Temporal SNR tSNR

Compute per-voxel temporal SNR using sample SD without detrending; constant series have undefined infinite tSNR and return an error, not a fabricated finite value.

Description

Temporal SNR compares a voxel’s mean signal with its variability over time.

Temporal SNR compares a voxel’s mean signal with its variability over time. The temporal variability includes both noise and physiological or motion-related changes.

The tool returns meanSignals (mean signals); sampleStandardDeviations (sample standard deviations); temporalSnrValues (temporal snr values). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.

When to use Temporal SNR tSNR

Use Temporal SNR tSNR when you already have timeSeriesByVoxel and want meanSignals, sampleStandardDeviations, temporalSnrValues. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.

How to use Temporal SNR tSNR and understand its results

Enter timeSeriesByVoxel using the field table below. Match the units, ordering and data boundaries described in the calculation.
Run the tool after checking the stated bounds and matching paired observations or categories. A malformed request gives a tool error rather than a usable measurement.
Read meanSignals, sampleStandardDeviations, temporalSnrValues. Use the calculation and interpretation below to distinguish amounts, ratios, signs and decision flags where applicable.
Review the supplied parameters and assumptions before using the result in a report or comparing it with another observation.

Input field	Meaning	Value type and bounds	Requirement
timeSeriesByVoxel	Time series by voxel	list of list of number	Required

Use a nonempty matrix with one voxel per row and equal row lengths of at least two. Require nonzero sample SD in every row.

tSNR = mean(time series)/sampleSD(time series)

The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.

json

{
  "input": {
    "timeSeriesByVoxel": [
      [
        9,
        10,
        11
      ],
      [
        4,
        6,
        8
      ]
    ]
  },
  "output": {
    "meanSignals": [
      10,
      6
    ],
    "sampleStandardDeviations": [
      1,
      2
    ],
    "temporalSnrValues": [
      10,
      3
    ]
  }
}

Limitations and assumptions

No detrending, nuisance regression or motion correction is applied. Constant time series fail; negative means are not clipped.

Numerical inputs must be finite. The tool checks its declared input shape and calculation constraints; those checks do not verify the quality, calibration or representativeness of the supplied observations.

Alternative or Complementary methods

SNR From Two Acquisitions: Estimate SNR from matched independent acquisitions using SD(image1−image2)/√2; drift, motion and mismatched anatomy contaminate the noise estimate. MRI Signal to Noise Ratio: Estimate single-channel magnitude MRI SNR using Rayleigh background SD correction; parallel imaging, multicoil noise and spatially varying g factors violate this model.

Methodological background: Measures of scale: sample variance and standard deviation.^[1]

References

Measures of scale: sample variance and standard deviation — NIST/SEMATECH

Similar or alternative tools

SNR From Two Acquisitions
Estimate SNR from matched independent acquisitions using SD(image1−image2)/√2; drift, motion and mismatched anatomy contaminate the noise estimate.
MRI Signal to Noise Ratio
Estimate single-channel magnitude MRI SNR using Rayleigh background SD correction; parallel imaging, multicoil noise and spatially varying g factors violate this model.