TITLE: SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation 

ABSTRACT: Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging.
However, long image acquisition times, the need for qualitative expert
analysis, and the lack of (and difficulty extracting) quantitative indicators
that are sensitive to tissue health have curtailed widespread clinical and
research studies. While recent machine learning methods for MRI reconstruction
and analysis have shown promise for reducing this burden, these techniques are
primarily validated with imperfect image quality metrics, which are discordant
with clinically-relevant measures that ultimately hamper clinical deployment
and clinician trust. To mitigate this challenge, we present the Stanford Knee
MRI with Multi-Task Evaluation (SKM-TEA) dataset, a collection of quantitative
knee MRI (qMRI) scans that enables end-to-end, clinically-relevant evaluation
of MRI reconstruction and analysis tools. This 1.6TB dataset consists of
raw-data measurements of ~25,000 slices (155 patients) of anonymized patient
MRI scans, the corresponding scanner-generated DICOM images, manual
segmentations of four tissues, and bounding box annotations for sixteen
clinically relevant pathologies. We provide a framework for using qMRI
parameter maps, along with image reconstructions and dense image labels, for
measuring the quality of qMRI biomarker estimates extracted from MRI
reconstruction, segmentation, and detection techniques. Finally, we use this
framework to benchmark state-of-the-art baselines on this dataset. We hope our
SKM-TEA dataset and code can enable a broad spectrum of research for modular
image reconstruction and image analysis in a clinically informed manner.
Dataset access, code, and benchmarks are available at
https://github.com/StanfordMIMI/skm-tea.
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging.
However, long image acquisition times, the need for qualitative expert
analysis, and the lack of (and difficulty extracting) quantitative indicators
that are sensitive to tissue health have curtailed widespread clinical and
research studies. While recent machine learning methods for MRI reconstruction
and analysis have shown promise for reducing this burden, these techniques are
primarily validated with imperfect image quality metrics, which are discordant
with clinically-relevant measures that ultimately hamper clinical deployment
and clinician trust. To mitigate this challenge, we present the Stanford Knee
MRI with Multi-Task Evaluation (SKM-TEA) dataset, a collection of quantitative
knee MRI (qMRI) scans that enables end-to-end, clinically-relevant evaluation
of MRI reconstruction and analysis tools. This 1.6TB dataset consists of
raw-data measurements of ~25,000 slices (155 patients) of anonymized patient
MRI scans, the corresponding scanner-generated DICOM images, manual
segmentations of four tissues, and bounding box annotations for sixteen
clinically relevant pathologies. We provide a framework for using qMRI
parameter maps, along with image reconstructions and dense image labels, for
measuring the quality of qMRI biomarker estimates extracted from MRI
reconstruction, segmentation, and detection techniques. Finally, we use this
framework to benchmark state-of-the-art baselines on this dataset. We hope our
SKM-TEA dataset and code can enable a broad spectrum of research for modular
image reconstruction and image analysis in a clinically informed manner.
Dataset access, code, and benchmarks are available at
https://github.com/StanfordMIMI/skm-tea.
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation
http://arxiv.org/abs/2203.06823v1