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A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping.

A. Fazlollahi, S. Ayton, P. Bourgeat, P. Raniga, A. Ng, J. Fripp, D. Ames, C. Masters, C. Rowe, V. Villemagne, A. Bush, and O. Salvado. ISBI, page 97-100. IEEE, (2017)

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Mansoor Anwer Ismail Amir

Belal Amireh

Borzu Amirhosseini

Manutschehr Amirfeyz

Iradj Amirzadeh-Asl

Other publications of authors with the same name

Synthetic microbleeds generation for classifier training without ground truth.S. Momeni, A. Fazlollahi, P. Yates, C. Rowe, Y. Gao, A. Liew, and O. Salvado. Comput. Methods Programs Biomed., (2021)Automatic Brain Tumour Segmentation in 18F-FDOPA PET Using PET/MRI Fusion.A. Fazlollahi, N. Dowson, F. Mériaudeau, S. Rose, M. Fay, P. Thomas, Z. Taylor, Y. Gal, A. Coulthard, C. Winter and 4 other author(s). DICTA, page 325-329. IEEE Computer Society, (2011)Automatic detection of small spherical lesions using multiscale approach in 3D medical images.A. Fazlollahi, F. Mériaudeau, V. Villemagne, C. Rowe, P. Desmond, P. Yates, O. Salvado, and P. Bourgeat. ICIP, page 1158-1162. IEEE, (2013)Data Augmentation Using Synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI.S. Momeni, A. Fazlollahi, P. Bourgeat, P. Raniga, P. Yates, N. Yassi, P. Desmond, J. Fripp, Y. Gao, and O. Salvado. SASHIMI@MICCAI, volume 11037 of Lecture Notes in Computer Science, page 12-19. Springer, (2018)Line segment based structure and motion from two views.S. Mosaddegh, A. Fazlollahi, D. Fofi, and P. Vasseur. Image Processing: Machine Vision Applications, volume 7877 of SPIE Proceedings, page 787709. SPIE, (2011)A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping.A. Fazlollahi, S. Ayton, P. Bourgeat, P. Raniga, A. Ng, J. Fripp, D. Ames, C. Masters, C. Rowe, V. Villemagne and 2 other author(s). ISBI, page 97-100. IEEE, (2017)Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP).Z. Xiong, Y. Gao, Y. Liu, A. Fazlollahi, P. Nestor, F. Liu, and H. Sun. CoRR, (2023)Efficient machine learning framework for computer-aided detection of cerebral microbleeds using the Radon transform.A. Fazlollahi, F. Mériaudeau, V. Villemagne, C. Rowe, P. Yates, O. Salvado, and P. Bourgeat. ISBI, page 113-116. IEEE, (2014)G.fast for FTTdp: Enabling gigabit copper access.D. Wei, A. Fazlollahi, G. Long, and E. Wang. GLOBECOM Workshops, page 668-673. IEEE, (2014)Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods.A. Fazlollahi, P. Bourgeat, X. Liang, F. Mériaudeau, A. Connelly, O. Salvado, and F. Calamante. NeuroImage, (2015)

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Disambiguation of "Fazlollahi, Amir"

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A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping.

Please choose a person to relate this publication to

Mansoor Anwer Ismail Amir

Belal Amireh

Borzu Amirhosseini

Manutschehr Amirfeyz

Iradj Amirzadeh-Asl

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Fazlollahi, Amir"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping.

Please choose a person to relate this publication to

Mansoor Anwer Ismail Amir

Belal Amireh

Borzu Amirhosseini

Manutschehr Amirfeyz

Iradj Amirzadeh-Asl

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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A normalisation framework for quantitative brain imaging; application to quantitative susceptibility mapping.