Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Kratika Gupta"'
Publikováno v:
ISBI
Accelerated resting-state functional magnetic resonance imaging (R-fMRI) can provide higher spatial resolution and improved brain connectivity maps. Current methods for fast R-fMRI rely on either fully-sampled parallel imaging or undersampled reconst
Autor:
Suyash P. Awate, Kratika Gupta
Publikováno v:
ICIP
In high-angular-resolution diffusion imaging (HARDI), simultaneous multislice (SMS) acquisition incorporated in multi-coil parallel imaging offers speedups in addition to the speedup obtained from undersampling gradient directions. We propose a novel
Autor:
Kratika Gupta, Suyash P. Awate
Publikováno v:
ICIP
High-angular-resolution diffusion imaging (HARDI) relies on multicoil acquisitions for clinical applications. HARDI scan time can be reduced by undersampling the set of gradient directions. Typical methods for undersampled HARDI reconstruction use tw
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030322472
MICCAI (3)
MICCAI (3)
Simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) provide complementary information about brain function and structure. Joint reconstruction of MRI and PET images can improve image quality in both modalities, potent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02179cfdb551eeab0f8f33b36b48f9c8
https://doi.org/10.1007/978-3-030-32248-9_5
https://doi.org/10.1007/978-3-030-32248-9_5
Autor:
Kratika Gupta, Suyash P. Awate
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319590493
IPMI
IPMI
High angular resolution diffusion imaging (HARDI) at higher b values leads to signal measurements having (exponentially) lower magnitudes, a strong Rician bias, and more corruptions from artifacts. Typical undersampled-HARDI reconstruction methods as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f6cc73de396b402dd91708093484370b
https://doi.org/10.1007/978-3-319-59050-9_36
https://doi.org/10.1007/978-3-319-59050-9_36
Regularized Dictionary Learning with Robust Sparsity Fitting for Compressed Sensing Multishell HARDI
Publikováno v:
Computational Diffusion MRI ISBN: 9783319541297
This paper presents a new compressed sensing framework for multishell HARDI. Unlike methods that model diffusion signals using analytical bases, we learn a dictionary of multishell diffusion signals, with a proposed regularization term to handle low
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a38e02d8d5956e38ec2505352294c80
https://doi.org/10.1007/978-3-319-54130-3_3
https://doi.org/10.1007/978-3-319-54130-3_3
Publikováno v:
RecSys
Repeat Purchases have become increasingly important in measuring customer's satisfaction and loyalty to e-commerce websites in regard to online shopping. In this paper, we first propose a model for estimating repeat purchase frequency in a given time