Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction
<|reference_start|>Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction: Magnetic Resonance (MR) imaging, despite its proven diagnostic utility, remains an inaccessible imaging modality for disease surveillance at the population level. A major factor rendering MR inaccessible is lengthy scan times. An MR scanner collects measurements associated with the underlying anatomy in the Fourier space, also known as the k-space. Creating a high-fidelity image requires collecting large quantities of such measurements, increasing the scan time. Traditionally to accelerate an MR scan, image reconstruction from under-sampled k-space data is the method of choice. However, recent works show the feasibility of bypassing image reconstruction and directly learning to detect disease directly from a sparser learned subset of the k-space measurements. In this work, we propose Adaptive Sampling for MR (ASMR), a sampling method that learns an adaptive policy to sequentially select k-space samples to optimize for target disease detection. On 6 out of 8 pathology classification tasks spanning the Knee, Brain, and Prostate MR scans, ASMR reaches within 2% of the performance of a fully sampled classifier while using only 8% of the k-space, as well as outperforming prior state-of-the-art work in k-space sampling such as EMRT, LOUPE, and DPS.<|reference_end|>
@article{yen2024adaptive,
    title={Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology
  Prediction},
    author={Chen-Yu Yen, Raghav Singhal, Umang Sharma, Rajesh Ranganath, Sumit
  Chopra, Lerrel Pinto},
    journal={arXiv preprint arXiv:2406.04318},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04318},
    primaryClass={cs.LG cs.AI cs.CV}
}