Abstract
Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.
| Original language | English (US) |
|---|---|
| Journal | Magnetic Resonance Imaging |
| DOIs | |
| State | Published - Jan 1 2019 |
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Keywords
- Contrast harmonization
- Deep learning
- Magnetic resonance imaging
ASJC Scopus subject areas
- Biophysics
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
DeepHarmony : A deep learning approach to contrast harmonization across scanner changes. / Dewey, Blake E.; Zhao, Can; Reinhold, Jacob C.; Carass, A.; Fitzgerald, Kathryn C.; Sotirchos, Elias S.; Saidha, Shiv; Oh, J.; Pham, Dzung L.; Calabresi, Peter A.; van Zijl, Peter C.M.; Prince, Jerry L.
In: Magnetic Resonance Imaging, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - DeepHarmony
T2 - A deep learning approach to contrast harmonization across scanner changes
AU - Dewey, Blake E.
AU - Zhao, Can
AU - Reinhold, Jacob C.
AU - Carass, A.
AU - Fitzgerald, Kathryn C.
AU - Sotirchos, Elias S.
AU - Saidha, Shiv
AU - Oh, J.
AU - Pham, Dzung L.
AU - Calabresi, Peter A.
AU - van Zijl, Peter C.M.
AU - Prince, Jerry L.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.
AB - Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.
KW - Contrast harmonization
KW - Deep learning
KW - Magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=85069945285&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069945285&partnerID=8YFLogxK
U2 - 10.1016/j.mri.2019.05.041
DO - 10.1016/j.mri.2019.05.041
M3 - Article
C2 - 31301354
AN - SCOPUS:85069945285
JO - Magnetic Resonance Imaging
JF - Magnetic Resonance Imaging
SN - 0730-725X
ER -