21.8.10.1 Positron Emission Tomography, PET Image Reconstruction

Klyuzhin, I.S., Sossi, V.,
PET Image Reconstruction and Deformable Motion Correction Using Unorganized Point Clouds,
MedImg(36), No. 6, June 2017, pp. 1263-1275.
IEEE DOI 1706
Image reconstruction, Logic gates, Mathematical model, Positron emission tomography, Rodents, Deformation, Voronoi cell, image reconstruction, meshfree, motion correction, point, cloud BibRef

Tafsast, A.[Abdelghani], Hadjili, M.L.[Mohamed Laid], Bouakaz, A.[Ayache], Benoudjit, N.[Nabil],
Unsupervised cluster-based method for segmenting biological tumour volume of laryngeal tumours in 18F-FDG-PET images,
IET-IPR(11), No. 6, June 2017, pp. 389-396.
DOI Link 1706
BibRef

Freese, D.L., Hsu, D.F.C., Innes, D., Levin, C.S.,
Robust Timing Calibration for PET Using L1-Norm Minimization,
MedImg(36), No. 7, July 2017, pp. 1418-1426.
IEEE DOI 1707
Calibration, Crystals, Delays, Detectors, Minimization, Photonics, coincidence timing resolution, offset delay correction, positron emission tomography (PET), time-of-flight (TOF), timing alignment, timing, calibration BibRef

Miranda, A., Staelens, S., Stroobants, S., Verhaeghe, J.,
Fast and Accurate Rat Head Motion Tracking With Point Sources for Awake Brain PET,
MedImg(36), No. 7, July 2017, pp. 1573-1582.
IEEE DOI 1707
Adaptive optics, Head, Image reconstruction, Position measurement, Positron emission tomography, Rats, Tracking, Nuclear imaging, animal models and imaging, motion, compensation BibRef

Gong, K., Zhou, J., Tohme, M., Judenhofer, M., Yang, Y., Qi, J.,
Sinogram Blurring Matrix Estimation From Point Sources Measurements With Rank-One Approximation for Fully 3-D PET,
MedImg(36), No. 10, October 2017, pp. 2179-2188.
IEEE DOI 1710
Detectors, Estimation, Kernel, Photonics, Positron emission tomography, rank one approximation, resolution modeling, system, modeling BibRef

Mehranian, A., Belzunce, M.A., Prieto, C., Hammers, A., Reader, A.J.,
Synergistic PET and SENSE MR Image Reconstruction Using Joint Sparsity Regularization,
MedImg(37), No. 1, January 2018, pp. 20-34.
IEEE DOI 1801
biomedical MRI, image reconstruction, medical image processing, optimisation, positron emission tomography, ADMM optimization, PLS, total variation BibRef

Bergounioux, M.[Maitine], Papoutsellis, E.,
An Anisotropic Inf-Convolution BV Type Model for Dynamic Reconstruction,
SIIMS(11), No. 1, 2018, pp. 129-163.
DOI Link 1804
Apply to denoise, deblurring, tomography. BibRef

Hsu, D.F.C., Freese, D.L., Reynolds, P.D., Innes, D.R., Levin, C.S.,
Design and Performance of a 1 mm3 Resolution Clinical PET System Comprising 3-D Position Sensitive Scintillation Detectors,
MedImg(37), No. 4, April 2018, pp. 1058-1066.
IEEE DOI 1804
Breast, Crystals, Data acquisition, Detectors, Image resolution, Positron emission tomography, Biomedical imaging, system verifications BibRef

Tsai, Y.J., Bousse, A., Ehrhardt, M.J., Stearns, C.W., Ahn, S., Hutton, B.F., Arridge, S., Thielemans, K.,
Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning,
MedImg(37), No. 4, April 2018, pp. 1000-1010.
IEEE DOI 1804
Approximation algorithms, Convergence, Image reconstruction, Linear programming, Maximum likelihood estimation, Optimization, preconditioning BibRef

Yang, B., Ying, L., Tang, J.,
Artificial Neural Network Enhanced Bayesian PET Image Reconstruction,
MedImg(37), No. 6, June 2018, pp. 1297-1309.
IEEE DOI 1806
Bayes methods, Brain modeling, Image enhancement, Image reconstruction, Imaging, Iterative methods, maximum a posteriori image reconstruction BibRef

Ouerghi, H.[Hajer], Mourali, O.[Olfa], Zagrouba, E.[Ezzeddine],
Non-subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space,
IET-IPR(12), No. 10, October 2018, pp. 1873-1880.
DOI Link 1809
BibRef

Schramm, G., Holler, M., Rezaei, A., Vunckx, K., Knoll, F., Bredies, K., Boada, F., Nuyts, J.,
Evaluation of Parallel Level Sets and Bowsher's Method as Segmentation-Free Anatomical Priors for Time-of-Flight PET Reconstruction,
MedImg(37), No. 2, February 2018, pp. 590-603.
IEEE DOI 1802
Image reconstruction, Image resolution, Image segmentation, Level set, Positron emission tomography, positron emission tomography (PET) BibRef

Hong, X., Zan, Y., Weng, F., Tao, W., Peng, Q., Huang, Q.,
Enhancing the Image Quality via Transferred Deep Residual Learning of Coarse PET Sinograms,
MedImg(37), No. 10, October 2018, pp. 2322-2332.
IEEE DOI 1810
Crystals, Image quality, Positron emission tomography, Spatial resolution, Image reconstruction, transfer learning BibRef

Salvo, K., Defrise, M.,
A Convergence Proof of MLEM and MLEM-3 With Fixed Background,
MedImg(38), No. 3, March 2019, pp. 721-729.
IEEE DOI 1903
Convergence, Positron emission tomography, Image reconstruction, Data models, Photonics, Indexes, Maximum likelihood estimation, image reconstruction BibRef

Wang, G.,
High Temporal-Resolution Dynamic PET Image Reconstruction Using a New Spatiotemporal Kernel Method,
MedImg(38), No. 3, March 2019, pp. 664-674.
IEEE DOI 1903
Kernel, Image reconstruction, Spatiotemporal phenomena, Kinetic theory, Positron emission tomography, Correlation, spatiotemporal correlation BibRef

Filipovic, M., Barat, É., Dautremer, T., Comtat, C., Stute, S.,
PET Reconstruction of the Posterior Image Probability, Including Multimodal Images,
MedImg(38), No. 7, July 2019, pp. 1643-1654.
IEEE DOI 1907
Image reconstruction, Probability distribution, Uncertainty, Data models, Bayes methods, Mathematical model, Reliability, PET, spatial regularization BibRef

Lin, Y., Schmidtlein, C.R., Li, Q., Li, S., Xu, Y.,
A Krasnoselskii-Mann Algorithm With an Improved EM Preconditioner for PET Image Reconstruction,
MedImg(38), No. 9, September 2019, pp. 2114-2126.
IEEE DOI 1909
Image reconstruction, Convergence, TV, Positron emission tomography, Convex functions, Optimization, total variation BibRef

Gong, K., Berg, E., Cherry, S.R., Qi, J.,
Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction,
PIEEE(108), No. 1, January 2020, pp. 51-68.
IEEE DOI 2001
Photonics, Detectors, Machine learning, Image reconstruction, Deep learning, Positrons, Positron emission tomography, timing resolution BibRef

Scipioni, M., Pedemonte, S., Santarelli, M.F., Landini, L.,
Probabilistic Graphical Models for Dynamic PET: A Novel Approach to Direct Parametric Map Estimation and Image Reconstruction,
MedImg(39), No. 1, January 2020, pp. 152-160.
IEEE DOI 2001
Kinetic theory, Image reconstruction, Probabilistic logic, Uncertainty, Computational modeling, Photonics, Graphical models, probabilistic graphical modeling BibRef

Bousse, A., Courdurier, M., Émond, É., Thielemans, K., Hutton, B.F., Irarrazaval, P., Visvikis, D.,
PET Reconstruction With Non-Negativity Constraint in Projection Space: Optimization Through Hypo-Convergence,
MedImg(39), No. 1, January 2020, pp. 75-86.
IEEE DOI 2001
Image reconstruction, Maximum likelihood estimation, Positron emission tomography, Convergence, Linear programming, hypo-convergence BibRef

Huang, J., Mou, T., O'Regan, K., O'Sullivan, F.,
Spatial Auto-Regressive Analysis of Correlation in 3-D PET With Application to Model-Based Simulation of Data,
MedImg(39), No. 4, April 2020, pp. 964-974.
IEEE DOI 2004
Quality assurance, simulation, spatial autocorrelation, PET, iterative EM reconstruction, gamma distribution, standard errors BibRef

Rezaei, A., Schramm, G., van Laere, K., Nuyts, J.,
Estimation of Crystal Timing Properties and Efficiencies for the Improvement of (Joint) Maximum-Likelihood Reconstructions in TOF-PET,
MedImg(39), No. 4, April 2020, pp. 952-963.
IEEE DOI 2004
Time of flight (TOF), positron emission tomography (PET), timing calibration, joint reconstruction BibRef

Millardet, M., Moussaoui, S., Mateus, D., Idier, J., Carlier, T.,
Local-Mean Preserving Post-Processing Step for Non-Negativity Enforcement in PET Imaging: Application to 90Y-PET,
MedImg(39), No. 11, November 2020, pp. 3725-3736.
IEEE DOI 2011
Imaging, Linear programming, Image reconstruction, Laplace equations, Optimization, optimization BibRef

Jansen, F.P., Deller, T.W., Fries, M.D., Khalighi, M.M., White, S.L., Radford, L.L., McConathy, J.E., Lapi, S.E.,
Real-Time Gain Control of PET Detectors and Evaluation With Challenging Radionuclides,
MedImg(40), No. 1, January 2021, pp. 71-80.
IEEE DOI 2012
Detectors, Gain control, Measurement, Real-time systems, Positron emission tomography, PET/MR BibRef

Crisci, S., Piana, M., Ruggiero, V., Scussolini, M.,
A Regularized Affine-Scaling Trust-Region Method for Parametric Imaging of Dynamic PET Data,
SIIMS(14), No. 1, 2021, pp. 418-439.
DOI Link 2104
BibRef

Polycarpou, I.[Irene], Soultanidis, G.[Georgios], Tsoumpas, C.[Charalampos],
Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data,
MedImg(37), No. 3, March 2018, pp. 703-711.
IEEE DOI 1804
biomedical MRI, diseases, image reconstruction, image registration, medical image processing, motion compensation, phantoms, phantoms BibRef

Polycarpou, I.[Irene], Soultanidis, G.[Georgios], Tsoumpas, C.[Charalampos],
Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging,
Royal(A: 379), No. 2204, August 2021, pp. 20200207.
DOI Link 2107
MRI. BibRef

Brown, R.[Richard], Kolbitsch, C.[Christoph], Delplancke, C.[Claire], Papoutsellis, E.[Evangelos], Mayer, J.[Johannes], Ovtchinnikov, E.[Evgueni], Pasca, E.[Edoardo], Neji, R.[Radhouene], da Costa-Luis, C.[Casper], Gillman, A.G.[Ashley G.], Ehrhardt, M.J.[Matthias J.], McClelland, J.R.[Jamie R.], Eiben, B.[Bjoern], Thielemans, K.[Kris],
Motion estimation and correction for simultaneous PET/MR using SIRF and CIL,
Royal(A: 379), No. 2204, August 2021, pp. 20200208.
DOI Link 2107
MRI. BibRef

Li, S.Q.[Si-Qi], Wang, G.B.[Guo-Bao],
Modified kernel MLAA using autoencoder for PET-enabled dual-energy CT,
Royal(A: 379), No. 2204, August 2021, pp. 20200204.
DOI Link 2107
BibRef

Celik, A.A.[A. Avdo], Choi, C.H.[Chang-Hoon], Tellmann, L.[Lutz], Rick, C.[Claire], Shah, N.J.[N. Jon], Felder, J.[Jörg],
Design and Construction of a PET-Compatible Double-Tuned 1H/31P MR Head Coil,
MedImg(40), No. 8, August 2021, pp. 2015-2022.
IEEE DOI 2108
Radio frequency, Protons, Capacitors, Imaging, Absorption, Magnetic resonance imaging, Head, ¹H/³¹P, double-tuned RF coil, simultaneous PET-MR BibRef

Abouhawwash, M.[Mohamed], Alessio, A.M.[Adam M.],
Multi-Objective Evolutionary Algorithm for PET Image Reconstruction: Concept,
MedImg(40), No. 8, August 2021, pp. 2142-2151.
IEEE DOI 2108
Image reconstruction, Optimization, Statistics, Sociology, Positron emission tomography, Tomography, Genetic algorithms, emission tomography BibRef

Zhou, B.[Bo], Tsai, Y.J.[Yu-Jung], Chen, X.C.[Xiong-Chao], Duncan, J.S.[James S.], Liu, C.[Chi],
MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET,
MedImg(40), No. 11, November 2021, pp. 3154-3164.
IEEE DOI 2111
Logic gates, Noise reduction, Motion estimation, Positron emission tomography, Image reconstruction, deep learning BibRef

Yang, B.[Bao], Wang, X.H.[Xin-Hui], Li, A.[Andi], Moody, J.B.[Jonathan B.], Tang, J.[Jing],
Dictionary Learning Constrained Direct Parametric Estimation in Dynamic Myocardial Perfusion PET,
MedImg(40), No. 12, December 2021, pp. 3485-3497.
IEEE DOI 2112
Image reconstruction, Heuristic algorithms, Positron emission tomography, Myocardium, Kinetic theory, Imaging, sparse representation BibRef

Hu, S.Y.[Sheng-Ye], Lei, B.Y.[Bai-Ying], Wang, S.Q.[Shu-Qiang], Wang, Y.[Yong], Feng, Z.G.[Zhi-Guang], Shen, Y.Y.[Yan-Yan],
Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis,
MedImg(41), No. 1, January 2022, pp. 145-157.
IEEE DOI 2201
Generators, Generative adversarial networks, Positron emission tomography, Training, Image synthesis, bidirectional mapping mechanism BibRef

Zhou, Y.[Yang], Yang, Z.W.[Zhi-Wen], Zhang, H.[Hui], Chang, E.I.C.[Eric I-Chao], Fan, Y.[Yubo], Xu, Y.[Yan],
3D Segmentation Guided Style-Based Generative Adversarial Networks for PET Synthesis,
MedImg(41), No. 8, August 2022, pp. 2092-2104.
IEEE DOI 2208
Image segmentation, Task analysis, Positron emission tomography, Modulation, Image synthesis, Generators, PET, GAN, style modulation, segmentation BibRef

Guo, J.F.[Jian-Feng], Schmidtlein, C.R.[C. Ross], Krol, A.[Andrzej], Li, S.[Si], Lin, Y.Z.[Yi-Zun], Ahn, S.[Sangtae], Stearns, C.[Charles], Xu, Y.S.[Yue-Sheng],
A Fast Convergent Ordered-Subsets Algorithm With Subiteration-Dependent Preconditioners for PET Image Reconstruction,
MedImg(41), No. 11, November 2022, pp. 3289-3300.
IEEE DOI 2211
Image reconstruction, Positron emission tomography, Convergence, Germanium, Phantoms, Cancer, Software algorithms, relative difference prior BibRef

Li, S.Q.[Si-Qi], Wang, G.B.[Guo-Bao],
Deep Kernel Representation for Image Reconstruction in PET,
MedImg(41), No. 11, November 2022, pp. 3029-3038.
IEEE DOI 2211
Kernel, Image reconstruction, Positron emission tomography, Neural networks, Feature extraction, Data models, Tomography, neural networks BibRef

Twyman, R.[Robert], Arridge, S.[Simon], Kereta, Z.[Zeljko], Jin, B.[Bangti], Brusaferri, L.[Ludovica], Ahn, S.[Sangtae], Stearns, C.W.[Charles W.], Hutton, B.F.[Brian F.], Burger, I.A.[Irene A.], Kotasidis, F.[Fotis], Thielemans, K.[Kris],
An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction,
MedImg(42), No. 1, January 2023, pp. 29-41.
IEEE DOI 2301
Image reconstruction, Convergence, Positron emission tomography, Linear programming, Stochastic processes, Optimization, Indexes, relative difference penalty BibRef

Ote, K.[Kibo], Hashimoto, F.[Fumio], Onishi, Y.[Yuya], Isobe, T.[Takashi], Ouchi, Y.[Yasuomi],
List-Mode PET Image Reconstruction Using Deep Image Prior,
MedImg(42), No. 6, June 2023, pp. 1822-1834.
IEEE DOI 2306
Image reconstruction, Positron emission tomography, Electronics packaging, Convolutional neural networks, unsupervised learning BibRef

Guo, S.[Shiyao], Sheng, Y.X.[Yu-Xia], Chai, L.[Li], Zhang, J.X.[Jing-Xin],
PET Image Reconstruction With Kernel and Kernel Space Composite Regularizer,
MedImg(42), No. 6, June 2023, pp. 1786-1798.
IEEE DOI 2306
Kernel, Image reconstruction, Optimization, Iterative methods, Sensitivity, Maximum likelihood estimation, Lesions, kernel space energy regularizer BibRef

Zhang, Q.Y.[Qi-Yang], Hu, Y.Y.[Ying-Ying], Zhao, Y.[Yumo], Cheng, J.[Jing], Fan, W.[Wei], Hu, D.B.[De-Bin], Shi, F.X.[Fu-Xiao], Cao, S.L.[Shuang-Liang], Zhou, Y.[Yun], Yang, Y.F.[Yong-Feng], Liu, X.[Xin], Zheng, H.R.[Hai-Rong], Liang, D.[Dong], Hu, Z.L.[Zhan-Li],
Deep Generalized Learning Model for PET Image Reconstruction,
MedImg(43), No. 1, January 2024, pp. 122-134.
IEEE DOI 2401
BibRef

Onishi, Y.[Yuya], Hashimoto, F.[Fumio], Ote, K.[Kibo], Ota, R.[Ryosuke],
Whole Reconstruction-Free System Design for Direct Positron Emission Imaging From Image Generation to Attenuation Correction,
MedImg(43), No. 5, May 2024, pp. 1654-1663.
IEEE DOI 2405
Imaging, Image reconstruction, Scattering, Positrons, Detectors, Photonics, Attenuation, Attenuation correction, compton scatter, time-of-flight BibRef

Jang, S.I.[Se-In], Pan, T.S.[Tin-Su], Li, Y.[Ye], Heidari, P.[Pedram], Chen, J.Y.[Jun-Yu], Li, Q.Z.[Quan-Zheng], Gong, K.[Kuang],
Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising,
MedImg(43), No. 6, June 2024, pp. 2036-2049.
IEEE DOI 2406
Transformers, Positron emission tomography, Biomedical imaging, Image denoising, Image restoration, Decoding, Convolution, local and global self-attention BibRef

Cui, J.Q.[Jia-Qi], Wang, Y.[Yan], Zhou, L.P.[Lu-Ping], Fei, Y.C.[Yu-Chen], Zhou, J.[Jiliu], Shen, D.G.[Ding-Gang],
3D Point-Based Multi-Modal Context Clusters GAN for Low-Dose PET Image Denoising,
CirSysVideo(34), No. 10, October 2024, pp. 9400-9413.
IEEE DOI 2411
Magnetic resonance imaging, Positron emission tomography, Noise reduction, Point cloud compression, Image denoising, generative adversarial network (GAN) BibRef

Li, Y.[Yanyi], Yin, J.P.[Jian-Ping],
Adaptive Sampling Method for Whole-Body Low-Dose Pet Reconstruction Based on Reconstruction Difficulty,
ICIP24(2839-2845)
IEEE DOI 2411
Training, Hospitals, Noise reduction, Noise, Imaging, Whole-body PET, Sampling methods, Low-dose PET Reconstruction, Deep Learning, Image Denoising BibRef

Sharma, V.[Vatsala], Awate, S.P.[Suyash P.],
Adversarial EM For Partially-Supervised Image-Quality Enhancement: Application to Low-Dose PET Imaging,
ICIP24(4007-4013)
IEEE DOI 2411
Training, Degradation, Visualization, Uncertainty, Image analysis, Perturbation methods, Supervised learning, PET BibRef

Rossignol, C.[Claire], Sureau, F.[Florent], Chouzenoux, E.[Emilie], Comtat, C.[Claude], Pesquet, J.C.[Jean-Christophe],
A Bregman Majorization-Minimization Framework for Pet Image Reconstruction,
ICIP22(1736-1740)
IEEE DOI 2211
Neurology, Inverse problems, Heuristic algorithms, Graphics processing units, Programming, Oncology, GPU implementation BibRef

Boudjelal, A.[Abdelwahhab], El Moataz, A.[Abderrahim], Messali, Z.[Zoubeida],
A New Method of Image Reconstruction for PET Using a Combined Regularization Algorithm,
ICISP20(178-185).
Springer DOI 2009
BibRef

Yamaya, T.,
Plenary speaker: Development of the next generation positron emission tomography,
IVPR17(1-1)
IEEE DOI 1704
Atmospheric measurements BibRef

Abbas, S.T.[Syed Tabish], Sivaswamy, J.[Jayanthi],
PET image reconstruction and denoising on hexagonal lattices,
ICIP15(3481-3485)
IEEE DOI 1512
Denoising; Hexagonal lattice; Image Reconstruction; PET Images BibRef

Bilgel, M.[Murat], Carass, A.[Aaron], Resnick, S.M.[Susan M.], Wong, D.F.[Dean F.], Prince, J.L.[Jerry L.],
Deformation Field Correction for Spatial Normalization of PET Images Using a Population-Derived Partial Least Squares Model,
MLMI14(198-206).
Springer DOI 1410
BibRef

Sun, Y.P.[Yi-Peng], Tao, X.M.[Xiao-Ming], Li, Y.[Yang], Fall, M.D.[Marne Diarra], Barai, E.[Eric], Comtat, C.[Claude], Dautremer, T.[Thomas], Montagu, T.[Thierry], Stute, S.[Simon],
Dynamic and clinical PET data reconstruction: A nonparametric Bayesian approach,
ICIP13(345-349)
IEEE DOI 1402
Bayes methods BibRef

Sroubek, F.[Filip], Sorel, M.[Michal], Boldys, J.[Jiri], Sroubek, J.[Jan],
PET image reconstruction using prior information from CT or MRI,
ICIP09(2493-2496).
IEEE DOI 0911
BibRef

Jin, M.W.[Ming-Wu], Yang, Y.Y.[Yong-Yi], Wernick, M.N.[Miles N.],
Dynamic Image Reconstruction Using Temporally Adaptive Regularization for Emission Tomography,
ICIP07(IV: 141-144).
IEEE DOI 0709

See also Gated Dynamic Image Reconstruction Using Temporal B-Splines. BibRef

Yan, J.H.[Jian-Hua], Yu, J.[Jun],
A Nonlinear Variational Model for PET Reconstruction,
ICPR06(IV: 699-702).
IEEE DOI 0609
BibRef

Liu, H.F.[Hua-Feng], Jiang, X.N.[Xiao-Na], Shi, P.C.[Peng-Cheng],
Uncertainty Penalized Weighted Least Squares Framework for PET Reconstruction Under Uncertain System Models,
ICIP05(III: 736-739).
IEEE DOI 0512
BibRef

Frese, T., Bouman, C.,
Bayesian Multiresolution Algorithm for PET Reconstruction,
ICIP00(Vol II: 613-616).
IEEE DOI 0008
BibRef

Bonneville, M., Meunier, J., Roy, S.,
Graph cut: application to Bayesian emission tomography reconstruction,
CIAP99(1184-1189).
IEEE DOI 9909
BibRef

Velipasaoglu, E.O., Ersoy, O.K.,
Optimal block-size selection for iterative reconstruction of images from projections,
ICIP98(II: 676-679).
IEEE DOI 9810
BibRef
Earlier:
Block iterative methods for Bayesian segmentation of positron emission tomography images,
ICIP96(II: 269-272).
IEEE DOI 9610
BibRef

Furuie, S.S., Herman, G.T., Mascarenhas, N.D.A., Matej, S.,
Projection estimation in 3D PET reconstruction: a statistical evaluation,
ICIP95(I: 618-621).
IEEE DOI 9510
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Positron Emission Tomography, Fessler Group Papers .

Last update:Nov 26, 2024 at 16:40:19