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Abstract #4819

SigPy: A Python Package for High Performance Iterative Reconstruction

Frank Ong1 and Michael Lustig1

1University of California, Berkeley, Berkeley, CA, United States

We present SigPy, a Python package designed for high performance iterative reconstruction. Its main features include:

- A unified CPU and GPU Python interface to signal processing functions, including convolution, FFT, NUFFT, wavelet transform, and thresholding functions.

- Convenient classes (Linop, Prox, Alg, App) to build more complicated iterative reconstruction algorithms.

- Commonly used MRI reconstruction methods as Apps, including SENSE, L1-wavelet regularized reconstruction, total-variation regularized reconstruction, and JSENSE.

- MRI-specific functions, including poisson-disc sampling, ESPIRiT calibration, and non-Cartesian preconditioners.

- Simple installation via pip and conda.

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