Evidence map›Paper›PMID 37845457›Full record

ArticleAdvances in experimental medicine and biology2023

Time Domain Near-Infrared Optical Tomography Utilizing Full Temporal Data: A Simulation Study.

Letizia Lanini, Alexander Kalyanov, Meret Ackermann, Emanuele Russomanno, Aldo Di Costanzo Mata, Martin Wolf, Jingjing Jiang

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Article in Advances in experimental medicine and biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Letizia LaniniDepartment of Physics, ETH Zürich, Zürich, Switzerland. laninil@student.ethz.ch.
Alexander KalyanovBiomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Meret AckermannBiomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Emanuele RussomannoBiomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Aldo Di Costanzo MataBiomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Martin WolfBiomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.
Jingjing JiangBiomedical Optics Research Laboratory (BORL), Department of Neonatology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The analysis of full temporal data in time-domain near-infrared optical tomography (TD NIROT) measurements enables valuable information to be obtained about tissue properties with good temporal and spatial resolution. However, the large amount of data obtained is not easy to handle in the image reconstruction. The goal of the project is to employ full-temporal data from a TD NIROT modality. We improved TD data-based 3D image reconstruction and compared the performance with other methods using frequency domain (FD) and temporal moments. The iterative reconstruction algorithm was evaluated in simulations with both noiseless and noisy in-silico data. In the noiseless cases, a superior image quality was achieved by the reconstruction using full temporal data, especially when dealing with inclusions at 20 mm and deeper in the tissue. When noise similar to measured data was present, the quality of the recovered image from full temporal data was no longer superior to the one obtained from the analysis of FD data and temporal moments. This indicates that denoising methods for TD data should be developed. In conclusion, TD data contain richer information and yield better image quality.

Indexed as

Tomography, OpticalTomography, X-Ray ComputedAlgorithmsComputer SimulationImage Processing, Computer-AssistedPhantoms, ImagingImage qualityImage reconstructionTime Domain Near-Infrared Optical Tomography

Identifiers

PMID37845457

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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.