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1. VICE: Visual Identification and Correction of Neural Circuit Errors

   https://doi.org/10.1111/cgf.14320  

   Gonda, F; Wang, S; Beyer, J; Lichtman, J; Pfister, H (January 2021, Eurographics Conference on Visualization (EuroVis))

   A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visually intensive and time-consuming process. This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity-related errors. We accomplish this with automated likely-error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. In particular, our strategy centers on proofreading the local circuit of a single cell to ensure a basic level of completeness. We demonstrate our framework’s utility with a user study and report quantitative and subjective feedback from our users. Overall, users find the framework more efficient for proofreading, understanding evolving graphs, and sharing error correction strategies. 

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2. Machine Learning and Deep Learning Applications in Magnetic Particle Imaging

   https://doi.org/10.1002/jmri.29294  

   Nigam, Saumya; Gjelaj, Elvira; Wang, Rui; Wei, Guo‐Wei; Wang, Ping (January 2025, Journal of Magnetic Resonance Imaging)

   In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non‐ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X‐space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence‐based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. Level of Evidence5 Technical EfficacyStage 1 

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3. On cross-correlations, averages and noise in electron microscopy

   https://doi.org/10.1107/S2053230X18014036  

   Radermacher, Michael; Ruiz, Teresa (January 2019, Acta Crystallographica Section F Structural Biology Communications)

   Biological samples are radiation-sensitive and require imaging under low-dose conditions to minimize damage. As a result, images contain a high level of noise and exhibit signal-to-noise ratios that are typically significantly smaller than 1. Averaging techniques, either implicit or explicit, are used to overcome the limitations imposed by the high level of noise. Averaging of 2D images showing the same molecule in the same orientation results in highly significant projections. A high-resolution structure can be obtained by combining the information from many single-particle images to determine a 3D structure. Similarly, averaging of multiple copies of macromolecular assembly subvolumes extracted from tomographic reconstructions can lead to a virtually noise-free high-resolution structure. Cross-correlation methods are often used in the alignment and classification steps of averaging processes for both 2D images and 3D volumes. However, the high noise level can bias alignment and certain classification results. While other approaches may be implicitly affected, sensitivity to noise is most apparent in multireference alignments, 3D reference-based projection alignments and projection-based volume alignments. Here, the influence of the image signal-to-noise ratio on the value of the cross-correlation coefficient is analyzed and a method for compensating for this effect is provided. 

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4. High-performance and scalable on-chip digital Fourier transform spectroscopy

   https://doi.org/10.1038/s41467-018-06773-2  

   Kita, Derek M.; Miranda, Brando; Favela, David; Bono, David; Michon, Jérôme; Lin, Hongtao; Gu, Tian; Hu, Juejun (October 2018, Nature Communications)

   Abstract On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an ‘elastic-D₁’ regularized regression method that we develop, we achieved significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion. 

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5. Extended depth-of-field light-sheet microscopy improves imaging of large volumes at high numerical aperture

   https://doi.org/10.1063/5.0101426  

   Keomanee-Dizon, Kevin; Jones, Matt; Luu, Peter; Fraser, Scott_E; Truong, Thai_V (October 2022, Applied Physics Letters)

   Light-sheet microscopes must compromise among field of view, optical sectioning, resolution, and detection efficiency. High-numerical-aperture (NA) detection objective lenses provide higher resolution, but their narrow depth of field inefficiently captures the fluorescence signal generated throughout the thickness of the illumination light sheet when imaging large volumes. Here, we present ExD-SPIM (extended depth-of-field selective-plane illumination microscopy), an improved light-sheet microscopy strategy that solves this limitation by extending the depth of field (DOF) of high-NA detection objectives to match the thickness of the illumination light sheet. This extension of the DOF uses a phase mask to axially stretch the point-spread function of the objective lens while largely preserving lateral resolution. This matching of the detection DOF to the illumination-sheet thickness increases the total fluorescence collection, reduces the background, and improves the overall signal-to-noise ratio (SNR), as shown by numerical simulations, imaging of bead phantoms, and imaging living animals. In comparison to conventional light sheet imaging with low-NA detection that yields equivalent DOF, the results show that ExD-SPIM increases the SNR by more than threefold and dramatically reduces the rate of photobleaching. Compared to conventional high-NA detection, ExD-SPIM improves the signal sensitivity and volumetric coverage of whole-brain activity imaging, increasing the number of detected neurons by over a third. 

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