Mr. Vinod Amilneni Profile

Vinod Amilneni

at GE Healthcare

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Self-supervised learning for CT deconvolution

Prasad Sudhakar, Rajesh Langoju, Utkarsh Agrawal, Bhushan D. Patil, Ajay Narayanan, Vinay Chaugule, Vinod Amilneni, Paul Cheerankal, Bipul Das

Proceedings Volume 11595, 115953Z (2021) https://doi.org/10.1117/12.2581269

KEYWORDS: Deconvolution, Visual process modeling, Model-based design, X-ray computed tomography, Sensors, Physics, X-rays, X-ray detectors, Scanners, Raman spectroscopy

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Images produced by CT systems with larger detector pixels often suffer from lower z resolution due to their wider slice sensitivity profile (SSP). Reducing the effect of SSP blur will result in resolution of finer structures and enables better clinical diagnosis. Hardware solutions such as dicing the detector cells smaller or dynami- cally deflecting the X-ray focal spot do exist to improve the resolution, but they are expensive. In the past, algorithmic solutions like deconvolution techniques also have been used to reduce the SSP blur. These model- based approaches are iterative in nature and are time consuming. Recently, supervised data-driven deep learning methods have become popular in computer vision for deblurring/deconvolution applications. Though most of these methods need corresponding pairs of blurred (LR) and sharp (HR) images, they are non-iterative during inference and hence are computationally efficient. However, unlike the model-based approaches, these methods do not explicitly model the physics of degradation. In this work, we propose Resolution Amelioration using Machine Adaptive Network (RAMAN), a self-supervised deep learning framework, that explicitly uses best of both learning and model based approaches. The framework explicitly accounts for the physics of degradation and appropriately regularizes the learning process. Also, in contrary to supervised deblurring methods that need paired LR and HR images, the RAMAN framework requires only LR images and SSP information for training, making it self-supervised. Validation of proposed framework with images obtained from larger detector systems shows marked improvement in image sharpness while maintaining HU integrity.

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