  Accurate and reliable Magnetic Resonance Imaging (MRI) analysis is
particularly important for adaptive radiotherapy, a recent medical advance
capable of improving cancer diagnosis and treatment. Recent studies have shown
that IVIM-NET, a deep neural network (DNN), can achieve high accuracy in MRI
analysis, indicating the potential of deep learning to enhance diagnostic
capabilities in healthcare. However, IVIM-NET does not provide calibrated
uncertainty information needed for reliable and trustworthy predictions in
healthcare. Moreover, the expensive computation and memory demands of IVIM-NET
reduce hardware performance, hindering widespread adoption in realistic
scenarios. To address these challenges, this paper proposes an
algorithm-hardware co-optimization flow for high-performance and reliable MRI
analysis. At the algorithm level, a transformation design flow is introduced to
convert IVIM-NET to a mask-based Bayesian Neural Network (BayesNN),
facilitating reliable and efficient uncertainty estimation. At the hardware
level, we propose an FPGA-based accelerator with several hardware
optimizations, such as mask-zero skipping and operation reordering.
Experimental results demonstrate that our co-design approach can satisfy the
uncertainty requirements of MRI analysis, while achieving 7.5 times and 32.5
times speedup on an Xilinx VU13P FPGA compared to GPU and CPU implementations
with reduced power consumption.
