In many optical imaging applications, it is necessary to overcome aberrations to obtain high-resolution images. Aberration correction can be performed by either physically modifying the optical wavefront using hardware components, or by modifying the wavefront during image reconstruction using computational imaging. Here we address a longstanding issue in computational imaging: photons that are not collected cannot be corrected. This severely restricts the applications of computational wavefront correction. Additionally, performance limitations of hardware wavefront correction leave many aberrations uncorrected. We combine hardware and computational correction to address the shortcomings of each method. Coherent optical backscattering data is collected using high-speed optical coherence tomography, with aberrations corrected at the time of acquisition using a wavefront sensor and deformable mirror to maximize photon collection. Remaining aberrations are corrected by digitally modifying the coherently-measured wavefront during imaging reconstruction. This strategy obtains high-resolution images with improved signal-to-noise ratio of human photoreceptor cells with more complete correction of ocular aberrations, and increased flexibility to image at multiple retinal depths, field locations, and time points. While our approach is not restricted to retinal imaging, this application is one of the most challenging for computational imaging due to the large aberrations of the dilated pupil, time-varying aberrations, and unavoidable eye motion. In contrast with previous computational imaging work, we have imaged single photoreceptors and their waveguide modes in fully dilated eyes with a single acquisition. Combined hardware and computational wavefront correction improves the image sharpness of existing adaptive optics systems, and broadens the potential applications of computational imaging methods.
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http://dx.doi.org/10.1364/BOE.9.002562 | DOI Listing |
Front Psychol
December 2024
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders.
Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN.
Acta Cardiol Sin
January 2025
Division of Cardiology, Cardiovascular Medical Center, and Department of Nuclear Medicine, Far Eastern Memorial Hospital, New Taipei City.
This 2025 updated consensus outlines the diagnostic strategy for transthyretin amyloid cardiomyopathy (ATTR-CM). Given that ATTR-CM is a significant contributor to heart failure, this article emphasizes the importance of making an early and precise diagnosis, particularly as new therapeutic options become available. Highlighting the critical importance of an early and accurate diagnosis, particularly in light of emerging therapeutic modalities, this consensus underscores the central role of Tc-pyrophosphate (PYP) scintigraphy as a non-invasive diagnostic tool.
View Article and Find Full Text PDFFront Syst Neurosci
December 2024
Universidade Federal de Goias, School of Electrical, Mechanical and Computer Engineering, Goiânia, Brazil.
Dysfunction in fear and stress responses is intrinsically linked to various neurological diseases, including anxiety disorders, depression, and Post-Traumatic Stress Disorder. Previous studies using in vivo models with Immediate-Extinction Deficit (IED) and Stress Enhanced Fear Learning (SEFL) protocols have provided valuable insights into these mechanisms and aided the development of new therapeutic approaches. However, assessing these dysfunctions in animal subjects using IED and SEFL protocols can cause significant pain and suffering.
View Article and Find Full Text PDFPatterns (N Y)
December 2024
Data Sciences and Artificial Intelligence Section, College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA.
The placenta is vital to maternal and child health but often overlooked in pregnancy studies. Addressing the need for a more accessible and cost-effective method of placental assessment, our study introduces a computational tool designed for the analysis of placental photographs. Leveraging images and pathology reports collected from sites in the United States and Uganda over a 12-year period, we developed a cross-modal contrastive learning algorithm consisting of pre-alignment, distillation, and retrieval modules.
View Article and Find Full Text PDFJ Surg Case Rep
January 2025
Department of Gastroenterological and Transplant Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8551, Japan.
Neuroendocrine tumors (NENs) originate from neuroendocrine cells and predominantly occur in the gastrointestinal tract, lungs, and pancreas. Although the liver is commonly involved in NEN metastasis, primary hepatic neuroendocrine tumors (PHNETs) are rare. Herein, we report a case of a 52-year-old female who presented with slowly enlarging, cystic, multiple PHNETs.
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