تقرير
A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography
| العنوان: | A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography |
|---|---|
| المؤلفون: | Mendoza, Rey, Nguyen, Minh, Zhu, Judith Weng, Dumont, Vincent, Perciano, Talita, Mueller, Juliane, Ganapati, Vidya |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Physics - Data Analysis, Statistics and Probability |
| الوصف: | Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an object at different rotations relative to the beam. By imaging 2-dimensional projections, a 3-dimensional object can be reconstructed through a computational algorithm. Imaging at a greater number of rotation angles allows for improved reconstruction. However, taking more measurements increases the x-ray dose and may cause sample damage. Deep neural networks have been used to transform sparse 2-D projection measurements to a 3-D reconstruction by training on a dataset of known similar objects. However, obtaining high-quality object reconstructions for the training dataset requires high x-ray dose measurements that can destroy or alter the specimen before imaging is complete. This becomes a chicken-and-egg problem: high-quality reconstructions cannot be generated without deep learning, and the deep neural network cannot be learned without the reconstructions. This work develops and validates a self-supervised probabilistic deep learning technique, the physics-informed variational autoencoder, to solve this problem. A dataset consisting solely of sparse projection measurements from each object is used to jointly reconstruct all objects of the set. This approach has the potential to allow visualization of fragile samples with x-ray computed tomography. We release our code for reproducing our results at: https://github.com/vganapati/CT_PVAE . Comment: NeurIPS 2022 Machine Learning and the Physical Sciences Workshop. arXiv admin note: text overlap with arXiv:2210.16709 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.00002 |
| رقم الأكسشن: | edsarx.2211.00002 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |