OCT imaging reconstruction on spectral-domain optical coherence tomography
PyOCT: Imaging Reconstruction for Spectral-Domain Optical Coherence Tomography
PyOCT is developed to conduct normal spectral-domain optical coherence tomography (SD-OCT) imaging reconstruction with main steps as:
- Reading Data
- Background Subtraction
- Spectral Resampling
- Comutational Aberration Correction (Alpha-correction)
- Camera Dispersion Correction (Beta-correction with camera calibration factors)
- Inverse Fourier Transform
- Obtain OCT Image
The algorithms was developed initially in Prof. Steven G. Adie research lab at Cornell University using MATLAB. The reconstruction speed has been improved with matrix-operation. Compared with MATLAB, Python language have a much better performance in loading data from binary files tested only in our lab computer. Currently, PyOCT only supports python 3.0+.
PyOCT can be install using pip:
$pip install PyOCT
If you want to run the latest version of the code, you can install from git:
$python -m pip install -U git+git://github.com/NeversayEverLin/PyOCT.git
After successful installaiton, you can test program under python environment:
$from PyOCT import VolumeReconstruction $VolumeReconstruction.Run_test()
To run the OCT imaging reconstruction, you can construct class OCTImagingProcessing() from PyOCTRecon module:
$from PyOCT import PyOCTRecon $OCTImage = PyOCTRecon.OCTImagingProcessing()
Class OCTImagingProcessing require at least 3 positional arguments. All input parameters are:
- root_dir: required, root directory path where OCT raw data located, ENDING WITHOUT /; e.g., root_dir = 'D:/cuda_practice/OCT_Recon/OCTdata'.
- SampleData: optional, sample data, most of time won't need.
- Settings: optional, Settings, most time won't need.
- sampleID: required, sample data file name. ENDING WITHOUT _raw.bin; e.g., sampleID = 'OCT_100mV_2'.
- bkgndID: required, background data file name, ending with _raw.bin; e.g., bkgndID = 'bkgnd_512_0_raw.bin'.
- Sample_sub_path: optional, default as None; sub-directory where OCT raw data located. ENDING WITHOUT /.
- Bkgnd_sub_path: optional, default as None; sub-directory where OCT bkgnd data located. ENDING WITHOUT /.
- saveOption: optional, bool, default as False.
- saveFolder: optional, name for folder to save data; default as None, which will save data in root directory.
- RorC: optional,"real" or "complex", tell to save data or show data in complex format or single precison (float32) format.
- verbose: optional, bool, default as True. If True, the data processing will show processing information during each step.
- frames: optional, int, number of frames to read and process, defaults as 1.
- alpha2, alpha3: optional, parameters for computational dispersion correction.
- depths: optional, nuumpy.linspace() created array, depths to be processed, default as: np.linspace(1024,2047,1024), indicating procesing 1024th z-pixel to 2048-pixel.
- gamma: optional, power factor to do plotting, default as 0.4.
- wavelength: optional, nominal central wavelength of OCT laser source in unit of nm, default as 1300.
- XYConversion: optional, 2 elements numpy array, calibration factor for galvo-scanning voltage to scanning field of view in x and y axis at unit of um/V, default as [660,660].
- camera_matrix: optional, camera dispersion correction factor, numpy array as [c0,c1,c2,c3]; default as np.asarray([1.169980E3,1.310930E-1,-3.823410E-6,-7.178150E-10]) for 1300nm system.
- start_frame: which frame to start reading and being processed. default is 1, indicating starting from first frame.
- OptimizingDC: [Required Further Developement] optional, bool, optimizing dispersion correction to search optimized alpha2 and alpha3. default as False.
- singlePrecision: only workable when RorC = 'real', Default as True, data will be converted into numpy.float32 single precision data type.
- ReconstructionMethods: 'cao' or 'nocao', default as 'NoCAO'. using bkgnd data as real time background estimation from signal dataset ("CAO") or directly from bkgnd file ("NoCAO")
Another class in PyOCT is Batch_OCTProcessing(), which using data processing provided by class OCTImagingProcessing() with additional inputs as:
- Ascans: number of Ascans.
- Frames: number of frames.
- ChunkSize: number of frames at each sub-segmentation dataset.
Batch_OCTProcessing() should be used when dataset is too large to be directly processed by whole volume which might exhaust your RAM/CPU. It will automatically segmented dataset into sub-segmentation dataset to be processed. The processed volume data and settings could be accessed by Batch_OCTProcessing.data or Batch_OCTProcessing.OCTData and Batch_OCTProcessing.Settings. You can still access to basic OCTImagingProcessing methods by accessing to methods like Batch_OCTProcessing.OCTRe.ShowXZ().
Class OCTImagingProcessing also provides several accesses/members to imaging processing data:
- self.root_dir: root directory of data set
- self.sampleID: sample ID
- self.bkgndID: background ID
- self.Settings: parameters of settings of reconstruction
- self.OCTData: single precision OCT intensity data
- self.data: complex OCT reconstruction data, only accessible when datatype is not "real".
- self.InterferencePattern: interference fringes of OCT imaging
- self.DepthProfile: depth profile (along z-axis) of reconstructed image
- self.ShowXZ(OCTData): member function to show cross-section.
Example dataset could be download under the request to email address: firstname.lastname@example.org
PyOCT is licensed under the terms of the MIT License (see the file LICENSE).# PyOCT
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