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attenComp

Attenuation compensation using time-variant filtering

Syntax

signal = attenComp(signal, dt, c, alpha_0, y)
signal = attenComp(signal, dt, c, alpha_0, y, ...)
[signal, tfd, cutoff_freq] = attenComp(signal, dt, c, alpha_0, y)
[signal, tfd, cutoff_freq] = attenComp(signal, dt, c, alpha_0, y, ...)

Description

attenComp corrects for frequency dependent acoustic attenuation in photoacoustic signals using time-variant filtering [1]. The time-variant filter is constructed to correct for acoustic attenuation and dispersion following a frequency power law of the form alpha_0*f^y under the assumption the distribution of attenuation parameters is homogeneous. The filter is applied directly to the recorded time-domain signals using a form of non-stationary convolution. The approach is computationally efficient and can be used with any detector geometry or reconstruction algorithm.

To prevent high-frequency noise from being amplified, the compensation is regularised using a Tukey window with a time-variant cutoff frequency. The cutoff frequency can be specified manually using the optional input 'FilterCutoff'. This is set as a two-element vector corresponding to the cutoff frequency in Hz for the first and last time points, respectively. For a fixed cutoff, these should be specified as the same value, e.g., [3e6, 3e6]. Alternatively, if 'FilterCutoff' is set to 'auto' (the default), the cutoff frequency is chosen based on the local time-frequency distribution of the recorded signals using the following steps:

  1. Compute the average time-frequency distribution of the input signals (the method can be defined using the optional input 'Distribution')
  2. Threshold the time-frequency distribution to remove noise (the threshold value can be defined using the optional input 'NoiseThreshold')
  3. Calculate the integral of the thresholded time-frequency distribution at each time point using cumsum
  4. Find the cutoff frequency at each time point where the integral reaches a given percentage of the maximum value (this percentage can be defined using the optional input 'EnergyThreshold')
  5. Increase the filter cutoff frequency by a fixed multiplier so the cutoff corresponds to the edge of the passband for the Tukey window (the multiplier can be defined using the optional input 'FrequencyMultiplier')
  6. Smooth the variation of the cutoff frequency over time (the smoothing function can be defined using the optional input 'FitType')
  7. Threshold any values of the cutoff frequency below zero or above the Nyquist limit

If the input contains a matrix of signals, the cutoff frequency is based on the average time frequency distribution. To calculate the cutoff frequency for each signal individually, this function should be called in a loop. This can be parallelised, for example, using parfor from the parallel computing toolbox. For further details about this function and attenuation compensation using time variant filtering, see the reference below.

[1] B. E. Treeby (2013) "Acoustic attenuation compensation in photoacoustic tomography using time-variant filtering," J. Biomed. Opt., vol. 18, no. 3, p.036008.

Inputs

signal

matrix of time series to compensate indexed as (sensor_index, time_index)

dt

time step [s]

c

sound speed [m/s]

alpha_0

power law absorption prefactor [dB/(MHz^y cm)]

y

power law absorption exponent [0 < y < 3, y ~= 1]

Optional Inputs

Optional 'string', value pairs that may be used to modify the default computational settings.

Input Valid Settings Default Description

'DisplayUpdates'

(Boolean scalar)

true

Boolean controlling whether command line updates and compute time are printed to the command line.

'Distribution'

'Rihaczek'
'Wigner'

'Rihaczek'

Time-frequency distribution used to automatically compute the filter cutoff frequency if 'FilterCutoff' is set to 'auto'. Note, the option 'Wigner' requires the Discrete TFD toolbox from http://tfd.sourceforge.net.

'EnergyThreshold'

(numeric scalar)

0.98

Threshold value given as a percentage of the total amplitude spectrum used to choose the filter cutoff frequency at each time point.

'FilterCutoff'

(numeric two element vector) or
'auto'

'auto'

Option to manually define the cutoff frequencies for a linear variation in the filter cutoff instead of using an automatic search.

'FitType'

'spline'
'linear'
'mav'

'spline'

Fitting type used to smooth the filter cutoff frequency after an automatic search, where 'spline' fits a smoothed spline, 'linear' fits a linear line, and 'mav' computes the moving average.

'FrequencyMultiplier'

(numeric scalar)

2

By default, the compensation is regularised using a Tukey window with a time-variant cutoff frequency. The default Tukey window has a taper ratio of 0.5, so the filter cutoff frequency found by the automatic search is increased by a frequency multiplier so that the filter cutoff frequency corresponds to the edge of the passband of the Tukey window.

'NumSplines'

(numeric scalar)

40

Number of spline segments used in the smoothing spline if 'FitType' is set to 'spline'.

'NoiseThreshold'

(numeric scalar)

0.03

Threshold value given as a percentage of the signal maximum used to threshold the TFD before the automatic search for the filter cutoff.

'Plot'

(Boolean scalar)

false

Boolean controlling whether a plot of the time frequency distribution and filter cutoff frequency are displayed.

'PlotRange'

(numeric two element vector) or
'auto'
'full'

'auto'

Option to manually set the plot range in the frequency axis when 'Plot' is set to true. This can be manually specified, or set to 'auto' (1.5 x the maximum filter cutoff frequency) or 'full' (maximum supported frequency range).

'TaperRatio'

(numeric scalar)

0.5

Taper ratio used to construct the Tukey Windows.

'T0'

(numeric scalar)

0

Time index of T0 in the input signals. For photoacoustic imaging, T0 corresponds to the arrival of the excitation laser pulse at the sample.

Outputs

signal_comp

time series after attenuation compensation

tfd

average time frequency distribution of the input signals

cutoff_freq

filter cutoff frequency for each time index

Examples

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© 2009-2014 Bradley Treeby and Ben Cox.