Patient-specific hyperparameter learning for optimization-based CT image reconstruction
| Autor: | Jingyan Xu, Frédéric Noo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Hyperparameter
Radiological and Ultrasound Technology Computer science business.industry X-Rays ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Parameterized complexity Pattern recognition Iterative reconstruction Patient specific Convolutional neural network Article Dynamic programming Image Processing Computer-Assisted Learning methods Humans Radiology Nuclear Medicine and imaging Artificial intelligence Neural Networks Computer Focus (optics) business Tomography X-Ray Computed |
| Zdroj: | Phys Med Biol |
| Popis: | We propose a hyperparameter learning framework that learns patient-specific hyperparameters for optimization based image reconstruction problems for x-ray CT applications. The framework consists of two functional modules: (1) a hyperparameter learning module parameterized by a convolutional neural network, (2) an image reconstruction module that takes as inputs both the noisy sinogram and the hyperparameters from (1) and generates the reconstructed images. As a proof-of-concept study, in this work we focus on a subclass of optimization based image reconstruction problems with exactly computable solutions so that the whole network can be trained end-to end in an efficient manner. Unlike existing hyperparameter learning methods, our proposed framework generates patient-specific hyperparameters from the sinogram of the same patient. Numerical studies demonstrate the effectiveness of our proposed approach compared to bi-level optimization. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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