More Like this

1. Determining the thermal characteristics of breast cancer based on high-resolution infrared imaging, 3D breast scans, and magnetic resonance imaging

   https://doi.org/10.1038/s41598-020-66926-6  

   Lozano, Adolfo ; Hayes, Jody C. ; Compton, Lindsay M. ; Azarnoosh, Jamasp ; Hassanipour, Fatemeh ( December 2020 , Scientific Reports)
2. LBIC Imaging of Solar Cells: An Introduction to Scanning Probe-Based Imaging Techniques

   https://doi.org/10.1021/acs.jchemed.2c00623  

   Marquez, Steven ; Varra, Travis ; Christensen, Cami ; Rajasekharan, Om ; Dojan, Carter ; Hobbs, Janelle ; Otten, Abigail ; Salzer, Luke ; Schuttlefield Christus, Jennifer D. ; Sambur, Justin B. ( January 2023 , Journal of Chemical Education)

   Scanning probe-based microscopes (SPMs) are widely used in biology, chemistry, materials science, and physics to image and manipulate matter on the nanoscale. Unfortunately, high school and university departments lack expensive SPM tools and materials microscopy activities to educate a large number of students in this vital SPM imaging technique. As a result, students face challenges participating in and contributing value to the nanotechnology revolution driving modern scientific innovations. Here we demonstrate an affordable scanning laser-based imaging system (approximately $400, excluding the computer) to introduce students to the point-by-point image formation process underlying SPM methods. In this laboratory activity, students learn how to construct and optimize images of a working solar panel using a laser beam-induced current (LBIC) imaging system. We envision undergraduate and graduate students should be able to use this LBIC system for independent solar energy research projects as well as apply fundamental knowledge and measurement skills to understand other SPM techniques. 

   more » « less
3. Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging

   https://doi.org/10.1609/aaai.v35i3.16366  

   Sun, He ; Bouman, Katherine L. ( May 2021 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Computational image reconstruction algorithms generally produce a single image without any measure of uncertainty or confidence. Regularized Maximum Likelihood (RML) and feed-forward deep learning approaches for inverse problems typically focus on recovering a point estimate. This is a serious limitation when working with under-determined imaging systems, where it is conceivable that multiple image modes would be consistent with the measured data. Characterizing the space of probable images that explain the observational data is therefore crucial. In this paper, we propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty. Deep Probabilistic Imaging (DPI) employs an untrained deep generative model to estimate a posterior distribution of an unobserved image. This approach does not require any training data; instead, it optimizes the weights of a neural network to generate image samples that fit a particular measurement dataset. Once the network weights have been learned, the posterior distribution can be efficiently sampled. We demonstrate this approach in the context of interferometric radio imaging, which is used for black hole imaging with the Event Horizon Telescope, and compressed sensing Magnetic Resonance Imaging (MRI). 

   more » « less
4. Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

   https://doi.org/10.1109/msp.2022.3215288  

   Hammernik, Kerstin ; Kustner, Thomas ; Yaman, Burhaneddin ; Huang, Zhengnan ; Rueckert, Daniel ; Knoll, Florian ; Akcakaya, Mehmet ( January 2023 , IEEE Signal Processing Magazine)
5. Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli

   https://doi.org/10.1021/jasms.3c00033  

   Esselman, Allison B. ; Patterson, Nathan Heath ; Migas, Lukasz G. ; Dufresne, Martin ; Djambazova, Katerina V. ; Colley, Madeline E. ; Van de Plas, Raf ; Spraggins, Jeffrey M. ( July 2023 , Journal of the American Society for Mass Spectrometry)

   The glomerulus is a multicellular functional tissue unit (FTU) of the nephron that is responsible for blood filtration. Each glomerulus contains multiple substructures and cell types that are crucial for their function. To understand normal aging and disease in kidneys, methods for high spatial resolution molecular imaging within these FTUs across whole slide images is required. Here we demonstrate a workflow using microscopy-driven selected sampling to enable 5 μm pixel size matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of all glomeruli within whole slide human kidney tissues. Such high spatial resolution imaging entails large numbers of pixels, increasing the data acquisition times. Automating FTU-specific tissue sampling enables high-resolution analysis of critical tissue structures, while concurrently maintaining throughput. Glomeruli were automatically segmented using coregistered autofluorescence microscopy data, and these segmentations were translated into MALDI IMS measurement regions. This allowed high-throughput acquisition of 268 glomeruli from a single whole slide human kidney tissue section. Unsupervised machine learning methods were used to discover molecular profiles of glomerular subregions and differentiate between healthy and diseased glomeruli. Average spectra for each glomerulus were analyzed using Uniform Manifold Approximation and Projection (UMAP) and k-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise k-means clustering was applied to all glomeruli, showing unique molecular profiles localized to subregions within each glomerulus. Automated microscopy-driven, FTU-targeted acquisition for high spatial resolution molecular imaging maintains high-throughput and enables rapid assessment of whole slide images at cellular resolution and identification of tissue features associated with normal aging and disease. 

   more » « less