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Published on: April 14, 2014
Deep Learning-Based Enhancement of Already Diagnostic-Quality MRI for Alzheimer's Disease Classification: Effects on
Zheng Zhang1,2, Zechen Zhou1, Lei Xiang1
1Subtle Medical Inc., Menlo Park, California, USA.
Background:
Deep learning (DL)-based image enhancement is widely used to improve suboptimal medical imaging. Whether it also benefits diagnostic-quality MRI in downstream task performance and data-efficiency remains unclear.
Purpose:
To investigate the impact of DL-based enhancement applied to diagnostic quality structural MRI for Alzheimer's disease (AD) classification.
Study Type:
Retrospective.
Population:
A total of 2293 brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were split into training (n = 1605), validation (n = 229), and internal test (n = 459) sets. Two hundred and seventy scans from the National Alzheimer's Coordinating Center (NACC) served as an external test set.
Field Strength/Sequence:
1.5 T; 3D T1-weighted gradient-echo.
Assessment:
Each scan was enhanced by SubtleHD (SHD), an FDA-cleared DL-based MR enhancement tool. ResNet34 and DenseNet121 were trained on standard-of-care (SOC) and SHD-enhanced images to classify subjects as cognitively normal, mild cognitive impairment, or AD and evaluated by accuracy and macro-area under the receiver operating characteristic curve (macro-AUC). Data efficiency was assessed by retraining on stratified training subsets (50%-100%).
Statistical Tests:
McNemar test for accuracy and DeLong test for macro-AUC in three-class one-versus-rest setting (p < 0.05).
Results:
SHD enhancement increased ResNet34 accuracy from 85.2% to 88.7% and macro-AUC from 0.951 to 0.968 (both significant), and DenseNet121 accuracy from 90.2% to 92.2% (p = 0.18) and macro-AUC from 0.978 to 0.982 (p = 0.29). Models trained on 70% of SHD-enhanced dataset matched those trained on the full SOC dataset (accuracy: 85.9%, macro-AUC: 0.942), indicating improved data efficiency with enhancement. In NACC, the SOC-trained model achieved accuracy of 49.2% and macro-AUC of 0.679 versus 63.0% and 0.819 for the SHD-trained model (both significant); the SHD-trained model retained an advantage on unenhanced NACC images (macro-AUC: 0.772).
Data Conclusion:
DL-based enhancement of diagnostic-quality MRI improves downstream Alzheimer's disease classification performance and reduces the amount of training data required. This suggests that conventional definitions of image quality may underestimate the information content available for machine learning.
Evidence Level:
3.
Technical Efficacy Stage:
2.
