Singanallur Venkatakrishnan

Aniket Pramanik, Obaidullah Rahman, Singanallur V. Venkatakrishnan et al.

Cone-beam X-ray Computed Tomography (XCT) with large detectors and corresponding large-scale 3D reconstruction plays a pivotal role in micron-scale characterization of materials and parts across various industries. In this work, we present a novel deep neural network-based iterative algorithm that integrates an artifact reduction-trained CNN as a prior model with automated regularization parameter selection, tailored for large-scale industrial cone-beam XCT data. Our method achieves high-quality 3D reconstructions even for extremely dense thick metal parts - which traditionally pose challenges to industrial CT images - in just a few iterations. Furthermore, we show the generalizability of our approach to out-of-distribution scans obtained under diverse scanning conditions. Our method effectively handles significant noise and streak artifacts, surpassing state-of-the-art supervised learning methods trained on the same data.

Suhas Sreehari, S. V. Venkatakrishnan, Katherine L. Bouman et al.

Perhaps surprisingly, the total electron microscopy (EM) data collected to date is less than a cubic millimeter. Consequently, there is an enormous demand in the materials and biological sciences to image at greater speed and lower dosage, while maintaining resolution. Traditional EM imaging based on homogeneous raster-order scanning severely limits the volume of high-resolution data that can be collected, and presents a fundamental limitation to understanding physical processes such as material deformation, crack propagation, and pyrolysis. We introduce a novel multi-resolution data fusion (MDF) method for super-resolution computational EM. Our method combines innovative data acquisition with novel algorithmic techniques to dramatically improve the resolution/volume/speed trade-off. The key to our approach is to collect the entire sample at low resolution, while simultaneously collecting a small fraction of data at high resolution. The high-resolution measurements are then used to create a material-specific patch-library that is used within the "plug-and-play" framework to dramatically improve super-resolution of the low-resolution data. We present results using FEI electron microscope data that demonstrate super-resolution factors of 4x, 8x, and 16x, while substantially maintaining high image quality and reducing dosage.