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Glimpse: Generalized Locality for Scalable and Robust CT
- Deep learning has become the preferred approach for medical tomographic imaging, with common methods using a convolutional neural network (CNN) after simple inversion steps like backprojection.
- Current CNN approaches tend to overfit large-scale structures and struggle with generalization on out-of-distribution (OOD) samples.
- Multiscale CNNs are computationally complex and memory-intensive at high resolutions, limiting practical applications in realistic clinical settings.
- A new approach called Glimpse, a local coordinate-based neural network for computed tomography, processes only neighborhood measurements for pixel reconstruction.
- Glimpse outperforms CNNs on OOD samples, maintains performance on in-distribution test data, and has a memory footprint independent of image resolution.
- Training Glimpse on 1024x1024 images requires only 5GB of memory, significantly less than needed for CNNs.
- Glimpse is fully differentiable and can be integrated into various deep learning architectures, allowing tasks like correcting miscalibrated projection orientations.
- The implementation and demo of Glimpse are available on GitHub at https://github.com/swing-research/Glimpse.
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