Maarten W. Tuijtel

cryoTIGER uses deep learning to generate intermediate tilt images in cryo-electron tomography, improving 3D reconstruction and structural recovery.

This study evaluates tilt increments in cryo-ET to find the optimal balance between image quality and radiation damage, recommending 3 degrees as the best increment.

This paper introduces a high-confidence 3D template matching method for CryoET, enabling accurate localization of cellular structures with improved statistical confidence.

The paper investigates how support grid design in cryo-TEM affects fluorescence microscopy illumination and demonstrates improvements in super-resolution image quality through optimal grid selection.