Dataset_phantom-intra40.mat: Two-Layer Flow Phantom DCS Measurements

1540 autocorrelation functions collected at three source-detector separations using a two-layer flow phantom for diffuse correlation spectroscopy. The data includes preset and measured flow rates and estimated Blood Flow Index values, supporting the research published in Biomedical Optics Express in 2023. The dataset is provided in MATLAB .mat format with matrices for flow rates, BFIs, and averaged autocorrelation waveforms.

Use Cases

• Training deep learning models for layer-separated blood flow estimation based on autocorrelation functions.
• Benchmarking homogeneous semi-infinite plate models against measured phantom flow rates.
• Analyzing the relationship between source-detector separation and estimated Blood Flow Index (BFI).
• Validating diffuse correlation spectroscopy (DCS) instrumentation and analysis methods using controlled phantom data.

Strengths

• Contains 1540 distinct measurement instances, each representing one minute of averaged data.
• Each instance includes three autocorrelation functions from different source-detector separations (1, 2, 3 cm).
• Provides both preset pump flow rates and physically measured flow rates at the phantom outlet for validation.
• Includes Blood Flow Index (BFI) estimates calculated using a standard homogeneous model for comparison.

Limitations

• Column-level documentation is absent; field semantics must be inferred from the high-level description after download.
• Row count is unknown for the primary data matrices, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Research data from the paper 'Deep-learning-based separation of shallow and deep layer blood flow rates in diffuse correlation spectroscopy' by Nakabayashi et al. (2023).

Collection Method

Measurements made using a two-layer flow phantom with diffuse correlation spectroscopy (DCS).