# Artificial Intelligence for Neuro MRI Acquisition: A Review

arXiv: https://arxiv.org/abs/2406.05982 · HTML: https://mlsanity.com/paper/2406.05982/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 133 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 30 of 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Active MR k-space Sampling with Reinforcement Learning](https://mlsanity.com/paper/2007.10469/) (2020)
- [B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI](https://mlsanity.com/paper/2101.11369/) (2021)
- [PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI](https://mlsanity.com/paper/1909.05773/) (2019)
- [Learning the Sampling Pattern for MRI](https://mlsanity.com/paper/1906.08754/) (2019)
- [Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents](https://mlsanity.com/paper/1806.03228/) (2018)
- [OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRI](https://mlsanity.com/paper/1805.00524/) (2018)
- [MRzero -- Fully automated discovery of MRI sequences using supervised learning](https://mlsanity.com/paper/2002.04265/) (2020)
- [Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI](https://mlsanity.com/paper/2004.10536/) (2020)
- [Deep reinforcement learning-designed radiofrequency waveform in MRI](https://mlsanity.com/paper/2105.03061/) (2021)
- [FD-Net: An Unsupervised Deep Forward-Distortion Model for Susceptibility Artifact Correction in EPI](https://mlsanity.com/paper/2303.10436/) (2023)
- [Distortion-Corrected Image Reconstruction with Deep Learning on an MRI-Linac](https://mlsanity.com/paper/2205.10993/) (2022)
- [Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT)](https://mlsanity.com/paper/2111.02912/) (2021)
- [High-fidelity, high-isotropic resolution diffusion imaging through gSlider acquisition with B1+ & T1 corrections and integrated DeltaB0/Rx shim array](https://mlsanity.com/paper/1811.05473/) (2018)
- [3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI](https://mlsanity.com/paper/2008.04808/) (2020)
- [Joint Design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRI](https://mlsanity.com/paper/2008.10594/) (2020)
- [Off-resonance artifact correction for magnetic resonance imaging: a review](https://mlsanity.com/paper/2205.01028/) (2022)
- [Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams](https://mlsanity.com/paper/2106.08963/) (2021)
- [Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC within 3 minutes](https://mlsanity.com/paper/2205.02335/) (2022)
- [Spurious minimizers in non uniform Fourier sampling optimization](https://mlsanity.com/paper/2207.10323/) (2022)
- [Adaptive Sampling for Linear Sensing Systems via Langevin Dynamics](https://mlsanity.com/paper/2302.13468/) (2023)
- [On Variant Strategies To Solve The Magnitude Least Squares Optimization Problem In Parallel Transmission Pulse Design And Under Strict SAR And Power Constraints](https://mlsanity.com/paper/1309.1567/) (2013)
- [Efficient T2 mapping with Blip-up/down EPI and gSlider-SMS (T2-BUDA-gSlider)](https://mlsanity.com/paper/1909.12999/) (2019)
- [Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-stack Attention Neural Network](https://mlsanity.com/paper/2211.04703/) (2022)
- [Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution](https://mlsanity.com/paper/2305.07524/) (2023)

## Built on by (most cited first, 1 of 1)

- [Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset](https://mlsanity.com/paper/2412.06666/) (2024)

## Abstract (arXiv)

Magnetic resonance imaging (MRI) has significantly benefited from the resurgence of artificial intelligence (AI). By leveraging AI's capabilities in large-scale optimization and pattern recognition, innovative methods are transforming the MRI acquisition workflow, including planning, sequence design, and correction of acquisition artifacts. These emerging algorithms demonstrate substantial potential in enhancing the efficiency and throughput of acquisition steps. This review discusses several pivotal AI-based methods in neuro MRI acquisition, focusing on their technological advances, impact on clinical practice, and potential risks.