LineScanVel

Analyse line scan images of vessel velocities

Usage

OBJ = LineScanVel(NAME, RAWIMG, CONFIG, ISDS, COLS)

Arguments

• NAME is the name for this LineScanVel object.
• RAWIMG is the RawImg object that will be used to create the LineScanVel object.
• CONFIG contains the configuration parameters needed for the calcVelocity object.
• ISDS specifies whether the streaks to analyse are dark (i.e. negatively labelled) or bright (i.e. positively labelled).
• COLS specifies the left and right columns that will form the edges of the RawImg data to use in the calculation.

Details

LineScanVel objects are used to analyse the velocity from line scan images acquired by scanning along to the vessel axis. Typically, the blood plasma will be labelled by a fluorescent marker, like a dextran conjugated fluorophore (e.g. FITC, as in the figure below), but the method also works with labelled red blood cells (RBCs).

See Also

• LineScanVel class documentation
• ConfigVelocityRadon class documentation
• ConfigVelocityLSPIV class documentation
• CalcVelocityRadon class documentation
• CalcVelocityLSPIV class documentation
• ImgGroup class documentation
• ImgGroup quick start guide

Examples

The following examples require the sample images and other files, which can be downloaded manually, from the University of Zurich website (http://www.pharma.uzh.ch/en/research/functionalimaging/CHIPS.html), or automatically, by running the function utils.download_example_imgs().

Create a LineScanVel object interactively

The following example will illustrate the process of creating a LineScanVel object interactively, starting with calling the constructor.

% Call the LineScanVel constructor
lsv01 = LineScanVel()

Since no RawImg has been specified, the first stage is to select the type of RawImg to create. Press three and then enter to select the SCIM_Tif.

----- What type of RawImg would you like to load? -----

  >> 1) BioFormats
     2) RawImgDummy
     3) SCIM_Tif

Select a format: 3

Then, use the interactive dialogue box to select the raw image file linescanvel_scim.tif, which should be located in the subfolder tests>res, within the CHIPS root directory.

A warning may appear about the pixel aspect ratio, but this is not relevant for LineScanVel images.

Use the interactive dialogue box to select the dummy calibration (calibration_dummy.mat):

The next stage is to define the ‘meaning’ of the image channels. The first channel represents the blood plasma. Press one and then enter to complete the selection.

----- What is shown on channel 1? -----

  >> 0) <blank>
     1) blood_plasma
     2) blood_rbcs

Answer: 1

The next stage is to specify which velocity calculation algorithm should be used. In this case we will choose the Radon transform method. Press two and then enter to complete the selection

----- What type of velocity calculation would you like to use? -----

  >> 1) CalcVelocityLSPIV
     2) CalcVelocityRadon

Select a format: 2

The final stage is to select the left and right limits of the image to use for velocity calculations. This can be useful to exclude the edges where there can be artefacts associated with the scan mirrors changing speed and/or direction.

We have now created a LineScanVel object interactively.

lsv01 =

  LineScanVel with properties:

        plotList: [1x1 struct]
    calcVelocity: [1x1 CalcVelocityRadon]
    colsToUseVel: [17 112]
   isDarkStreaks: 1
           state: 'unprocessed'
            name: 'linescanvel_scim'
          rawImg: [1x1 SCIM_Tif]

The process is almost exactly the same to create an array of LineScanVel objects; when the software prompts you to select one or more raw images, simply select multiple images by using either the shift or control key.

Prepare a RawImg for use in these examples

% Prepare a rawImg for use in these examples
fnRawImg = fullfile(utils.CHIPS_rootdir, 'tests', 'res', ...
    'linescanvel_scim.tif');
channels = struct('blood_plasma', 1);
fnCalibration = fullfile(utils.CHIPS_rootdir, 'tests', 'res', ...
    'calibration_dummy.mat');
calibration = CalibrationPixelSize.load(fnCalibration);
rawImg = SCIM_Tif(fnRawImg, channels, calibration);

Opening linescanvel_scim.tif: 100% [===============================]

Create a LineScanVel object without any interaction

% Create a LineScanVel object without any interaction
nameLSV02 = 'test LSV 02';
configRadon = ConfigVelocityRadon();
isDarkStreaks = [];
colsToUse = [17 112];
lsv02 = LineScanVel(nameLSV02, rawImg, configRadon, ...
    isDarkStreaks, colsToUse)

lsv02 = 
  LineScanVel with properties:

         plotList: [1×1 struct]
     calcVelocity: [1×1 CalcVelocityRadon]
     colsToUseVel: [17 112]
    isDarkStreaks: 1
            state: 'unprocessed'
             name: 'test LSV 02'
           rawImg: [1×1 SCIM_Tif]

Create a LineScanVel object array

% Create the RawImg array first
rawImgArray(1:3) = copy(rawImg);
rawImgArray = copy(rawImgArray)

rawImgArray = 
  1×3 SCIM_Tif array with properties:

    filename
    isDenoised
    isMotionCorrected
    metadata_original
    name
    rawdata
    t0
    metadata

% Then create a LineScanVel object array
lsvArray = LineScanVel('test LSV Array', rawImgArray, configRadon, ...
    isDarkStreaks, colsToUse)

lsvArray = 
  1×3 LineScanVel array with properties:

    plotList
    calcVelocity
    colsToUseVel
    isDarkStreaks
    state
    name
    rawImg

Create a LineScanVel object with a custom config

% Create a LineScanVel object with a custom config
configLSPIV = ConfigVelocityLSPIV('shiftAmt', 8, 'windowTime', 100, ...
    'thresholdSNR', 1.5);
lsv03 = LineScanVel('test LSV 03', rawImg, configLSPIV, ...
    isDarkStreaks, colsToUse);
confVel = lsv03.calcVelocity.config

confVel = 
  ConfigVelocityLSPIV with properties:

      windowTime: 100
        nOverlap: 4
        shiftAmt: 8
    nPixelsToFit: 10
       pointsSNR: 12
    thresholdSNR: 1.5000
    thresholdSTD: 3

Process a scalar LineScanVel object

% Process a scalar LineScanVel object
lsv03 = lsv03.process();

Calculating velocity: 100% [=======================================]

Process a LineScanVel object array (in parallel)

% Process a LineScanVel object array (in parallel).
% This code requires the Parallel Computing Toolbox to run in parallel
useParallel = true;
lsvArray = lsvArray.process(useParallel);
lsvArray_state = {lsvArray.state}

Processing array: 100% [===========================================]
lsvArray_state =
  1×3 cell array
    'processed'    'processed'    'processed'

Plot a figure showing the output

% Plot a figure showing the output
hFig03 = lsv03.plot();
set(hFig03, 'Position', [50, 50, 600, 700])

Produce a GUI to optimise the parameters

% Produce a GUI to optimise the Radon transform parameters
hFigOpt03 = lsv03.opt_config();

Output the data

% Output the data.  This requires write access to the working directory
fnCSV03 = lsv03.output_data('lsv03', 'overwrite', true);
fID03 = fopen(fnCSV03{1}, 'r');
fileContents03 = textscan(fID03, '%s');
fileContents03{1}{1:5}
fclose(fID03);

ans =
    'time,velocity,yPosition,pixelShift,estSNR,maskSNR,maskSTD,mask'
ans =
    '0.050,4.441,NaN,22.739,2.713,FALSE,FALSE,FALSE'
ans =
    '0.075,4.352,NaN,22.285,2.884,FALSE,FALSE,FALSE'
ans =
    '0.100,4.760,NaN,24.371,2.490,FALSE,FALSE,FALSE'
ans =
    '0.125,5.267,NaN,26.966,2.406,FALSE,FALSE,FALSE'

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