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Mehmet Akcakaya

University of Minnesota

United States

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Research Interests

Medical Imaging

100%

Computational Neuroscience

70%

Magnetic Resonance Imaging

90%

In Vivo Imaging

70%

Molecular Imaging

50%

Cardiac Imaging

50%

Neuroimaging

40%

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Articles21

Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data

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Year:

2024

Unsupervised deep learning with convolutional neural networks for static parallel transmit design

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Year:

2024

Feasibility of relaxation along a fictitious field in the 2nd rotating frame (TRAFF2) mapping in the human myocardium at 3 T

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Year:

2024

Reducing thermal noise in high-resolution quantitative magnetic resonance imaging rotating frame relaxation mapping of the human brain at 3 T

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Year:

2024

Improved quantitative parameter estimation for prostate T2 relaxometry using convolutional neural networks

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Year:

2024

UNCERTAINTY-GUIDED PHYSICS-DRIVEN DEEP LEARNING RECONSTRUCTION VIA CYCLIC MEASUREMENT CONSISTENCY

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Year:

2024

Robust cardiac T1ρ$$ {\mathrm{T}}_{1_{\boldsymbol{\rho}}} $$ mapping at 3T using adiabatic spin‐lock preparations

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Year:

2023

Improved Simultaneous Multi-slice imaging with Composition of k-space Interpolations (SMS-COOKIE) for myocardial T1 mapping

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Year:

2023

Signal intensity informed multi‐coil encoding operator for physics‐guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

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Year:

2023

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

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Year:

2023

High-fidelity Database-free Deep Learning Reconstruction for Real-time Cine Cardiac MRI

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Year:

2023

Evaluating increases in sensitivity from NORDIC for diverse fMRI acquisition strategies

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Year:

2023

Cardiac phase-resolved late gadolinium enhancement imaging

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Year:

2022

Revisiting ℓ1-wavelet compressed-sensing MRI in the era of deep learning

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Year:

2022

Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging

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Year:

2022

Residual RAKI

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Year:

2022

Adiabatic spin-lock preparations enable robust in vivo cardiac T1ρ-mapping at 3T

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Year:

2022

Myocardial Approximate Spin-lock Dispersion Mapping using a Simultaneous T2 and TRAFF2 Mapping at 3T MRI

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Year:

2022

Signal-Intensity Informed Multi-Coil MRI Encoding Operator for Improved Physics-Guided Deep Learning Reconstruction of Dynamic Contrast-Enhanced MRI

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Year:

2022

Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement

Article Link

Year:

2022

Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging

Article Link

Year:

2021

Collaborators14

Kerstin Hammernik

Technical University of Munich

GERMANY

Logan Dowdle

Assistant Professor

University of Maastricht

NETHERLANDS

Lynn Eberly

University of Minnesota

UNITED STATES

Daniel Rueckert

Director, Alexander von Humboldt Professor for AI

Technical University of Munich

GERMANY

Chetan Shenoy

University of Minnesota

UNITED STATES

Maša Božić-Iven

Heidelberg University

GERMANY

Chiara Coletti

University of Oxford

UNITED KINGDOM

Armin Nagel

Professor for metabolic and functional MR imaging

Universitätsklinikum Erlangen

GERMANY

Sebastian Weingärtner

Delft University of Technology

NETHERLANDS

Qian Tao

Delft University of Technology

NETHERLANDS

Jutta Ellermann

Professor with Tenure

University of Minnesota

UNITED STATES

Geoffrey Ghose

Faculty, Professor

University of Minnesota

UNITED STATES

Federico De Martino

University of Maastricht

NETHERLANDS

Joao Tourais

Delft University of Technology

NETHERLANDS

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