Evidence map›Paper›PMID 19407796›Full record

ArticleNature2009

Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena.

K Goda, K K Tsia, B Jalali

Abstract read
PubMed Publisher
In one paragraph

Article in Nature, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.

0numbers the graph read from it
0cells of the map it votes in
105citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

105 citing papers in PubMed.

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  18. Recent Advances in Single-Molecule Tracking and Imaging Techniques.Annual review of analytical chemistry (Palo Alto, Calif.) · 2023
    Review
  19. Article
  20. Article

45 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

K GodaDepartment of Electrical Engineering, University of California, Los Angeles, California 90095, USA. goda@ee.ucla.edu
K K Tsia
B Jalali

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultrafast real-time optical imaging is an indispensable tool for studying dynamical events such as shock waves, chemical dynamics in living cells, neural activity, laser surgery and microfluidics. However, conventional CCDs (charge-coupled devices) and their complementary metal-oxide-semiconductor (CMOS) counterparts are incapable of capturing fast dynamical processes with high sensitivity and resolution. This is due in part to a technological limitation-it takes time to read out the data from sensor arrays. Also, there is the fundamental compromise between sensitivity and frame rate; at high frame rates, fewer photons are collected during each frame-a problem that affects nearly all optical imaging systems. Here we report an imaging method that overcomes these limitations and offers frame rates that are at least 1,000 times faster than those of conventional CCDs. Our technique maps a two-dimensional (2D) image into a serial time-domain data stream and simultaneously amplifies the image in the optical domain. We capture an entire 2D image using a single-pixel photodetector and achieve a net image amplification of 25 dB (a factor of 316). This overcomes the compromise between sensitivity and frame rate without resorting to cooling and high-intensity illumination. As a proof of concept, we perform continuous real-time imaging at a frame speed of 163 ns (a frame rate of 6.1 MHz) and a shutter speed of 440 ps. We also demonstrate real-time imaging of microfluidic flow and phase-explosion effects that occur during laser ablation.

Indexed as

Diagnostic ImagingLaser TherapyMicrofluidicsMicrospheresSensitivity and SpecificityTime FactorsWaterWater

Identifiers

PMID19407796

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.