# Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)

arXiv: https://arxiv.org/abs/2501.09799 · HTML: https://mlsanity.com/paper/2501.09799/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition](https://mlsanity.com/paper/2603.21806/) (2026)
- [NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRI](https://mlsanity.com/paper/2605.11583/) (2026)

## Abstract (arXiv)

Accelerated MRI involves collecting partial k-space measurements to reduce acquisition time, patient discomfort, and motion artifacts, and typically uses regular undersampling patterns or human-designed schemes. Recent works have studied population-adaptive sampling patterns learned from a group of patients (or scans). However, such patterns can be sub-optimal for individual scans, as they may fail to capture scan or slice-specific details, and their effectiveness can depend on the size and composition of the population. To overcome this issue, we propose a framework for jointly learning scan-adaptive Cartesian undersampling patterns and a corresponding reconstruction model from a training set. We use an alternating algorithm for learning the sampling patterns and the reconstruction model where we use an iterative coordinate descent (ICD) based offline optimization of scan-adaptive k-space sampling patterns for each example in the training set. A nearest neighbor search is then used to select the scan-adaptive sampling pattern at test time from initially acquired low-frequency k-space information. We applied the proposed framework (dubbed SUNO) to the fastMRI multi-coil knee and brain datasets, demonstrating improved performance over the currently used undersampling patterns at both 4× and 8× acceleration factors in terms of both visual quality and quantitative metrics. The code for the proposed framework is available at https://github.com/sidgautam95/adaptive-sampling-mri-suno. This paper has been accepted for publication in IEEE Transactions on Computational Imaging. The final published version is available at https://doi.org/10.1109/TCI.2026.3653330.