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1. Deep Learning-Based Point-Scanning Super-Resolution Imaging

   https://doi.org/10.1101/740548  

   Fang L, Monroe F (January 2019, PloS one)

   Point scanning imaging systems (e.g. scanning electron or laser scanning confocal microscopes) are perhaps the most widely used tools for high resolution cellular and tissue imaging. Like all other imaging modalities, the resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point scanning systems are difficult to optimize simultaneously. In particular, point scanning systems are uniquely constrained by an inverse relationship between imaging speed and pixel resolution. Here we show these limitations can be miti gated via the use of deep learning-based super-sampling of undersampled images acquired on a point-scanning system, which we termed point -scanning super-resolution (PSSR) imaging. Oversampled ground truth images acquired on scanning electron or Airyscan laser scanning confocal microscopes were used to generate semi-synthetictrain ing data for PSSR models that were then used to restore undersampled images. Remarkably, our EM PSSR model was able to restore undersampled images acquired with different optics, detectors, samples, or sample preparation methods in other labs . PSSR enabled previously unattainable xy resolution images with our serial block face scanning electron microscope system. For fluorescence, we show that undersampled confocal images combined with a multiframe PSSR model trained on Airyscan timelapses facilitates Airyscan-equivalent spati al resolution and SNR with ~100x lower laser dose and 16x higher frame rates than corresponding high-resolution acquisitions. In conclusion, PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity. 

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2. Optimizing dual‐energy CT technique for iodine‐based contrast‐to‐noise ratio, a theoretical study

   https://doi.org/10.1002/mp.17010  

   Terzioglu, Fatma; Sidky, Emil Y; Phillips, John Paul; Reiser, Ingrid S; Bal, Guillaume; Pan, Xiaochuan (April 2024, Medical Physics)

   Abstract BackgroundDual‐energy CT (DECT) systems provide valuable material‐specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast‐to‐noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x‐ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall image quality. PurposeThe goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiation dose. MethodsThe noise propagation in the process of projection based material estimation from DECT measurements is analyzed. The main components of the study are the mean pixel variances for the sinogram and monochromatic image and the CNR, which were shown to depend on the Jacobian matrix of the sinograms‐to‐DECT measurements map.Analytic estimates for the mean sinogram and monochromatic image pixel variances and the CNR as functions of tube potentials, fluence, and VMI energy are derived, and then used in a virtual phantom experiment as an objective function for optimizing the tube settings and VMI energy to minimize the image noise and maximize the CNR. ResultsIt was shown that DECT measurements corresponding to kV settings that maximize the square of Jacobian determinant values over a domain of interest lead to improved stability of basis material reconstructions.Instances of non‐uniqueness in DECT were addressed, focusing on scenarios where the Jacobian determinant becomes zero within the domain of interest despite significant spectral separation. The presence of non‐uniqueness can lead to singular solutions during the inversion of sinograms‐to‐DECT measurements, underscoring the importance of considering uniqueness properties in parameter selection.Additionally, the optimal VMI energy and tube potentials for maximal CNR was determined. When the x‐ray beam filter material was fixed at 2 mm of aluminum and the photon fluence for low and high kV scans were considered equal, the tube potential pair of 60/120 kV led to the maximal iodine CNR in the VMI at 53 keV. ConclusionsOptimizing DECT scan parameters to maximize the CNR can be done in a systematic way. Also, choosing the parameters that maximize the Jacobian determinant over the set of expected line integrals leads to more stable reconstructions due to the reduced amplification of the measurement noise. Since the values of the Jacobian determinant depend strongly on the imaging task, careful consideration of all of the relevant factors is needed when implementing the proposed framework. 

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3. Diffusion probabilistic generative models for accelerated, in‐NICU permanent magnet neonatal MRI

   https://doi.org/10.1002/mrm.30585  

   Arefeen, Yamin; Levac, Brett; Patel, Bhairav; Ho, Chang; Tamir, Jonathan I (June 2025, Magnetic Resonance in Medicine)

   Purpose: Magnetic Resonance Imaging (MRI) enables non‐invasive assessment of brain abnormalities during early life development. Permanent magnet scanners operating in the neonatal intensive care unit (NICU) facilitate MRI of sick infants, but have long scan times due to lower signal‐to‐noise ratios (SNR) and limited receive coils. This work accelerates in‐NICU MRI with diffusion probabilistic generative models by developing a training pipeline accounting for these challenges. Methods: We establish a novel training dataset of clinical, 1 Tesla neonatal MR images in collaboration with Aspect Imaging and Sha'are Zedek Medical Center. We propose a pipeline to handle the low quantity and SNR of our real‐world dataset (1) modifying existing network architectures to support varying resolutions; (2) training a single model on all data with learned class embedding vectors; (3) applying self‐supervised denoising before training; and (4) reconstructing by averaging posterior samples. Retrospective under‐sampling experiments, accounting for signal decay, evaluated each item of our proposed methodology. A clinical reader study with practicing pediatric neuroradiologists evaluated our proposed images reconstructed from under‐sampled data. Results: Combining all data, denoising pre‐training, and averaging posterior samples yields quantitative improvements in reconstruction. The generative model decouples the learned prior from the measurement model and functions at two acceleration rates without re‐training. The reader study suggests that proposed images reconstructed from under‐sampled data are adequate for clinical use. Conclusion: Diffusion probabilistic generative models applied with the proposed pipeline to handle challenging real‐world datasets could reduce the scan time of in‐NICU neonatal MRI. 

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4. Fast photostable expansion microscopy using QDots and deconvolution

   https://doi.org/10.1371/journal.pone.0325155  

   Gunawardhana, Loku; Moree, Wilna; Guo, Jiaming; Artur, Camille; Womack, Tasha; Eriksen, Jason L; Mayerich, David (June 2025, PLOS One)

   Pesce, Luca (Ed.)

   Expansion microscopy (ExM) enables sub-diffraction imaging by physically expanding labeled tissue samples. This increases the tissue volume relative to the instrument point spread function (PSF), thereby improving the effective resolution by reported factors of 4 - 20X. However, this volume increase dilutes the fluorescence signal, reducing both signal-to-noise ratio (SNR) and acquisition speed. This paper proposes and validates a method for mitigating these challenges. We overcame the limitations of ExM by developing a fast photo-stable protocol to enable scalable widefield three-dimensional imaging with ExM. We combined widefield imaging with quantum dots (QDots). Widefield imaging provides a significantly faster acquisition of a single field-of-view (FOV). However, the uncontrolled incoherent illumination induces photobleaching. We mitigated this challenge using QDots, which exhibit a long fluorescence lifetime and improved photostability. First, we developed a protocol for QDot labeling. Next, we utilized widefield imaging to obtain 3D image stacks and applied deconvolution, which is feasible due to reduced scattering in ExM samples. We show that increased transparency, which is a side-effect of ExM, enables widefield deconvolution, dramatically reducing the acquisition time for three-dimensional images compared to laser scanning microscopy. The proposed QDot labeling protocol is compatible with ExM and provides enhanced photostability compared to traditional fluorescent dyes. Widefield imaging significantly improves SNR and acquisition speed compared to conventional confocal microscopy. Combining widefield imaging with QDot labeling and deconvolution has the potential to be applied to ExM for faster imaging of large three-dimensional samples with improved SNR. 

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5. Submillimeter lung MRI at 0.55 T using balanced steady‐state free precession with half‐radial dual‐echo readout (bSTAR)

   https://doi.org/10.1002/mrm.29757  

   Bauman, Grzegorz; Lee, Nam G; Tian, Ye; Bieri, Oliver; Nayak, Krishna S (November 2023, Magnetic Resonance in Medicine)

   PurposeTo demonstrate the feasibility of high‐resolution morphologic lung MRI at 0.55 T using a free‐breathing balanced steady‐state free precession half‐radial dual‐echo imaging technique (bSTAR). MethodsSelf‐gated free‐breathing bSTAR (TE₁/TE₂/TR of 0.13/1.93/2.14 ms) lung imaging in five healthy volunteers and a patient with granulomatous lung disease was performed using a 0.55 T MR‐scanner. A wobbling Archimedean spiral pole (WASP) trajectory was used to ensure a homogenous coverage of k‐space over multiple breathing cycles. WASP uses short‐duration interleaves randomly tilted by a small polar angle and rotated by a golden angle about the polar axis. Data were acquired continuously over 12:50 min. Respiratory‐resolved images were reconstructed off‐line using compressed sensing and retrospective self‐gating. Reconstructions were performed with a nominal resolution of 0.9 mm and a reduced isotropic resolution of 1.75 mm corresponding to shorter simulated scan times of 8:34 and 4:17 min, respectively. Analysis of apparent SNR was performed in all volunteers and reconstruction settings. ResultsThe technique provided artifact‐free morphologic lung images in all subjects. The short TR of bSTAR in conjunction with a field strength of 0.55 T resulted in a complete mitigation of off‐resonance artifacts in the chest. Mean SNR values in healthy lung parenchyma for the 12:50 min scan were 3.6 ± 0.8 and 24.9 ± 6.2 for 0.9 mm and 1.75 mm reconstructions, respectively. ConclusionThis study demonstrates the feasibility of morphologic lung MRI with a submillimeter isotropic spatial resolution in human subjects with bSTAR at 0.55 T. 

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