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Expensive inner-loop solvers in model-based inverse reconstruction can be entirely replaced by a neural network trained on pure Gaussian noise, delivering an 18x speedup without sacrificing analytic precision.
A unified computational framework reveals how efficient computing can dramatically enhance medical image reconstruction across multiple imaging modalities.
