Evidence map›Paper›PMID 38745746›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2024

A hyperspectral surgical microscope with super-resolution reconstruction for intraoperative image guidance.

Ling Ma, Kelden Pruitt, Baowei Fei

Abstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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.

Ling MaCenter for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX.
Kelden PruittCenter for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX.
Baowei FeiCenter for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX.

Funding

ACADEMIC-INDUSTRIAL PARTNERSHIP FOR TRANSLATION OF PET/TRUS GUIDED INTERVENTIONR01CA204254 · NCI · UNIVERSITY OF TEXAS DALLAS · PI FEI, BAOWEI · 2017 to 2021
$2.0M
A Real-Time Hyperspectral Laparoscopic Stereo Imaging System for Robot-Assisted SurgeryR01CA288379 · NCI · UNIVERSITY OF TEXAS DALLAS · PI BAOWEI FEI · 2024 to 2026
$1.6M
NCI NIH HHS R01 CA204254NCI NIH HHS R01 CA288379
6 · The paper itself

Abstract

Hyperspectral imaging (HSI) is an emerging imaging modality in medical applications, especially for intraoperative image guidance. A surgical microscope improves surgeons' visualization with fine details during surgery. The combination of HSI and surgical microscope can provide a powerful tool for surgical guidance. However, to acquire high-resolution hyperspectral images, the long integration time and large image file size can be a burden for intraoperative applications. Super-resolution reconstruction allows acquisition of low-resolution hyperspectral images and generates high-resolution HSI. In this work, we developed a hyperspectral surgical microscope and employed our unsupervised super-resolution neural network, which generated high-resolution hyperspectral images with fine textures and spectral characteristics of tissues. The proposed method can reduce the acquisition time and save storage space taken up by hyperspectral images without compromising image quality, which will facilitate the adaptation of hyperspectral imaging technology in intraoperative image guidance.

Indexed as

deep learningHyperspectral imagingimage-guided surgeryneural networksuper resolution reconstructionsurgical microscope

Identifiers

PMID38745746
PMCPMC11093589

What OpenQuestion holds

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Registered trials

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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.