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Effects of the Training Data Condition on Arterial Spin Labeling Parameter Estimation Using a Simulation-Based
Shota Ishida1, Makoto Isozaki2, Yasuhiro Fujiwara3
1From the Department of Radiological Technology, Faculty of medical sciences, Kyoto College of Medical Science, Kyoto.
Journal of Computer Assisted Tomography
|December 27, 2023
Summary
Optimizing ground truth ranges for training data significantly improves deep neural network (DNN) accuracy in estimating cerebral blood flow (CBF) and arterial transit time (ATT). Appropriate settings ensure precise and reliable estimations from arterial spin labeling signals.
Area of Science:
- Neuroimaging
- Medical Physics
- Artificial Intelligence
Background:
- Deep neural networks (DNNs) show promise for estimating cerebral blood flow (CBF) and arterial transit time (ATT) from arterial spin labeling (ASL) signals.
- The accuracy of these DNNs is highly dependent on the characteristics of the training dataset, particularly the ground truth (GT) ranges used.
Purpose of the Study:
- To investigate the impact of ground truth (GT) ranges for CBF and ATT on the performance of simulation-based supervised DNNs.
- To determine optimal GT ranges for training data to enhance the accuracy and reliability of ASL signal analysis.
Main Methods:
- Trained DNNs using 36 distinct training data patterns derived from ASL signal simulations.
- Evaluated DNN performance using simulation test data (1,000,000 points) and in vivo data from healthy volunteers and a moyamoya patient.
- Assessed accuracy, precision, and noise immunity using metrics like NMAE, NRMSE, and CV Net.
Main Results:
- The highest DNN performance was achieved with GT ranges of 0-120 mL/100 g/min for CBF and 0-4500 ms for ATT.
- While predicted CBF and ATT values varied with GT ranges, appropriate settings maintained DNN accuracy, precision, and noise immunity.
- These findings were consistent in both simulation and in vivo studies.
Conclusions:
- The selection of GT ranges for training data critically influences the performance of simulation-based supervised DNNs for ASL analysis.
- Appropriate GT range settings are essential for achieving accurate and precise estimations of CBF and ATT, whereas inappropriate settings can degrade performance.
