Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A fatty acid amide activates myeloid cells and improves neurovascular outcomes in retinal degeneration.

Nature neuroscience·2026

Same author

Accurate and fast event-based shape measurement of mixed reflectance scenes.

Nature communications·2026

Same author

Deep-learning endomicroscope with large field-of-view and depth-of-field for real-time in vivo imaging of epithelial cancer hallmarks.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Tet2 deficiency alters CD4+ T cell function and promotes T cell lymphoma with a TFH cell immunophenotype.

The Journal of experimental medicine·2026

Same author

Patient-Friendly Real-Time Optical Tomographic Imaging System (LOTIS) for Lupus Arthritis.

Biosensors·2026

Same author

Privacy-Aware Meta-Optics for Person Detection.

ACS photonics·2026

Same journal

2D Ultrasound Elasticity Imaging of Abdominal Aortic Aneurysms Using Deep Neural Networks.

IEEE transactions on computational imaging·2026

Same journal

Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO).

IEEE transactions on computational imaging·2026

Same journal

Spatiotemporal Maps for Dynamic MRI Reconstruction.

IEEE transactions on computational imaging·2026

Same journal

A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers.

IEEE transactions on computational imaging·2026

Same journal

IE-GADCI: An End-to-End Incoherence-Enhanced Generative Adversarial Deep Compressive Imaging.

IEEE transactions on computational imaging·2026

Same journal

Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction.

IEEE transactions on computational imaging·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 23, 2025

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

High-Speed Time-Domain Diffuse Optical Tomography with a Sensitivity Equation-based Neural Network.

Fay Wang¹, Stephen H Kim², Yongyi Zhao³

¹Department of Biomedical Engineering, Columbia University, New York, NY 10027.

IEEE Transactions on Computational Imaging

|July 17, 2023

Summary

This summary is machine-generated.

A new deep learning algorithm, SENSOR-NET, enables rapid, high-resolution reconstructions for time-domain diffuse optical tomography (TD-DOT). This breakthrough accelerates brain monitoring and other high-speed applications by reducing computational demands.

Keywords:

Deep learning diffuse optics image reconstruction inverse problem sensitivity equation sparse image reconstruction

More Related Videos

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Related Experiment Videos

Last Updated: Jul 23, 2025

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Area of Science:

Biomedical optics
Medical imaging
Computational neuroscience

Background:

Time-domain diffuse optical tomography (TD-DOT) offers accurate physiological measurements but faces challenges in temporal resolution due to computationally intensive inverse problem solving.
Current TD-DOT reconstruction methods require empirical tuning, increasing complexity and slowing down the process.

Purpose of the Study:

To develop a novel, rapid, and high-resolution reconstruction algorithm for TD-DOT.
To overcome the limitations of long reconstruction times and empirical parameter tuning in existing TD-DOT methods.

Main Methods:

Introduced SENSOR-NET, a deep learning algorithm that integrates with the Sensitivity Equation-based, Non-iterative Sparse Optical Reconstruction (SENSOR) code.
Unfolded SENSOR iterations into a deep neural network, utilizing learned parameters for reconstruction and eliminating the need for empirical tuning.
Validated the algorithm using numerical and experimental data.

Main Results:

Achieved accurate reconstructions with 1 mm spatial resolution in under 20 milliseconds.
Demonstrated that reconstruction time is independent of the number of sources or wavelengths after network training.
Showcased the potential for real-time brain monitoring and other high-speed diffuse optical tomography applications.

Conclusions:

SENSOR-NET significantly enhances the speed and efficiency of TD-DOT reconstructions.
The algorithm's performance and speed pave the way for widespread clinical adoption of TD-DOT for dynamic physiological monitoring.
This advancement facilitates real-time applications in neuroscience and beyond.