Computer hardware

Algorithm

Reconfigurable computing

Statistics

Hardware description language

Embedded system

Verilog

Computation

Parallel computing

VHDL

Computer science

Mathematical optimization

Hardware acceleration

Software

Virtex

Mathematics

Expectation–maximization algorithm

Programming language

Maximum likelihood

Field-programmable gate array

Maximization

Hardware architecture

IEEE Access

The major requirements of a good tomographic reconstruction algorithm are a reduction in radiation dosage, accurate reconstruction, detail enhancement, and rapid reconstruction time. Some of these factors are covered by many algorithms but are not collectively addressed in one. While the Maximum Likelihood Expectation Maximization (MLEM) algorithm fares well on many of these factors, it is difficult to apply this algorithm in real-time due to its long execution time. Our predetermined goal is to reduce the execution time to a large extent so that the MLEM's advantages can be leveraged by using hardware accelerators such as Field Programmable Gate Arrays (FPGA). The FPGAs are becoming especially popular as hardware accelerators and are well known for their programmability, configurability, and massive parallelism through a large number of Configurable Logic Blocks (CLBs). Although FPGAs are extremely versatile, they require complex languages like Verilog or VHDL to program them, incorporating changes in the design level at a later stage in FPGAs demands increased effort. Here, in this paper, for the first time, we present a parallel structure for hardware acceleration of the MLEM on the mammoth Virtex 7 $VC$ 709 FPGA. Using available tools, we also present a programming flow to design the algorithmic acceleration hardware architecture. The proposed flow does not require prior knowledge of the traditionally cumbersome Hardware Description Languages (HDLs) and this makes post design changes very easy to incorporate and validate. The parallel architecture is implemented on an FPGA operating at 220 MHz and we have achieved a $288\times $ performance compared to an optimized software execution on an Intel Xeon workstation with 12-cores 3.1 GHz 32 GB RAM and 12 MB Cache architecture.

FPGA as a Hardware Accelerator for Computation Intensive Maximum Likelihood Expectation Maximization Medical Image Reconstruction Algorithm