Productively accelerating positron emission tomography image reconstruction on graphics processing units with Julia. (May 2022) - Van Gendt, Michiel; Besard, Tim; Vandenberghe, Stefaan; De Sutter, Bjorn
Research in medical imaging is hampered by a lack of programming languages that support productive, flexible programming as well as high performance. In search for higher quality imaging, researchers can ideally experiment with novel algorithms using rapid-prototyping languages such as Python. However, to speed up image reconstruction, computational resources such as those of graphics processing units (GPUs) need to be used efficiently. Doing so requires re-programming the algorithms in lower-level programming languages such as CUDA C/C++ or rephrasing them in terms of existing implementations of established algorithms in libraries. The former has a detrimental impact on research productivity and requires system-level programming expertise, and the latter puts severe constraints on the flexibility to research novel algorithms. Here, we investigate the use of the Julia scientific programming language in the domain of PET image reconstruction as a means to obtain both high performance (portability) on GPUs and high programmer productivity and flexibility, all at once, without requiring expert GPU programming knowledge. Using rapid-prototyping features of Julia, we developed basic and performance-optimized GPU implementations of baseline maximum likelihood expectation maximization (MLEM) positron emission tomography (PET) image reconstruction algorithms, as well as multiple existing algorithmic extensions. Thus, we mimic the effort that researchers would have to invest to evaluate the quality and performance potential of algorithms. We evaluate the obtained performance and compare it to state-of-the-art existing implementations. We also analyse and compare the required programming effort. With the Julia implementations, performance in line with existing GPU implementations written in the low-level, unproductive programming language CUDA C is achieved, while requiring much less programming effort, even less than what is needed for much less performant CPU implementations in C++. Switching to Julia as the programming language of choice can therefore boost the productivity of research into medical imaging and deliver excellent performance at a low cost in terms of programming effort.