44 citations · 46 across the 4 of their papers we have counts for
9 papers
A Technical Primer on the Physical Modeling of Diffusion-Encoded Magnetic Resonance Experiments: A Random Process Perspective
Justin P. Haldar
Diffusion-encoded magnetic resonance (MR) experiments can provide important insights into the microstructural characteristics of a variety of biological tissues and other fluid- or…
PALMNUT: An Enhanced Proximal Alternating Linearized Minimization Algorithm with Application to Separate Regularization of Magnitude and Phase
Yunsong Liu, Justin P. Haldar
We introduce a new algorithm for complex image reconstruction with separate regularization of the image magnitude and phase. This optimization problem is interesting in many differ…
3D Phase Retrieval at Nano-Scale via Accelerated Wirtinger Flow
Zalan Fabian, Justin Haldar, Richard Leahy +1
Imaging 3D nano-structures at very high resolution is crucial in a variety of scientific fields. However, due to fundamental limitations of light propagation we can only measure th…
Optimal Sampling & Reconstruction: Theory and Applications
Justin P. Haldar
The optimization of MRI data sampling and image reconstruction methods has been a priority for the MRI community since the very early days of the field. Designing an "optimal" meth…
Fast Sub-millimeter Diffusion MRI using gSlider-SMS and SNR-Enhancing Joint Reconstruction
Justin P. Haldar, Qiuyun Fan, Kawin Setsompop
We evaluate a new approach for achieving diffusion MRI data with high spatial resolution, large volume coverage, and fast acquisition speed. A recent method called gSlider-SMS enab…
LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space
Tae Hyung Kim, Pratyush Garg, Justin P. Haldar
We propose and evaluate a new MRI reconstruction method named LORAKI that trains an autocalibrated scan-specific recurrent neural network (RNN) to recover missing k-space data. Met…