Jeffrey Fessler

Professor

University of Michigan

United States

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Jeffrey Fessler is a Professor at the University of Michigan in the United States. His research primarily focuses on advanced imaging techniques, quantitative MR imaging, and machine learning applications in medical imaging. Recent contributions include studies on tissue sample preparation methods, multidimensional RF pulse design, and innovative reconstruction algorithms in MRI and CT imaging.

Jeffrey Fessler

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Articles (22)

ALPCAH: Subspace Learning for Sample-Wise Heteroscedastic Data

Impact of tissue sample preparation methods on myelin‐sensitive quantitative MR imaging

Multidimensional RF pulse design using auto‐differentiable spin‐domain optimization and its application to reduced field‐of‐view imaging

Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction to ICF Applications

Test-Time Adaptation Improves Inverse Problem Solving With Patch-Based Diffusion Models

A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI

Model‐based reconstruction for looping‐star MRI

FMCW Inverse Circular Synthetic Aperture Radar Using a Fast Time-Domain Reconstruction

Manifold Regularizer for High-Resolution fMRI Joint Reconstruction and Dynamic Quantification

Fused Multi-Modal Electron Microscopy

Accelerated Wirtinger Flow With Score-Based Image Priors for Holographic Phase Retrieval in Poisson-Gaussian Noise Conditions

Stochastic optimization of three‐dimensional non‐Cartesian sampling trajectory

Momentum-Net: Fast and Convergent Iterative Neural Network for Inverse Problems

Optimally Weighted PCA for High-Dimensional Heteroscedastic Data

Efficient Approximation of Jacobian Matrices Involving a Non-Uniform Fast Fourier Transform (NUFFT)

Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT

Sparse-View Cone Beam CT Reconstruction Using Data-Consistent Supervised and Adversarial Learning From Scarce Training Data

Training End-to-End Unrolled Iterative Neural Networks for SPECT Image Reconstruction

SPECT Reconstruction With a Trained Regularizer Using CT-Side Information: Application to $^{177}$Lu SPECT Imaging

Focal stack camera: depth estimation performance comparison and design exploration

B-Spline Parameterized Joint Optimization of Reconstruction and K-Space Trajectories (BJORK) for Accelerated 2D MRI

Cramér–Rao Bound Evaluations of Compton Imager Designs for Proton Beam Range Verification

Collaborators (10)

Hongki Lim

Laura Balzano

Se Young Chun

Il Yong Chun

Yuni Dewaraja

Douglas Noll

Liyue Shen

Yu-Tsun Shao

Luis Hernandez-Garcia

Kamal Sarabandi

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