Deep learning for undersampled MRI reconstruction

Chang Min Hyun, Hwa Pyung Kim, Sung Min Lee, Sungchul Lee, Jin Keun Seo

Physics in Medicine and Biology 2018 article cited by 429

Source Open-access PDF More in Medicine & Health

importance 6/10 solved Advanced MRI Techniques and Applications medical imaging stage: research

Problem

Standard MRI is slow because it requires time-consuming full k-space data acquisition.

Why it matters

Faster MRI scanning reduces acquisition time, benefiting patients and clinical efficiency.

What the AI did

A deep learning network trained on input-output pairs of Fourier-transformed subsampled and fully sampled k-space data reconstructs high-quality images from undersampled MRI data.

Results

Only 29% of k-space data generated images of high quality as effectively as standard fully sampled MRI reconstruction.

Impact

Peer-reviewed experimental demonstration that deep learning can match standard MRI quality with 29% of the data.

Record

Venue	Physics in Medicine and Biology
Published	2018
Work type	article
Authors	Chang Min Hyun, Hwa Pyung Kim, Sung Min Lee, Sungchul Lee, Jin Keun Seo
Citations	429 (OpenAlex, at import time)
References	14
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1088/1361-6560/aac71a
Open access	hybrid
Keywords	MRI, deep learning, undersampled reconstruction, k-space, medical imaging

Importance rating

6 / 10

significant — thousands to millions affected

Faster MRI via 29% k-space sampling could reduce acquisition time, benefiting patients and clinical efficiency; no scale of affected patients stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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