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Francesco De Carlo

Publications and source records attributed to Francesco De Carlo.

10 recordsLinked to original sources
arXiv2026

Tomo-center: an AI-based rotation-axis center finder for synchrotron micro- and nano-tomography

Accurate determination of the rotation-axis position is a prerequisite for artifact-free reconstruction in parallel-beam synchrotron micro-tomography. Traditional approaches such as Vo's method rely on sinogram features that can fail for low-contrast or weakly absorbing specimens. We present a learning-based method that treats center selection as a binary classification problem, using a DINOv2-pretrained vision transformer aggregated with attention-based multiple-instance learning, fine-tuned end-to-end on tomographic images. At inference time, the proposed algorithm was applied to a stack of tomograms reconstructed at a sweep of candidate centers to select the optimal center for reconstruction. We tested the estimation accuracy of the proposed method on two independent data sources and consistently achieved a mean absolute error of below 1 pixel. We also tested the method robustness to sparse or noisy acquisitions with the same datasets and demonstrated consistent performance when the number of projections was reduced by a factor of up to 10 or the blank scan factor of the underlying Poisson's noise was increased to 10. We also illustrated the interpretability of the proposed method by mapping out the relative contributions of continuous spatial features to the overall classification task. This method, delivered as tomo-center, an open-source command-line tool, has been integrated into several tomography software packages to assist experiments during the routine beamline operations.

eess.IV↗
arXiv2024

Murine AI excels at cats and cheese: Structural differences between human and mouse neurons and their implementation in generative AIs

Mouse and human brains have different functions that depend on their neuronal networks. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the mouse medial prefrontal cortex and compared them with structures of the human anterior cingulate cortex. The obtained results indicated that mouse neuronal somata are smaller and neurites are thinner than those of human neurons. These structural features allow mouse neurons to be integrated in the limited space of the brain, though thin neurites should suppress distal connections according to cable theory. We implemented this mouse-mimetic constraint in convolutional layers of a generative adversarial network (GAN) and a denoising diffusion implicit model (DDIM), which were then subjected to image generation tasks using photo datasets of cat faces, cheese, human faces, and birds. The mouse-mimetic GAN outperformed a standard GAN in the image generation task using the cat faces and cheese photo datasets, but underperformed for human faces and birds. The mouse-mimetic DDIM gave similar results, suggesting that the nature of the datasets affected the results. Analyses of the four datasets indicated differences in their image entropy, which should influence the number of parameters required for image generation. The preferences of the mouse-mimetic AIs coincided with the impressions commonly associated with mice. The relationship between the neuronal network and brain function should be investigated by implementing other biological findings in artificial neural networks.

q-bio.NC↗
arXiv2024

Laminography as a tool for imaging large-size samples with high resolution

Despite the increased brilliance of the new generation synchrotron sources, there is still a challenge with high-resolution scanning of very thick and absorbing samples, such as the whole mouse brain stained with heavy elements, and, extending further, brains of primates. Samples are typically cut into smaller parts, to ensure a sufficient X-ray transmission, and scanned separately. Compared to the standard tomography setup where the sample would be cut into many pillars, the laminographic geometry operates with slab-shaped sections significantly reducing the number of sample parts to be prepared, the cutting damage and data stitching problems. In this work, we present a laminography pipeline for imaging large samples (> 1 cm) at micrometer resolution. The implementation includes a low-cost instrument setup installed at the 2-BM micro-CT beamline of the Advanced Photon Source (APS). Additionally, we present sample mounting, scanning techniques, data stitching procedures, a fast reconstruction algorithm with low computational complexity, and accelerated reconstruction on multi-GPU systems for processing large-scale datasets. The applicability of the whole laminography pipeline was demonstrated with imaging 4 sequential slabs throughout the entire mouse brain sample stained with osmium, in total generating approximately 12TB of raw data for reconstruction.

physics.comp-ph↗
arXiv2022

Structural aging of human neurons is the opposite of the changes in schizophrenia

Human mentality develops with age and is altered in psychiatric disorders, though their underlying mechanism is unknown. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the anterior cingulate cortex from eight schizophrenia and eight control cases. The distribution profiles of neurite curvature of the control cases showed a trend depending on their age, resulting in an age-correlated decrease in the standard deviation of neurite curvature (Pearson's r = -0.80, p = 0.018). In contrast to the control cases, the schizophrenia cases deviate upward from this correlation, exhibiting a 60% higher neurite curvature compared with the controls (p = 7.8 x 10^(-4)). The neurite curvature also showed a correlation with a hallucination score (Pearson's r = 0.80, p = 1.8 x 10^(-4)), indicating that neurite structure is relevant to brain function. We suggest that neurite curvature plays a pivotal role in brain aging and can be used as a hallmark to exploit a novel treatment of schizophrenia. This nano-CT paper is the result of our decade-long analysis and is unprecedented in terms of number of cases.

q-bio.NC↗
arXiv2021

Distributed optimization for nonrigid nano-tomography

Resolution level and reconstruction quality in nano-computed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist beam damage during data acquisition. In such cases, there is no incentive in recovering the sample state at different time steps like in time-resolved reconstruction methods, but instead the goal is to retrieve a single reconstruction at the highest possible spatial resolution and without any imaging artifacts. Here we propose a joint solver for imaging samples at the nanoscale with projection alignment, unwarping and regularization. Projection data consistency is regulated by dense optical flow estimated by Farneback's algorithm, leading to sharp sample reconstructions with less artifacts. Synthetic data tests show robustness of the method to Poisson and low-frequency background noise. Applicability of the method is demonstrated on two large-scale nano-imaging experimental data sets.

cs.CE↗
arXiv2020

Structural diverseness of neurons between brain areas and between cases

The cerebral cortex is composed of multiple cortical areas that exert a wide variety of brain functions. Although human brain neurons are genetically and areally mosaic, the three-dimensional structural differences between neurons in different brain areas or between the neurons of different individuals have not been delineated. Here, we report a nanometer-scale geometric analysis of brain tissues of the superior temporal gyrus of 4 schizophrenia and 4 control cases by using synchrotron radiation nanotomography. The results of the analysis and a comparison with results for the anterior cingulate cortex indicated that 1) neuron structures are dissimilar between brain areas and that 2) the dissimilarity varies from case to case. The structural diverseness was mainly observed in terms of the neurite curvature that inversely correlates with the diameters of the neurites and spines. The analysis also revealed the geometric differences between the neurons of the schizophrenia and control cases, suggesting that neuron structure is associated with brain function. The area dependency of the neuron structure and its diverseness between individuals should represent the individuality of brain functions.

q-bio.NC↗
arXiv2019

TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks

Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for reconstructing the internal structure of materials at high spatial resolutions from tens of micrometers to a few nanometers. In order to resolve sample features at smaller length scales, however, a higher radiation dose is required. Therefore, the limitation on the achievable resolution is set primarily by noise at these length scales. We present \TOMOGAN{}, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions. We evaluate our approach in two photon-budget-limited experimental conditions: (1) sufficient number of low-dose projections (based on Nyquist sampling), and (2) insufficient or limited number of high-dose projections. In both cases the angular sampling is assumed to be isotropic, and the photon budget throughout the experiment is fixed based on the maximum allowable radiation dose on the sample. Evaluation with both simulated and experimental datasets shows that our approach can significantly reduce noise in reconstructed images, improving the structural similarity score of simulation and experimental data from 0.18 to 0.9 and from 0.18 to 0.41, respectively. Furthermore, the quality of the reconstructed images with filtered back projection followed by our denoising approach exceeds that of reconstructions with the simultaneous iterative reconstruction technique, showing the computational superiority of our approach.

cs.CV↗
arXiv2019

Dynamic in-situ imaging of methane hydrate formation and self-preservation

We present the results of dynamic in-situ 3D X-ray imaging of methane hydrates microstructure during methane hydrate formation and decomposition in sand samples. Short scanning times and high resolution provided by synchrotron X-rays allowed for better understanding of water movement and different types of gas-hydrate formation. Complementing previous observations, we conclude that the process of gas-hydrate formation is accompanied by the water movements caused by cryogenic water suction that happens in short sequences with longer equilibrium states in between (when the water is immobile). Based on the 3D microstructure we identified two types of gas-hydrate formation: (i) into the gas pockets and (ii) inside water volumes. During the decomposition in the self-preservation mode (pressure drop at negative temperatures) the latter remains more stable compared to the hydrate formed as growing into the gas pocket. This means that the history of the gas-hydrate formation influences its behaviour at the decomposition stage (e.g. gas-hydrate production).

physics.geo-ph↗
arXiv2019

Three-dimensional alteration of neurites in schizophrenia

This paper reports nano-CT analysis of brain tissues of schizophrenia and control cases. The analysis revealed that: (1) neuronal structures vary between individuals, (2) the mean curvature of distal neurites of the schizophrenia cases was 1.5 times higher than that of the controls, and (3) dendritic spines were categorized into two geometrically distinctive groups, though no structural differences were observed between the disease and control cases. The differences in the neurite curvature result in differences in the spatial trajectory and hence alter neuronal circuits. We suggest that the structural alteration of neurons in the schizophrenia cases should reflect psychiatric symptoms of schizophrenia.

q-bio.NC↗