Abstract
We implemented the graphics processing unit (GPU) accelerated compressive sensing (CS) non-uniform in k-space spectral domain optical coherence tomography (SD OCT). Kaiser-Bessel (KB) function and Gaussian function are used independently as the convolution kernel in the gridding-based non-uniform fast Fourier transform (NUFFT) algorithm with different oversampling ratios and kernel widths. Our implementation is compared with the GPU-accelerated modified non-uniform discrete Fourier transform (MNUDFT) matrix-based CS SD OCT and the GPU-accelerated fast Fourier transform (FFT)-based CS SD OCT. It was found that our implementation has comparable performance to the GPU-accelerated MNUDFT-based CS SD OCT in terms of image quality while providing more than 5 times speed enhancement. When compared to the GPU-accelerated FFT based-CS SD OCT, it shows smaller background noise and less side lobes while eliminating the need for the cumbersome k-space grid filling and the k-linear calibration procedure. Finally, we demonstrated that by using a conventional desktop computer architecture having three GPUs, real-time B-mode imaging can be obtained in excess of 30 fps for the GPU-accelerated NUFFT based CS SD OCT with frame size 2048(axial) × 1,000(lateral).
Highlights
In the past decade, compressive sensing (CS) [1, 2] has emerged as an effective technique for sampling a signal with smaller number of measurements than that required by the classical Shannon/Nyquist theory
In regular spectral domain optical coherence tomography (SD OCT), the non-uniform discrete Fourier transform (NUDFT) matrix [10] is used to transform the full-length non-linear wavenumber spectral data y to the A-scan image, x
By adding the zero-padding/under-sampling steps to the CS procedure, non-uniform fast Fourier transform (NUFFT) can be used in CS SD OCT with the under-sampled nonlinear wavenumber sampling
Summary
Compressive sensing (CS) [1, 2] has emerged as an effective technique for sampling a signal with smaller number of measurements than that required by the classical Shannon/Nyquist theory. As stated in [9], these two methods have their own limitations: the previous one requires a complicated and time-consuming spectral calibration with numerical interpolation; the latter one demands an inflexible pre-calibration process that needs to be repeated even if a slight change on the sampling rate is desired. It has an upper bound on the under-sampling rate because of the nature of the non-uniformity of the wavenumber in SD OCT. Compared to GPU-FFT-CS, GPU-NUFFT-CS has improved sensitivity roll-off, better signal-to-noise ratio, and less side-lobes while eliminating the need for the cumbersome k-space grid filling and the pre-calibration process.
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