Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy

• URL: http://arxiv.org/abs/2401.11541v1
• Date: Sun, 21 Jan 2024 16:46:04 GMT
• Title: Multi-View Neural 3D Reconstruction of Micro-/Nanostructures with Atomic Force Microscopy
• Authors: Shuo Chen, Mao Peng, Yijin Li, Bing-Feng Ju, Hujun Bao, Yuan-Liu Chen, Guofeng Zhang
• Abstract summary: We propose a multi-view neural-network-based framework with Atomic Force Microscopy (MVN-AFM) MVN-AFM uses an iterative method to align multi-view data and eliminate AFM artifacts simultaneously. Experiments show that MVN-AFM effectively eliminates artifacts present in raw AFM images and reconstructs various micro-/nanostructures.
• Score: 34.22380991792944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Atomic Force Microscopy (AFM) is a widely employed tool for micro-/nanoscale topographic imaging. However, conventional AFM scanning struggles to reconstruct complex 3D micro-/nanostructures precisely due to limitations such as incomplete sample topography capturing and tip-sample convolution artifacts. Here, we propose a multi-view neural-network-based framework with AFM (MVN-AFM), which accurately reconstructs surface models of intricate micro-/nanostructures. Unlike previous works, MVN-AFM does not depend on any specially shaped probes or costly modifications to the AFM system. To achieve this, MVN-AFM uniquely employs an iterative method to align multi-view data and eliminate AFM artifacts simultaneously. Furthermore, we pioneer the application of neural implicit surface reconstruction in nanotechnology and achieve markedly improved results. Extensive experiments show that MVN-AFM effectively eliminates artifacts present in raw AFM images and reconstructs various micro-/nanostructures including complex geometrical microstructures printed via Two-photon Lithography and nanoparticles such as PMMA nanospheres and ZIF-67 nanocrystals. This work presents a cost-effective tool for micro-/nanoscale 3D analysis.

Related papers

• Diff-FMT: Diffusion Models for Fluorescence Molecular Tomography [16.950699640321936]
  We propose a FMT reconstruction method based on a denoising diffusion probabilistic model (DDPM) Through the step-by-step probability sampling mechanism, we achieve fine-grained reconstruction of the image, avoiding issues such as loss of image detail. We show that Diff-FMT can achieve high-resolution reconstruction images without relying on large-scale datasets.
  arXiv  Detail & Related papers  (2024-10-09T10:41:31Z)
• 3D Reconstruction of Protein Structures from Multi-view AFM Images using Neural Radiance Fields (NeRFs) [8.257409049477827]
  We investigate using atomic force microscopy (AFM) combined with deep learning to predict the 3D structures of protein complexes (PCs) AFM generates height maps that depict the PCs in various random orientations, providing a rich information for training a neural network to predict the 3D structures. The performance of UpFusion is evaluated through zero-shot predictions of 3D protein structures using AFM images.
  arXiv  Detail & Related papers  (2024-08-12T15:53:24Z)
• Robust single-particle cryo-EM image denoising and restoration [11.173867153077692]
  Cryo-electron microscopy (cryo-EM) has achieved near-atomic level resolution of biomolecules by reconstructing 2D micrographs. However, the resolution and accuracy of the reconstructed particles are significantly reduced due to the extremely low signal-to-noise ratio (SNR) and complex noise structure of cryo-EM images. We introduce a diffusion model with post-processing framework to effectively denoise and restore single particle cryo-EM images.
  arXiv  Detail & Related papers  (2024-01-02T08:33:36Z)
• Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs [0.0]
  We present a framework for reconstruction of anisotropic microstructures based on conditional diffusion-based generative models (DGMs) The proposed framework involves spatial connection of multiple 2D conditional DGMs, each trained to generate 2D microstructure samples for three different planes. The results demonstrate that the framework is capable of reproducing not only the statistical distribution of material phases but also the material properties in 3D space.
  arXiv  Detail & Related papers  (2023-12-13T01:36:37Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [49.84018914962972]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling [0.0]
  This study proposes a novel framework for 2D-to-3D reconstruction of microstructures called Micro3Diff. Specifically, this approach solely requires pre-trained DGMs for the generation of 2D samples. A harmonized sampling process is developed to address possible deviations from the reverse Markov chain of DGMs.
  arXiv  Detail & Related papers  (2023-08-27T07:57:25Z)
• Passive superresolution imaging of incoherent objects [63.942632088208505]
  Method consists of measuring the field's spatial mode components in the image plane in the overcomplete basis of Hermite-Gaussian modes and their superpositions. Deep neural network is used to reconstruct the object from these measurements.
  arXiv  Detail & Related papers  (2023-04-19T15:53:09Z)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics [77.34726150561087]
  This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
  arXiv  Detail & Related papers  (2022-05-31T13:26:51Z)
• 3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks [52.710012864395246]
  We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs. We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
  arXiv  Detail & Related papers  (2021-10-14T23:05:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.