gapat 0.0.4

A comprehensive framework of GPU-accelerated image reconstruction for photoacoustic computed tomography

A comprehensive framework of GPU-accelerated image reconstruction for photoacoustic computed tomography

The repository provides code of the paper with the same name as this repository.

A comprehensive framework of GPU-accelerated image reconstruction for photoacoustic computed tomography.

Abstract

Significance: Photoacoustic Computed Tomography (PACT) is a promising non-invasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms.

Aim: In this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (Gpu-Accelerated PhotoAcoustic computed Tomography, GAPAT), helping the research society to grasp this advanced image reconstruction method.

Approach: We leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform.

Results: Notably, our framework can achieve an effective rate of approximately 871 times when reconstructing extremely large-scale 3D PACT images on a dual-GPU platform compared to a 24-core workstation CPU. Besides this manuscript, we shared example codes in the GitHub.

Conclusions: Our approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine.

Keywords: photoacoustic computed tomography, large-scale data size, GPU-accelerated method, Taichi Lang for python, multiple GPU platform.

Documentation

gapat.algorithms

gapat.algorithms.recon(signal_backproj, detector_location, detector_normal, x_range, y_range, z_range, res, vs, fs, delay=0, method="das", device="cpu", num_devices=1, block_dim=512)

Reconstruction of photoacoustic computed tomography.

Warning: When using multi-device reconstruction, the function must be called on the main process.

Parameters

Parameter	Type	Description
signal_backproj	np.ndarray	The input signal. Each row is a signal of a detector. Shape: (num_detectors, num_times). Dtype: np.float32.
detector_location	np.ndarray	The location of the detectors. Each row is the coordinates of a detector. Shape: (num_detectors, 3). Dtype: np.float32.
detector_normal	np.ndarray	The normal of the detectors. Each row is the normal of a detector which points to the volume. Shape: (num_detectors, 3). Dtype: np.float32.
x_range	list	The range of the reconstruction volume. The first is the start x and the second is the end x. Example: [0, 1]. Shape: (2,). Dtype: float.
y_range	list	The range of the reconstruction volume. The first is the start y and the second is the end y. Example: [0, 1]. Shape: (2,). Dtype: float.
z_range	list	The range of the reconstruction volume. The first is the start z and the second is the end z. Example: [0, 1]. Shape: (2,). Dtype: float.
res	float	The resolution of the volume.
vs	float	The speed of sound in the volume.
fs	float	The sampling frequency.
delay	int	The delay of the detectors. Default: 0.
method	str	The method to use. Default: "das". Options: "das", "ubp".
device	str	The device to use. Default: "gpu". Options: "cpu", "gpu".
num_devices	int	The number of devices to use. Default: 1.
block_dim	int	The block dimension. Default: 512.

Returns

Parameter	Type	Description
signal_recon	np.ndarray	The reconstructed signal. Shape: (num_x, num_y, num_z). Dtype: np.float32.

gapat.processings

gapat.processings.bandpass_filter(signal_matrix, fs, band_range, order=2, axis=0)

Bandpass filter the signal matrix.

Parameters

Parameter	Type	Description
signal_matrix	np.ndarray	The signal matrix to be filtered. Shape: (num_detectors, num_times). Dtype: np.float32.
fs	float	The sampling frequency (Hz).
band_range	list	The band range to filter (Hz). The first is the low frequency and the second is the high frequency. Example: [10e6, 100e6]. Shape: (2,). Dtype: float.
order	int	The order of the filter. Default: 2.
axis	int	The axis to filter. Default: 0. (Which will be applied to each detector.)

Returns

Parameter	Type	Description
filtered_signal_matrix	np.ndarray	The filtered signal matrix. Shape: (num_detectors, num_times). Dtype: np.float32.

gapat.processings.negetive_processing(signal_recon, method="zero", axis=0)

Process the negative signal.

Parameters

Parameter	Type	Description
signal_recon	np.ndarray	The reconstructed signal to be processed. Shape: (num_x, num_y, num_z). Dtype: np.float32.
method	str	The method to process the negative signal. Default: "zero". Options: "zero", "abs", "hilbert". "zero": Set the negative signal to zero. "abs": Take the absolute value of the negative signal. "hilbert": Use the hilbert transform to get the envelope of the signal.
axis	int	The axis to process when method is "hilbert". Default: 0.

Returns

Parameter	Type	Description
processed_signal_recon	np.ndarray	The processed signal reconstruction. Shape: (num_x, num_y, num_z). Dtype: np.float32.

gapat.utils

gapat.utils.load_mat(filename)

Load .mat file and return a dictionary with variable names as keys, and loaded matrices as values.

Parameters

Parameter	Type	Description
filename	str	The path to the .mat file.

Returns

Parameter	Type	Description
data	dict	A dictionary with variable names as keys, and loaded matrices as values.

gapat.utils.save_mat(filename, varname, data)

Save data to .mat file with the given variable name.

Parameters

Parameter	Type	Description
filename	str	The path to the .mat file.
varname	str	The variable name to save the data to.
data	np.ndarray	The data to save.