Multiobjective Optimization of FPGA-Based Medical Image Registration
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Citations
A High Throughput FPGA-Based Floating Point Conjugate Gradient Implementation for Dense Matrices
A Framework for Customizable FPGA-based Image Registration Accelerators
System and methods for dynamic management of hardware resources
Multiobjective Optimization for Reconfigurable Implementation of Medical Image Registration
References
Evolutionary computation: comments on the history and current state
A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems
Multi-modality image registration by maximization of mutual information
Multi-modality image registration by maximization of mutual information
Bidwidth analysis with application to silicon compilation
Related Papers (5)
Frequently Asked Questions (8)
Q2. What is the trend in real-time signal processing systems?
An emerging trend in real-time signal processing systems is to accelerate computationally intensive algorithmic components by mapping them to custom or reconfigurable hardware platforms, such as applicationspecific integrated circuits (ASICs) and fieldprogrammable gate arrays (FPGAs).
Q3. What is the first step in the calculation of a voxel?
The initial step in MI calculation involves applying a candidate transformation (T), to each voxel coordinate ( rv ) in the RI to find the corresponding voxel coordinates in the FI ( fv ).
Q4. What other heuristic techniques are limited to software implementations?
Other heuristic techniques that take into account tradeoffs between hardware cost and implementation error and enable automatic conversion from floating-point to fixed-point representations are limited to software implementations only [26].
Q5. What is the MI value of the FPGA-based architecture?
The authors have developed a parameterized, bit-true emulation of the FPGA-based architecture that is capable of calculating the MI valuecorresponding to any feasible configuration for a given image transformation.
Q6. What is the importance of comparing the Pareto-optimized solution sets?
Quantitative comparison of the Pareto-optimized solution sets is essential in order to compare more precisely the effectiveness of various search methods.
Q7. What is the effect of the MI calculation on registration accuracy?
These factors, along with its execution time, in their experience, may render registration accuracy as an unsuitable objective function, especially if there is nonmonotonic behavior with respect to the wordlength of design variables.
Q8. How many accesses to MH memory for each RI voxel?
Because the MH must be updated (read–modify–write) at these eight locations, this amounts to 16 accesses to MH memory for each RI voxel.