23 papers · ranked by Valyu relevance
Mor Nitzan, Nikos Karaiskos, Nir Friedman, Nikolaus Rajewsky
Massively multiplexed sequencing of RNA in individual cells is transforming basic and clinical life sciences. However, in standard experiments, tissues must first be dissociated. Thus, after sequencing, information about the spatial relationships between cells is lost although this knowledge is crucial for…
Fangfang Xu, Zhenfeng Zhuang, Yun Zhu, Bo Ying + 5 more
Reconstructing whole organs in three-dimensional molecular detail is a key step toward building virtual organs for modeling tissue organization, disease progression and drug perturbation responses. However, high-resolution whole-organ spatial transcriptomic profiling remains impractical, forcing a trade-off between…
Chenlei Hu, Mehdi Borji, Giovanni J. Marrero, Vipin Kumar + 4 more
Tissue organization arises from the coordinated molecular programs of cells. Spatial genomics maps cells and their molecular programs within the spatial context of tissues. However, current methods measure spatial information through imaging or direct registration, which often require specialized equipment and are…
Jean-Baptiste Thomas, Pierre-Jean Lapray, Steven Le Moan, Barry K. Lavine
'Barry K. Lavine'] Recent advances in spectral imaging have enabled snapshot acquisition, as a means to mitigate the impracticalities of spectral imaging, e.g., expert operators and cumbersome hardware. Snapshot spectral imaging, e.g., in technologies like spectral filter arrays, has also enabled higher temporal…
Nora Brackbill, Colleen Rhoades, Alexandra Kling, Nishal P. Shah + 3 more
The visual message conveyed by a retinal ganglion cell (RGC) is often summarized by its spatial receptive field, but in principle also depends on the responses of other RGCs and natural image statistics. This possibility was explored by linear reconstruction of natural images from responses of the four…
Robert Sunderhaft, Logan Frank, Jim Davis
Accurately reconstructing a global spatial field from sparse data has been a longstanding problem in several domains, such as Earth Sciences and Fluid Dynamics. Historically, scientists have approached this problem by employing complex physics models to reconstruct the spatial fields. However, these methods are often…
Jie Sun, Fernando Quevedo, Erik M. Bollt
We introduce a kernel Lasso (kLasso) optimization that simultaneously accounts for spatial regularity and network sparsity to reconstruct spatial complex networks from data. Through a kernel function, the proposed approach exploits spatial embedding distances to penalize overabundance of spatially long-distance…
Sascha Arnold, Bilal Wehbe
— In this work we present a novel method for reconstructing 3D surfaces using a multi-beam imaging sonar. We integrate the intensities measured by the sonar from different viewpoints for fixed cell positions in a 3D grid. For each cell we integrate a feature vector that holds the mean intensity for a discretized range…
Tianjiao Zeng, Xu Zhan, Yu Ren, Xiangdong Ma + 5 more
'Shunjun Wei' 'Mou Wang' 'Xiaoling Zhang'] Array synthetic aperture radar (Array-SAR), also known as tomographic SAR (TomoSAR), has demonstrated significant potential for high-quality 3D mapping, particularly in urban areas. While deep learning (DL) methods have recently shown strengths in both precision and efficiency…
Haopeng Zhang, Quanmao Wei, Zhiguo Jiang
In this paper, a novel 3D reconstruction framework is proposed to recover the 3D structural model of a space object from its multi-view images captured by a visible sensor. Given an image sequence, this framework first estimates the relative camera poses and recovers the depths of the surface points by the structure…
Kirill M. Gerke, Marina V. Karsanina, Dirk Mallants
Spatial data captured with sensors of different resolution would provide a maximum degree of information if the data were to be merged into a single image representing all scales. We develop a general solution for merging multiscale categorical spatial data into a single dataset using stochastic reconstructions with…
Hongyang Dong, Simon D.M. Jacques, Winfried Kockelmann, Stephen W. T. Price + 10 more
Hongyang Dong 3 , Simon D.M. Jacques 1 , Winfried Kockelmann 4 , Stephen W. T. Price 1 , Robert Emberson 5 , Dorota Matras 6,7 , Yaroslav Odarchenko 1 , Vesna Middelkoop 10 , Athanasios Giokaris 1 , Olof Gutowski 8 , Ann-Christin Dippel 8 , Martin v. Zimmermann 8 , Andrew M. Beale 3 , Keith T. Butler 9 , Antonis…
Authors not listed
nDTomo is a Python-based software suite for the simulation, reconstruction and analysis of X-ray chemical imaging and computed tomography data. It provides a collection of Python function-based tools designed for accessibility and education as well as a graphical user interface (GUI). Prioritising transparency and ease…
Liheng Bian, Jinli Suo, Guohai Situ, Ziwei Li + 3 more
'Feng Chen' 'Qionghai Dai'] Existing multispectral imagers mostly use available array sensors to separately measure 2D data slices in a 3D spatial-spectral data cube. Thus they suffer from low photon efficiency, limited spectrum range and high cost. To address these issues, we propose to conduct multispectral imaging…
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…
Hongyang Dong, Simon D.M. Jacques, Keith T. Butler, Olof Gutowski + 4 more
In this study, we introduce a method designed to eliminate parallax artefacts present in Xray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as…
Liheng Bian, Jinli Suo, Xuemei Hu, Feng Chen + 1 more
Single pixel imaging (SPI) is a novel technique being able to capture 2D images using a bucket detector with high signal-to-noise ratio, wide spectrum range and low cost. Conventional SPI projects random illumination patterns to randomly and uniformly sample the entire scene's information. Determined by the Nyquist…
Frank Sippel, Jürgen Seiler, Nils Genser, André Kaup
> Light spectra are a very important source of information for diverse classification problems, e.g., for discrimination of materials. To lower the cost for acquiring this information, multispectral cameras are used. Several techniques exist for estimating light spectra out of multispectral images by exploiting…
Mengdi Li, Anumol Mathai, Stephen L. H. Lau, Jian Wei Yam + 2 more
'Xiping Xu' 'Xin Wang'] Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this…
Chongwu Shao, Yue Cao, Shijian Li, Xu-Ri Yao + 1 more
Single-pixel imaging (SPI), distinguished by its cost-efficiency, exceptional spectral adaptability, and robust sub-Nyquist-Shannon sampling reconstruction capabilities, demonstrates transformative potential across imaging applications yet faces critical limitations in capturing arbitrarily moving targets. This work…
Rafał Stojek, Anna Pastuszczak, Piotr Wróbel, Magdalena Cwojdzińska + 6 more
'Kacper Sobczak' 'Rafał Kotyński' 'Qing Yu' 'Ran Tu' 'Ting Liu' 'Lina Li'] We demonstrate high-resolution single-pixel imaging (SPI) in the visible and near-infrared wavelength ranges using an SPI framework that incorporates a novel, dedicated sampling scheme and a reconstruction algorithm optimized for the rapid…
WoongJae Baek, Jongchan Park, Liang Gao
Fluorescence lifetime imaging microscopy (FLIM) provides molecular contrast that is largely independent of fluorophore concentration, yet it remains constrained by a persistent trade-off among acquisition speed, photon dose, and detector complexity. To address this challenge, we developed image-projection fluorescence…
Sergey Vilov, Bastien Arnal, Eliel Hojman, Yonina C. Eldar + 2 more
'Ori Katz' 'Emmanuel Bossy'] It has previously been demonstrated that model-based reconstruction methods relying on a priori knowledge of the imaging point spread function (PSF) coupled to sparsity priors on the object to image can provide super-resolution in photoacoustic (PA) or in ultrasound (US) imaging. Here, we…