Summary of Adaptive Sampling Of K-space in Magnetic Resonance For Rapid Pathology Prediction, by Chen-yu Yen et al.
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction
by Chen-Yu Yen, Raghav Singhal, Umang Sharma, Rajesh Ranganath, Sumit Chopra, Lerrel Pinto
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Adaptive Sampling for MR (ASMR) method learns an adaptive policy to sequentially select k-space samples, optimizing for target disease detection. This approach bypasses traditional image reconstruction from under-sampled k-space data, accelerating MR scans while maintaining diagnostic performance. ASMR achieves state-of-the-art results on 6 out of 8 pathology classification tasks across Knee, Brain, and Prostate MR scans, using only 8% of the k-space measurements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ASMR is a new way to get better disease detection from MRI scans faster. Right now, MRI scanners take a long time to collect all the information they need. To make it go faster, people try to skip some steps and just use less information. But this can be tricky because you still want good results. The ASMR team figured out how to learn which parts of the scan are most important for finding diseases, so you can get the same level of detection with much less data. This makes MRI scans more useful for tracking diseases in large groups. |
Keywords
» Artificial intelligence » Classification » Tracking
