Pith. sign in

REVIEW 1 cited by

Model-based reconstructions for quantitative imaging in photoacoustic tomography

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2311.15735 v1 pith:7VJ7C7E3 submitted 2023-11-27 physics.med-ph cs.NAeess.IVmath.NA

classification physics.med-phcs.NAeess.IVmath.NA
keywords photoacousticquantitativeapproachesreconstructionrecoverytomographyacousticgive
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The reconstruction task in photoacoustic tomography can vary a lot depending on measured targets, geometry, and especially the quantity we want to recover. Specifically, as the signal is generated due to the coupling of light and sound by the photoacoustic effect, we have the possibility to recover acoustic as well as optical tissue parameters. This is referred to as quantitative imaging, i.e, correct recovery of physical parameters and not just a qualitative image. In this chapter, we aim to give an overview on established reconstruction techniques in photoacoustic tomography. We start with modelling of the optical and acoustic phenomena, necessary for a reliable recovery of quantitative values. Furthermore, we give an overview of approaches for the tomographic reconstruction problem with an emphasis on the recovery of quantitative values, from direct and fast analytic approaches to computationally involved optimisation based techniques and recent data-driven approaches.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis

    cs.AI 2025-07 conditional novelty 4.0 of 10

    A prompt-plus-knowledge-graph framework for legal dispute analysis reports improved LLM sensitivity and citation accuracy on a 100-pair test set, but with limited statistical support.

Pith tools