neuropsychiatric diseases. While quantitative imaging technology is challenging in clinical settings
no matter due to its long acquisition time or its different image presentation. The common quantitative PET and MRI measurements are based on data fitting from multiple measurement. The multiple measurements are time consuming and costly. The modeling and simulation of micro-physiological systems still need to be continuously developed and improved
including the development from static models to dynamic models. Our research review and discuss the key technical issues and development of existing quantitative imaging technologies for human brain microstructure and physiological function indicators detection through PET and MRI methods. The clinical applications and future directions are introduced as well. Specifically
we focus on the establishment of quantitative models
the measurement of quantitative parameters and imaging methods
the influencing factors in the measurement
and the clinical application of related technologies. First
the review of quantitative MRI is based on the current situation and deficiencies of single-parameter quantification and the development trendency of simultaneous multi-parameter quantification. Then
it introduces two methods of myelin imaging based on the quantification of microscopic parameters
including multicomponent T2 quantification and ultrashort echo based myelin imaging. An introduction to the comparability and reproducibility of magnetic resonance quantitative imaging is followed on
especially magnetic resonance diffusion imaging. Second
the review of quantitative PET is based on the most extensive metabolic kinetic model-the compartment model. To extend quantitative error sources like model option
image quality
and input functions
the relationship between physiological parameters and tracer uptake is clarified and three aspects of measurement error are analyzed in detail. The latest development is reviewed based on hardware equipment
image reconstruction methods and quantitative analysis methods. The future MRI quantification
PET quantification and PET/MRI quantification are briefly predicted further.
Alexander D C, Zikic D, Ghosh A, Tanno R, Wottschel V, Zhang J Y, Kaden E, Dyrby T B, Sotiropoulos S N, Zhang H and Criminisi A. 2017. Image quality transfer and applications in diffusion MRI. Neuroimage, 152: 283-298[DOI: 10.1016/j.neuroimage.2017.02.089]
Badawi R D, Shi H C, Hu P C, Chen S G, Xu T Y, Price P M, Ding Y, Spencer B A, Nardo L, Liu W P, Bao J, Jones T, Li H D and Cherry S R. 2019. First human imaging studies with the EXPLORER total-body PET scanner. The Journal of Nuclear Medicine, 60(3): 299-303[DOI: 10.2967/jnumed.119.226498]
Baranovicova E, Mlynarik V, Kantorova E, Hnilicova P and Dobrota D. 2016. Quantitative evaluation of cerebral white matter in patients with multiple sclerosis using multicomponent T2 mapping. Neurological Research, 38(5): 389-396[DOI: 10.1080/01616412.2016.1165450]
Bentourkia M and Zaidi H. 2007. Tracer kinetic modeling in PET. PET Clinics, 2(2): 267-277[DOI: 10.1016/J.CPET.2007.08.003]
Bernstein M A, King K F and Zhou X J. 2004. Handbook of MRF Pulse Sequences. Boston: Academic Press
Björk M, Zachariah D, Kullberg J and Stoica P. 2016. A multicomponent T 2 relaxometry algorithm for myelin water imaging of the brain. Magnetic Resonance in Medicine, 75(1): 390-402[DOI: 10.1002/mrm.25583 ]
Buchert R, Dirks M, Schütze C, Wilke F, Mamach M, Wirries A K, Pflugrad H, Hamann L, Langer L B N, Wetzel C, Lukacevic M, Polyak A, Kessler M, Petrusch C, Bengel F M, Geworski L, Rupprecht R, Weissenborn K, Ross T L and Berding G. 2020. Reliable quantification of 18 F-GE-180 PET neuroinflammation studies using an individually scaled population-based input function or late tissue-to-blood ratio. European Journal of Nuclear Medicine and Molecular Imaging, 47(12): 2887-2900[DOI: 10.1007/s00259-020-04810-1 ]
Cao X Z, Liao C Y, Iyer S S, Wang Z X, Zhou Z H, Dai E P, Liberman G, Dong Z J, Gong T, He H J, Zhong J H, Bilgic B and Setsompop K. 2022. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotrop ic-resolution quantitative imaging. Magnetic Resonance in Medicine: #29194[ DOI: 10.1002/mrm.29194 http://dx.doi.org/10.1002/mrm.29194 ]
Cao X Z, Ye H H, Liao C Y, Li Q, He H J and Zhong J H. 2019. Fast 3D brain MR fingerprinting based on multi-axis spiral projection trajectory. Magnetic Resonance in Medicine, 82(1): 289-301[DOI: 10.1002/mrm.27726]
Carson R E. 2005. Tracer kinetic modeling in PET//Bailey D L, Townsend D W, Valk P E and Maisey M N, eds. Positron Emission Tomography. London: Springer: 127-159[ DOI: 10.1007/1-84628-007-9_6 http://dx.doi.org/10.1007/1-84628-007-9_6 ]
Catana C, Benner T, Van Der Kouwe A, Byars L, Hamm M, Chonde D B, Michel C J, El Fakhri G, Schmand M and Sorensen A G. 2011. MRI-assisted PET motion correction forneurologic studies in an integrated MR-PET scanner. The Journal of Nuclear Medicine, 52(1): 154-161[DOI: 10.2967/jnumed.110.079343]
Ceccarini J, Liu H, Van Laere K, Morris E D and Sander C Y. 2020. Methods for quantifying neurotransmitter dynamics in the living brain with PET imaging. Frontiers in Physiology, 11: #792[DOI: 10.3389/fphys.2020.00792]
Chen K, Huang S C and Yu D C. 1991. The effects of measurement errors in the plasma radioactivity curve on parameter estimation in positron emission tomography. Physics in Medicine and Biology, 36(9): 1183-1200[DOI: 10.1088/0031-9155/36/9/003]
Cherry S R, Jones T, Karp J S, Qi J Y, Moses W W and Badawi R D. 2018. Total-body PET: maximizing sensitivity to create new opportunities for clinical research and patient care. The Journal of Nuclear Medicine, 59(1): 3-12[DOI: 10.2967/jnumed.116.184028]
Choi J Y, Hart T, Whyte J, Rabinowitz A R, Oh S H, Lee J and Kim J J. 2019. Myelin water imaging of moderate to severe diffuse traumatic brain injury. NeuroImage: Clinical, 22: #101785[DOI: 10.1016/j.nicl.2019.101785]
Christen T, Pannetier N A, Ni W W, Qiu D, Moseley M E, Schuff N and Zaharchuk G. 2014. MR vascular fingerprinting: a new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain. NeuroImage, 89: 262-270[DOI: 10.1016/j.neuroimage.2013.11.052]
Cloos M A, Knoll F, Zhao T J, Block K T, Bruno M, Wiggins G C and Sodickson D K. 2016. Multiparametric imaging with heterogeneous radiofrequency fields. Nature Communications, 7(1): #12445[DOI: 10.1038/ncomms12445]
Cohen O, Huang S N, McMahon M T, Rosen M S and Farrar C T. 2018. Rapid and quantitative chemical exchange saturation transfer (CEST) imaging with magnetic resonance fingerprinting (MRF). Magnetic Resonance in Medicine, 80(6): 2449-2463[DOI: 10.1002/mrm.27221]
Cruz G, Jaubert O, Qi H K, Bustin A, Milotta G, Schneider T, Koken P, Doneva M, Botnar R M and Prieto C. 2020.3D free-breathing cardiac magnetic resonance fingerprinting. NMR in Biomedicine, 33(10): #4370[DOI: 10.1002/nbm.4370]
Cui J N, Gong K, Guo N, Wu C X, Meng X X, Kim K, Zheng K, Wu Z F, Fu L P, Xu B X, Zhu Z H, Tian J H, Liu H F and Li Q Z. 2019. PET image denoising using unsupervised deep learning. European Journal of Nuclear Medicine and Molecular Imaging, 46(13): 2780-2789[DOI: 10.1007/s00259-019-04468-4]
Da-Ano R, Lucia F, Masson I, Abgral R, Alfieri J, Rousseau C, Mervoyer A, Reinhold C, Pradier O, Schick U, Visvikis D and Hatt M. 2021. A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets. PLoS One, 16(7): #0253653[DOI: 10.1371/journal.pone.0253653]
Deoni S C L, Rutt B K and Peters T M. 2003. Rapid combined T 1 and T 2 mapping using gradient recalled acquisition in the steady state. Magnetic Resonance in Medicine, 49(3): 515-526[DOI: 10.1002/mrm.10407 ] .
Erlandsson K, Buvat I, Pretorius P H, Thomas B A and Hutton B F. 2012. A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Physics in Medicine and Biology, 57(21): R119-R159[DOI: 10.1088/0031-9155/57/21/R119]
Faizy T D, Thaler C, Broocks G, Flottmann F, Leischner H, Kniep H, Nawabi J, Schön G, Stellmann J P, Kemmling A, Reddy R, Heit J J, Fiehler J, Kumar D and Hanning U. 2020. The myelin water fraction serves as a marker for age-relatedmyelin alterations in the cerebral white matter——a multiparametric MRI aging study. Frontiers in Neuroscience, 14: #136[DOI: 10.3389/fnins.2020.00136]
Feng D D, Chen K W and Wen L F. 2020. Noninvasive input function acquisition and simultaneous estimations with physiological parameters for PET quantification: a brief review. IEEE Transactions on Radiation and Plasma Medical Sciences, 4(6): 676-683[DOI: 10.1109/trpms.2020.3010844]
Feng D G, Huang S C and Wang X M. 1993. Models for computer simulation studies of input functions for tracer kinetic modeling with positron emission tomography. International Journal of Bio-Medical Computing, 32(2): 95-110[DOI: 10.1016/0020-7101(93)90049-C]
Fortin J P, Cullen N, Sheline Y I, Taylor W D, Aselcioglu I, Cook P A, Adams P, Cooper C, Fava M, McGrath P J, McInnis M, Phillips M L, Trivedi M H, Weissman M M and Shinohara R T. 2018. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage, 167: 104-120[DOI: 10.1016/j.neuroimage.2017.11.024]
Fortin J P, Parker D, Tunç B, Watanabe T, Elliott M A, Ruparel K, Roalf D R, Satterthwaite T D, Gur R C, Gur R E, Schultz R T, Verma R and Shinohara R T. 2017. Harmonization of multi-site diffusion tensor imaging data. NeuroImage, 161: 149-170[DOI: 10.1016/j.neuroimage.2017.08.047]
Gallezot J D, Lu Y H, Naganawa M and Carson R E. 2020. Parametric Imaging with PET and SPECT. IEEE Transactions on Radiation and Plasma Medical Sciences, 4(1): 1-23[DOI: 10.1109/TRPMS.2019.2908633]
Glatard T, Lewis L B, Da Silva R F, Adalat R, Beck N, Lepage C, Rioux P, Rousseau M E, Sherif T, Deelman E, Khalili-Mahani N and Evans A C. 2015. Reproducibility of neuroimaging analyses across operating systems. Frontiers in Neuroinformatics, 9: #12[DOI: 10.3389/fninf.2015.00012]
Gunn R N, Slifstein M, Searle G E and Price J C. 2015. Quantitative imaging of protein targets in the human brain with PET. Physics in Medicine and Biology, 60(22): 363-411[DOI: 10.1088/0031-9155/60/22/R363]
Homer J and Beevers M S. 1985. Driven-equilibrium single-pulse observation of T 1 relaxation. A reevaluation of a rapid "new" methodfor determining NMR spin-lattice relaxation times. Journal of Magnetic Resonance, 63(2): 287-297[DOI: 10.1016/0022-2364(85)90318-X ] .
Hu P C, Lin X, Zhuo W H, Tan H, Xie T W, Liu G B, Chen S G, Chen X, Yu H J, Zhang Y Q, Shi H C and Liu H K. 2021. Internal dosimetry in F-18 FDG PET examinations based on long-time-measured organ activities using total-body PET/CT: does it make any difference from a short-time measurement? EJNMMI Physics, 8(1): #51[DOI: 10.1186/s40658-021-00395-2]
Hume S P, Myers R, Bloomfield P M, Opacka-Juffry J, Cremer J E, Ahier R G, Luthra S K, Brooks D J and Lammertsma A A. 1992. Quantitation of Carbon-11-labeled raclopride in rat striatum using positron emission tomography. Synapse, 12(1): 47-54[DOI: 10.1002/syn.890120106]
Ikonomovic M D, Klunk W E, Abrahamson E E, Mathis C A, Price J C, Tsopelas N D, Lopresti B J, Ziolko S, Bi W Z, Paljug W R, Debnath M L, Hope C E, Isanski B A, Hamilton R L and Dekosky S T. 2008. Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer's disease. Brain, 131(6): 1630-1645[DOI: 10.1093/brain/awn016]
Kuttner S, Wickstrøm K K, Kalda G, Esmaeil Dorraji S, Martin-Armas M, Oteiza A, J enssen R, Fenton K, Sundset R and Axelsson J. 2020. Machine learning derived input-function in a dynamic 18 F-FDG PET study of mice. Biomedical Physics and Engineering Express, 6(1): #015020[DOI:10.1088/2057-1976/ab6496 ]
Lammertsma A A and Hume S P. 1996. Simplified reference tissue model for PET receptor studies. NeuroImage, 4(3): 153-158[DOI: 10.1006/NIMG.1996.0066]
Lecoq P. 2017. Pushing the limits in time-of-flight PET imaging. IEEE Transactions on Radiation and Plasma Medical Sciences, 1(6): 473-485[DOI: 10.1109/TRPMS.2017.2756674]
Li Q, Cao X, Ye H, Zhou Z, He H and Zhong J. 2020.3D UTE-MRF for multiple parameteric maps with sub-millimeter isotropic resolution using multi-dimensional golden-angle radial trajectory//Int Soc Magn Reson Med
Li Q, Cao X Z, Ye H H, Liao C Y, He H J and Zhong J H. 2019. Ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) for simultaneous quantification of long and ultrashort T 2 tissues. Magnetic Resonance in Medicine, 82(4): 1359-1372[DOI: 10.1002/mrm.27812 ] .
Liao C Y, Bilgic B, Manhard M K, Zhao B, Cao X Z, Zhong J H, Wald L L and Setsompop K. 2017.3D MR fingerprinting with accelerated stack-of-spirals and hybrid sliding-window and GRAPPA reconstruction. NeuroImage, 162: 13-22[DOI: 10.1016/j.neuroimage.2017.08.030]
Liao C Y, Wang K, Cao X Z, Li Y P, Wu D C, Ye H H, Ding Q P, He H J and Zhong J H. 2018. Detection of lesions in mesial temporal lobe epilepsy by using MR fingerprinting. Radiology, 288(3): 804-812[DOI: 10.1148/radiol.2018172131]
Liu G B, Hu P C, Yu H J, Tan H, Zhang Y Q, Yin H Y, Hu Y, Gu J Y and Shi H C. 2021a. Ultra-low-activity total-body dynamic PET imaging allows equal performance to full-activity PET imaging for investigating kinetic metrics of 18 F-FDG in healthy volunteers. European Journal of Nuclear Medicine and Molecular Imaging, 48(8): 2373-2383[DOI: 10.1007/s00259-020-05173-3 ]
Liu G B, Yu H J, Shi D, Hu P C, Hu Y, Tan H, Zhang Y Q, Yin H Y and Shi H C. 2021b. Short-time total-body dynamic PET imaging performance in quantifying the kinetic metrics of 18 F-FDG in healthy volunteers. European Journal of Nuclear Medicine and Molecular Imaging[ DOI: 10.1007/s00259-021-05500-2 http://dx.doi.org/10.1007/s00259-021-05500-2 ] .
Look D C and Locker D R. 1970. Time saving in measurement of NMR and EPR relaxation times. Review of Scientific Instruments, 41(2): 250-251[DOI: 10.1063/1.1684482]
Ma D, Gulani V, Seiberlich N, Liu K C, Sunshine J L, Duerk J L and Griswold M A. 2013. Magnetic resonance fingerprinting. Nature, 495(7440): 187-192[DOI: 10.1038/nature11971]
Ma S, Wang N, Fan Z Y, Kaisey M, Sicotte N L, Christodoulou A G and Li D B. 2021. Three-dimensional whole-brain simultaneous T1, T2, and T1 ρ quantification using MR Multitasking: Method and initial clinical experience in tissue characterization of multiple sclerosis. Magnetic Resonance in Medicine, 85(4): 1938-1952[DOI: 10.1002/mrm.28553 ]
Ma Y J, Jang H, Wei Z, Cai Z Y, Xue Y P, Lee R R, Chang E Y, Bydder G M, Corey-Bloom J and Du J. 2020a. Myelin imaging in human brain using a short repetition time adiabatic inversion recovery prepared ultrashort echo time (STAIR-UTE) MRI sequence in multiple sclerosis. Radiology, 297(2): 392-404[DOI: 10.1148/RADIOL.2020200425]
Ma Y J, Searleman A C, Jang H, Wong J, Chang E Y, Corey-Bloom J, Bydder G M and Du J. 2020b. Whole-brain myelin imaging using 3D double-echo sliding inversion recovery ultrashort echo time (DESIRE UTE) MRI. Radiology, 294(2): 362-374.
Maikusa N, Zhu Y H, Uematsu A, Yamashita A, Saotome K, Okada N, Kasai K, Okanoya K, Yamashita O, Tanaka S C and Koike S. 2021. Comparison of traveling-subject and ComBat harmonization methods forassessing structural brain characteristics. Human Brain Mapping, 42(16): 5278-5287[DOI: 10.1002/hbm.25615]
Meikle S R, Sossi V, Roncali E, Cherry S R, Banati R, Mankoff D, Jones T, James M, Sutcliffe J, Ouyang J S, Petibon Y, Ma C, El Fakhri G, Surti S, Karp J S, Badawi R D, Yamaya T, Akamatsu G, Schramm G, Rezaei A, Nuyts J, Fulton R, Kyme A, Lois C, Sari H, Price J, Boellaard R, Jeraj R, Bailey D L, Eslick E, Willowson K P and Dutta J. 2021. Quantitative PET in the 2020 s: a roadmap. Physics in Medicine and Biology, 66(6): #06RM01[DOI: 10.1088/1361-6560/abd4f7]
Moses W W. 2011. Fundamental limits of spatial resolution in PET. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 648(S1): 236-240[DOI: 10.1016/J.NIMA.2010.11.092]
Nagtegaal M, Koken P, Amthor T, De Bresser J, Mädler B, Vos F and Doneva M. 2020. Myelin water imaging from multi-echo T2 MR relaxometry data using a joint sparsity constraint. NeuroImage, 219: #117014[DOI: 10.1016/j.neuroimage.2020.117014]
Orlhac F, Eertink J J, Cottereau A S, Zijlstra J M, Thieblemont C, Meignan M A, Boellaard R and Buvat I. 2022. A guide to ComBat harmonization of imaging biomarkers in multicenter studies. The Journal of Nuclear Medicine, 63(2): 172-179[DOI: 10.2967/jnumed.121.262464]
Pan L Y, Cheng C X, Haberkorn U and Dimitrakopoulou-Strauss A. 2017. Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data. Physics in Medicine and Biology, 62(9): 3566-3581[DOI: 10.1088/1361-6560/aa6244]
Pantel A R, Viswanath V, Daube-Witherspoon M E, Dubroff J G, Muehllehner G, Parma M J, Pryma D A, Schubert E K, Mankoff D A and Karp J S. 2020. PennPET explorer: human imaging on a whole-body imager. The Journal of Nuclear Medicine, 61(1): 144-151[DOI: 10.2967/jnumed.119.231845]
Pomponio R, Erus G, Habes M, Doshi J, Srinivasan D, Mamourian E, Bashyam V, Nasrallah I M, Satterthwaite T D, Fan Y, Launer L J, Masters C L, Maruff P, Zhuo C J, Völzke H, Johnson S C, Fripp J, Koutsouleris N, Wolf D H, Gur R, Gur R, Morris J, Albert M S, Grabe H J, Resnick S M, Nick Bryan R, Wolk D A, Shinohara R T, Shou H C and Davatzikos C. 2020. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. NeuroImage, 208: #116450[DOI: 10.1016/j.neuroimage.2019.116450]
Reader A J, Corda G, Mehranian A, da Costa-Luis C, Ellis S and Schnabel J A. 2021. Deep learning for PET image reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences, 5(1): 1-25[DOI: 10.1109/trpms.2020.3014786]
Rieger B, Zimmer F, Zapp J, Weingärtner S and Schad L R. 2017. Magnetic resonance fingerprinting using echo-planar imaging: Joint quantification of T 1 and T 2 * relaxation times. Magnetic Resonance in Medicine, 78(5): 1724-1733[DOI: 10.1002/mrm.26561 ] .
Schmitt P, Griswold M A, Jakob P M, Kotas M, Gulani V, Flentje M and Haase A. 2004. Inversion recovery TrueFISP: quantification of T 1 , T 2 , and spin density. Magnetic Resonance in Medicine, 51(4): 661-667[DOI: 10.1002/mrm.20058 ] .
Schnack H G, Van Haren N E M, Brouwer R M, Van Baal G C M, Picchioni M, Weisbrod M, Sauer H, Cannon T D, Huttunen M, Lepage C, Collins D L, Evans A, Murray R M, Kahn R S and Hulshoff Pol H E. 2010. Mapping reliability in multicenter MRI: Voxel-based morphometry and cortical thickness. Human Brain Mapping, 31(12): 1967-1982[DOI: 10.1002/hbm.20991]
Shao H, Chang E Y, Pauli C, Zanganeh S, Bae W, Chung C B, Tang G and Du J. 2016. UTE bi-component analysis of T2 * relaxation in articular cartilage. Osteoarthritis and Cartilage, 24(2): 364-373[DOI: 10.1016/j.joca.2015.08.017 ]
Sheth V, Shao H D, Chen J, Vandenberg S, Corey-Bloom J, Bydder G M and Du J. 2016. Magnetic resonance imaging of myelin using ultrashort echo time (UTE) pulse sequences: phantom, specimen, volunteer and multiple sclerosis patient studies. NeuroImage, 136: 37-44[DOI: 10.1016/j.neuroimage.2016.05.012]
Smith S M and Nichols T E. 2018. Statistical challenges in "Big Data" human neuroimaging. Neuron, 97(2): 263-268[DOI: 10.1016/j.neuron.2017.12.018]
Song T A, Chowdhury S R, Yang F and Dutta J. 2020. PET image super-resolution using generative adversarial networks. Neural Networks, 125: 83-91[DOI: 10.1016/j.neunet.2020.01.029]
Su P, Mao D, Liu P Y, Li Y, Pinho M C, Welch B G and Lu H Z, 2017. Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL. Magnetic Resonance in Medicine, 78(5): 1812-1823[DOI: 10.1002/mrm.26587]
Surti S and Karp J S. 2016. Advances in time-of-flight PET. Physica Medica, 32(1): 12-22[DOI: 10.1016/J.EJMP.2015.12.007]
Tabesh A, Jensen J H, Ardekani B A and Helpern J A. 2011. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magnetic Resonance in Medicine, 65(3): 823-836[DOI: 10.1002/mrm.22655]
Tax C M, Grussu F, Kaden E, Ning L P, Rudrapatna U, John Evans C, St-Jean S, Leemans A, Koppers S, Merhof D, Ghosh A, Tanno R, Alexander D C, Zappalà S, Charron C, Kusmia S, Ej Linden D, Jones D K and Veraart J. 2019. Cross-scanner and cross-protocol diffusion MRI data harmonisation: a benchmark database and evaluation of algorithms. NeuroImage, 195: 285-299[DOI: 10.1016/j.neuroimage.2019.01.077]
Thie J A. 2004. Understanding the standardized uptake value, its methods, and implications for usage. The Journal of Nuclear Medicine, 45(9): 1431-1434
Tong Q Q, Gong T, He H J, Wang Z, Yu W W, Zhang J J, Zhai L H, Cui H S, Meng X, Tax C W M and Zhong J H. 2020. A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols. Magnetic Resonance Imaging, 73: 31-44[DOI: 10.1016/j.mri.2020.08.001]
Tong Q Q, He H J, Gong T, Li C, Liang P P, Qian T Y, Sun Y, Ding Q P, Li K C and Zhong J H. 2019. Reproducibility of multi-shell diffusion tractography on traveling subjects: a multicenter study prospective. Magnetic Resonance Imaging, 59: 1-9[DOI: 10.1016/j.mri.2019.02.011]
Vandenberghe S and Marsden P K. 2015. PET-MRI: a review of challenges and solutions in the development of integrated multimodality imaging. Physics in Medicine and Biology, 60(4): 115-154[DOI: 10.1088/0031-9155/60/4/R115]
Vaquero J J and Kinahan P. 2015. Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annual Review of Biomedical Engineering, 17(1): 385-414[DOI: 10.1146/annurev-bioeng-071114-040723]
Visvikis D, Cheze Le Rest C, Jaouen V and Hatt M. 2019. Artificial intelligence, machine (Deep) learning and Radio(Geno)Mics: definitions and nuclear medicine imaging applications. European Journal of Nuclear Medicine and Molecular Imaging, 46(13): 2630-2637[DOI: 10.1007/s00259-019-04373-w]
Wachinger C, Rieckmann A and Pölsterl S. 2021. Detect and correct bias in multi-site neuroimaging datasets. Medical Image Analysis, 67: #101879[DOI: 10.1016/j.media.2020.101879]
Wang B, Ruan D S and Liu H F. 2019. Noninvasive estimation of macro-parameters for dynamic PET images using deep learning//2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). Manchester, UK: IEEE: 1-4[ DOI: 10.1109/NSS/MIC42101.2019.9059691 http://dx.doi.org/10.1109/NSS/MIC42101.2019.9059691 ]
Warntjes J B M, Dahlqvist Leinhard O, West J and Lundberg P. 2008. Rapid magnetic resonance quantification on the brain: Optimization for clinical usage. Magnetic Resonance in Medicine, 60(2): 320-329[DOI: 10.1002/mrm.21635]
White T, Magnotta V A, Bockholt H J, Williams S, Wallace S, Ehrlich S, Mueller B A, Ho B C, Jung R E, Clark V P, Lauriello J, Bustillo J R, Schulz S C, Gollub R L, Andreasen N C, Calhoun V D and Lim K O. 2011. Global white matter abnormalities in schizophrenia: a multisite diffusion tensor imaging study. Schizophrenia Bulletin, 37(1): 222-232[DOI: 10.1093/schbul/sbp088]
Wilhelm M J, Ong H H, Wehrli S L, Li C, Tsai P H, Hackney D B and Wehrli F W. 2012. Direct magnetic resonance detection of myelin and prospects for quantitative imaging of myelin density. Proceedings of the National Academy of Sciences of the United States of America, 109(24): 9605-9610[DOI: 10.1073/pnas.1115107109]
Yang J, Park D, Gullberg G T and Seo Y. 2019. Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain 18 F-FDG PET. Physics in Medicine and Biology, 64(7): #075019[DOI: 10.1088/1361-6560/ab0606 ]
Ye H H, Cauley S F, Gagoski B, Bilgic B, Ma D, Jiang Y, Du Y P, Griswold M A, Wald L L and Setsompop K. 2017. Simultaneous multislice magnetic resonance fingerprinting (SMS-MRF) with direct-spiral slice-GRAPPA (ds-SG) reconstruction. Magnetic Resonance in Medicine, 77(5): 1966-1974[DOI: 10.1002/mrm.26271]
Ye H H, Ma D, Jiang Y, Cauley S F, Du Y P, Wald L L, Griswold M A and Setsompop K. 2016. Accelerating magnetic resonance fingerprinting (MRF) using t-blipped simultaneous multislice (SMS) acquisition. Magnetic Resonance in Medicine, 75(5): 2078-2085[DOI: 10.1002/mrm.25799]
Yu M C, Linn K A, Cook P A, Phillips M L, McInnis M, Fava M, Trivedi M H, Weissman M M, Shinohara R T and Sheline Y I. 2018. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Human Brain Mapping, 39(11): 4213-4227[DOI: 10.1002/hbm.24241]
Zanotti-Fregonara P, Chen K W, Liow J S, Fujita M and Innis R B. 2011. Image-derived input function for brain PET studies: Many challenges and few opportunities. Journal of Cerebral Blood Flow and Metabolism, 31(10): 1986-1998[DOI: 10.1038/jcbfm.2011.107]
Zhang X Z, Cherry S R, Xie Z H, Shi H C, Badawi R D and Qi J Y. 2020a. Subsecond total-body imaging using ultrasensitive positron emission tomography. Proceedings of the National Academy of Sciences of the United States of America, 117(5): 2265-2267[DOI: 10.1073/pnas.1917379117]
Zhang X Z, Xie Z H, Berg E, Judenhofer M S, Liu W P, Xu T Y, Ding Y, Lv Y, Dong Y, Deng Z L, Tang S S, Shi H C, Hu P C, Chen S G, Bao J, Li H D, Zhou J, Wang G B, Cherry S R, Badawi R D and Qi J Y. 2020b. Total-body dynamic reconstruction and parametric imaging on the UEXPLORER. The Journal of Nuclear Medicine, 61(2): 285-291[DOI: 10.2967/jnumed.119.230565]
Zhou Z W, Han P, Zhou B, Christodoulou A G, Shaw J L, Deng Z X and Li D B. 2018. Chemical exchange saturation transfer fingerprinting for exchange rate quantification. Magnetic Resonance in Medicine, 80(4): 1352-1363[DOI: 10.1002/mrm.27363]
相关作者
相关机构
京公网安备11010802024621
