"""
"""
Facebook AI Research (FAIR) and NYU School of Medicine’s Center for Advanced Imaging Innovation and Research (CAI²R) are sharing new open source tools and data as part of fastMRI, a joint research project to spur development of AI systems to speed MRI scans by up to 10x. Today’s releases include new [U-net] AI models and baselines for this task. It also includes the first large-scale MRI data set of its kind, which can serve as a benchmark for future research. [...] 1.5 million MR images drawn from 10,000 scans, as well as raw measurement data from nearly 1,600 scans.

By sharing a standardized set of AI tools and MRI data, as well as hosting a leaderboard where research teams can compare their results, we aim to help improve diagnostic imaging technology, and eventually increase patients’ access to a powerful and sometimes life-saving technology. With new AI techniques, we hope to generate scans that require much less measurement data to produce the image detail necessary for accurate detection of abnormalities.
[...]
In the more than four decades since medical MR imaging was introduced, researchers have tried consistently to shorten the technology’s long scan times, which can sometimes require patients to remain stationary for more than an hour. [...] MRI devices collect a series of individual 2D spatial measurements — known as k-space data in the medical imaging community — and convert them into various images. By training neural networks on a large amounts of k-space data, this image reconstruction technique allows for less detailed initial scans,
[...]
The ultimate goal of the fastMRI project is to use AI-driven image reconstruction to achieve up to a 10x reduction in scan times. To begin, we’re providing baseline models for ML-based image reconstruction from k-space data subsampled at 4x and 8x scan accelerations. And we’ve already seen promising preliminary results for accelerating MR imaging by up to four times.
[...]
The raw measurement data in this data set sets it apart from previous MR databases and could prove particularly valuable for researchers. It consists of the k-space data that’s collected during scanning and typically discarded after it’s used to generate images. [...] The fastMRI data set also includes undersampled versions of those measurements, with k-space lines retrospectively masked, to simulate partial-data scans.

The k-space data in this data set is drawn from MR devices with multiple magnetic coils, and it also includes data that simulates measurements from single-coil machines. Though research related to accelerating multi-coil scans is more relevant for clinical practice — they’re more precise and more common than single-coil scans — the inclusion of single-coil k-space data measurements offers AI researchers an entry point for applying ML to this imaging task. [...] we focused on two tasks — single-coil reconstruction and multi-coil reconstruction. [...] In both the single-coil and multi-coil deep learning baselines, our models are based on u-nets [...] Our initial work has indicated that the u-net architecture is particularly responsive to training on a large amount of data, such as the MR reconstruction data set released by NYU School of Medicine. [...] NYU School of Medicine plans to release additional images and measurements in the near future, related to brain and liver scans (the current data set consists of knee scans).
"""
https://code.fb.com/ai-research/fastmri/
https://code.fb.com/ai-research/fastmri/

Labels: Oleg Zabluda

- posted by Oleg Zabluda @ 11:55 AM | permalink | | | technorati | bloglines | feedster