Evidence map›Paper›PMID 40487237›Full record

ArticlePhotoacoustics2025

Scale-equivariant deep model-based optoacoustic image reconstruction.

Christoph Dehner, Ledia Lilaj, Vasilis Ntziachristos, Guillaume Zahnd, Dominik Jüstel

Abstract read
In one paragraph

Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Christoph DehneriThera Medical GmbH, Munich, Germany.
Ledia LilajiThera Medical GmbH, Munich, Germany.
Vasilis NtziachristosInstitute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany.
Guillaume ZahndiThera Medical GmbH, Munich, Germany.
Dominik JüstelInstitute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Model-based reconstruction provides state-of-the-art image quality for multispectral optoacoustic tomography. However, optimal regularization of in vivo data necessitates scan-specific adjustments of the regularization strength to compensate for fluctuations of the signal magnitudes between different sinograms. Magnitude fluctuations within in vivo data also pose a challenge for supervised deep learning of a model-based reconstruction operator, as training data must cover the complete range of expected signal magnitudes. In this work, we derive a scale-equivariant model-based reconstruction operator that

Indexed as

Model-based reconstructionOptoacoustic imagingRegularizationScale-equivariance

Identifiers

PMID40487237
PMCPMC12143758

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.