Mehmet Akcakaya

University of Minnesota

United States

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Articles (21)

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2024

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2021

Collaborators (14)

Kerstin Hammernik

Technical University of Munich

GERMANY

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Logan Dowdle

Assistant Professor

University of Maastricht

NETHERLANDS

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Lynn Eberly

University of Minnesota

UNITED STATES

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Daniel Rueckert

Director, Alexander von Humboldt Professor for AI

Technical University of Munich

GERMANY

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Chetan Shenoy

University of Minnesota

UNITED STATES

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Maša Božić-Iven

Heidelberg University

GERMANY

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Chiara Coletti

University of Oxford

UNITED KINGDOM

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Armin Nagel

Professor for metabolic and functional MR imaging

Universitätsklinikum Erlangen

GERMANY

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Sebastian Weingärtner

Delft University of Technology

NETHERLANDS

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Qian Tao

Delft University of Technology

NETHERLANDS

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Jutta Ellermann

Professor with Tenure

University of Minnesota

UNITED STATES

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Geoffrey Ghose

Faculty, Professor

University of Minnesota

UNITED STATES

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Federico De Martino

University of Maastricht

NETHERLANDS

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Joao Tourais

Delft University of Technology

NETHERLANDS

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Mehmet Akcakaya

Research Interests

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Articles (21)

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

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

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

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

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

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

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

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

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

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

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

Evaluating increases in sensitivity from NORDIC for diverse fMRI acquisition strategies

Cardiac phase-resolved late gadolinium enhancement imaging

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

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

Residual RAKI

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

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

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

Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement

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

Collaborators (14)

Kerstin Hammernik

Logan Dowdle

Lynn Eberly

Daniel Rueckert

Chetan Shenoy

Maša Božić-Iven

Chiara Coletti

Armin Nagel

Sebastian Weingärtner

Qian Tao

Jutta Ellermann

Geoffrey Ghose

Federico De Martino

Joao Tourais

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