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Artifacts in Photoacoustic Imaging: Origins and Mitigations

T0 review · 2 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper argues that every photoacoustic imaging artifact arises from insufficient data or incorrect assumptions in reconstruction, organized into five artifact sources with simulation and clinical examples.

desk verdict Useful educational taxonomy of PAI artifacts, honestly scoped in the discussion but overbroad in title and abstract; worth publishing after a scope-claim fix. read the letter →

arxiv 2504.12772 v1 pith:225L3ROJ submitted 2025-04-17 physics.med-ph

Max T. Rietberg , Janek Gröhl , Thomas R. Else , Sarah E. Bohndiek , Srirang Manohar , Ben T. Cox This is my paper · ORCID
classification physics.med-ph
keywords photoacousticimagingartifactsartifactclassificationimagereconstructionmedicalfluencedecaylimited-viewtomographymitigation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review argues that every artifact in photoacoustic imaging has one of two root causes: the imaging system records insufficient data, or the reconstruction algorithm embeds assumptions about the physics that do not hold. Around that distinction the authors build a five-way classification by source—patient, light-tissue interactions, the photoacoustic effect, sound-tissue interactions, and signal detection—and show each artifact type with paired simulations and clinical examples. The practical goal is to give clinical users a field guide for recognizing artifacts before they cause misdiagnosis, and to give system developers a reference for which measurement or modeling gaps to close. If the classification holds, artifact recognition becomes a matter of mapping a visual signature back to a violated assumption, which in turn points to the mitigation.

What carries the argument

The organizing device is the two-cause taxonomy and the assumption list behind it. The physical backbone is the initial-pressure equation $p_0(\mathbf{x}) = \Gamma(\mathbf{x})\mu_a(\mathbf{x})\Phi(\mathbf{x})$ for light absorption and the linear acoustic wave equation for propagation, and each artifact corresponds to a violated assumption in that chain. The taxonomy maps every artifact source to a cause, such as fluence decay, spectral coloring, sound-speed mismatch, or limited view, and to a visual effect, such as dislocation, splitting, blurring, clutter, or signal loss. Because the simulations isolate one violated assumption at a time, the machinery also provides a controlled way to learn what each artifact looks like.

What would settle it

A single clinical or simulated artifact that persists when the data are complete, noise-free, and broadband and the reconstruction uses an exact physical model would falsify the two-cause claim. A more practical check is to simulate two artifacts together, such as fluence decay plus sound-speed error, and see whether their isolated signatures still predict the combined image; if they mask or transform each other, the single-cause illustrations cannot be extended directly to clinical images.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that the confusing variety of photoacoustic artifacts is not arbitrary. Every artifact is produced either because the measured data are incomplete—limited detector coverage, sparse elements, finite bandwidth, noise—or because the reconstruction model assumes something false, such as uniform sound speed, no acoustic attenuation, omnidirectional detectors, or spatially constant light fluence. The authors organize the resulting phenomena into five sources (patient, light-tissue interactions, the photoacoustic effect, sound-tissue interactions, and signal detection) and connect each to concrete assumptions in the image-formation chain $p_0(\mathbf{x}) = \Gamma(\mathbf{x})\mu_a(\mathbf{x})\Phi(\mathbf{x})$, followed by linear acoustic propagation to the detectors. Simulated phantoms that deliberately break one assumption at a time, together with in vivo examples, demonstrate the characteristic signature of each artifact.

Load-bearing premise

The whole didactic apparatus depends on whether an artifact's isolated signature in an ideal simulation still appears recognizably the same in a real clinical image, where many assumptions fail at once and the reconstruction algorithm is different.

Editorial extensions

If this is right

  • Clinicians using multispectral PAI can learn to recognize spectral coloring and out-of-plane absorption as causes of false or missing features in oxygenation maps, reducing the risk of misdiagnosis.
  • Knowing that sound-speed mismatch causes dislocation, splitting, and blurring, scanner designers can incorporate measured sound-speed maps to recover vessels that are invisible under constant-sound-speed reconstruction.
  • For hardware, the taxonomy makes trade-offs explicit: more detector coverage or denser elements reduces limited- and sparse-view clutter, while detector directivity and bandwidth set a floor on resolution that deconvolution can only partially recover.
  • Artifacts can sometimes carry diagnostic information, so identifying an artifact correctly may assist diagnosis rather than merely confound it, as with the comet-tail artifact in ultrasound.
  • Because mitigation splits into improving the model versus supplementing the data, any reconstruction algorithm, including deep learning, will still produce artifacts whenever its implicit physics model is wrong or the data are incomplete.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Extension: If the two-cause framing is right, artifact identification becomes a diagnostic inverse problem: the violated assumption could be inferred from the image itself, pointing toward automated artifact annotation tools for clinical workflows.
  • Extension: The same two-cause dichotomy likely transfers to other hybrid modalities that reconstruct from physical models, so the taxonomy may apply to ultrasound-guided or optoacoustic tomography with re-labelled sources.
  • Extension: The paper leaves reconstruction-induced artifacts out of scope, so a natural extension of the classification would add a sixth source for algorithm-specific artifacts, including deep-learning hallucinations.
  • Extension: A testable extension of the isolation protocol is to break two assumptions at once; if the resulting signatures interact nonlinearly, clinical users would need signatures learned from combined artifact conditions rather than from single-cause examples.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. This review paper provides a systematic account of image artifacts in photoacoustic imaging (PAI). It proposes that the artifacts considered arise from two fundamental causes, insufficient data and incorrect assumptions embedded in the reconstruction model, and organizes them into five sources: patient, light-tissue interactions, the photoacoustic effect, sound-tissue interactions, and signal detection. For each source, the paper explains the underlying physics, illustrates the artifact with simulations that break exactly one idealizing assumption, and provides experimental images from the literature. It closes with a discussion of advanced mitigation strategies, including improving the physics model, supplementing the data, deep learning, and deconvolution. The simulation code is provided through SIMPA, and the figures are generally clear.

Significance. If the scope issue is resolved, this will be a timely and valuable educational resource for clinical users and a useful reference for system developers. The simulation protocol is transparent, uses no fitted parameters, and is reproducible via the linked code. The simulated artifact signatures are corroborated by experimental examples from the literature, including in vivo images. The proposed taxonomy provides a helpful mental model even though, as discussed below, its claimed completeness needs to be scoped.

major comments (2)
  1. [Section 2 and Section 11] The paper frames the artifact taxonomy as covering PAI artifacts generally, with Section 2 stating that there are two corresponding classes of underlying causes (insufficient data and incorrect assumptions in the image reconstruction algorithm). Section 11, however, explicitly limits the scope to artifacts that originate outside the reconstruction step and acknowledges that reconstruction algorithms can introduce new artifacts, such as backprojection streaks, negative-value artifacts, and deep-learning hallucinations. These reconstruction-induced artifacts are not classified in the five-source taxonomy, so the classification is not exhaustive as presented. The title, abstract, and Section 2 should either incorporate reconstruction-induced artifacts or state prominently that the taxonomy covers only artifacts arising before the reconstruction step; otherwise the reader is left without an entry point for a whole class of artifacts.
  2. [Section 4] The isolation methodology in Section 4 (ideal settings except for the one difference giving rise to the artifact) is clear pedagogically, but the paper does not address how artifact signatures interact when several assumptions are violated simultaneously, which is the clinical situation. Because the stated purpose is to train clinical users to identify artifacts, the Discussion should warn explicitly that combined artifacts may not have the same visual signature as the isolated demonstrations and, ideally, include at least one simulation with multiple simultaneous violations to illustrate the interaction. The experimental examples partially mitigate this concern, but they are not a substitute for a stated caveat.
minor comments (4)
  1. [Abstract] The phrase 'assess whether their impact' is incomplete; the sentence needs an object, e.g., 'assess whether their impact is clinically significant'.
  2. [Section 9.1] The text 'For a typical resolution of 100 µs' should read '100 µm'; the current wording mixes time and length units in the motion estimate.
  3. [Table 1] Assumption H7 is listed as 'Detectors are perfectly directional,' but Section 8.2.1 states the ideal assumption is omnidirectionality; the table wording should be corrected to avoid contradiction.
  4. [Section 4] The simulation volume is described with '0.25 mm/pixel'; for a 3D volume this should be '0.25 mm/voxel'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a self-contained educational taxonomy with no derived predictions or fitted parameters.

full rationale

This manuscript is an expository review that organizes known photoacoustic imaging artifacts into a taxonomy by source and cause. It introduces no fitted parameters, no self-derived constants, no inversion formula, and no quantitative prediction whose output is defined as its input. The central classification (Section 2) groups artifacts by their physical origin, while Section 4 explicitly states that the simulations isolate each artifact by deliberately breaking exactly one reconstruction assumption, which is a didactic construction rather than a claimed derivation. The paper's two-cause framing (insufficient data and incorrect reconstruction assumptions) is a high-level conceptual statement, not a mathematical result that could reduce to itself. Self-citations such as Dantuma et al. 2023 (Ref. 33) and Vogt et al. 2019 (Ref. 29) are used only as illustrative experimental examples of artifacts already demonstrated by the authors' own simulations; the taxonomy does not depend on any uniqueness theorem or load-bearing self-cited result. Section 11's explicit limitation that reconstruction-induced artifacts are outside scope is an honest scoping statement, not a circular step, and it does not undermine the internal consistency of the taxonomy as presented for its stated scope. No equation in the paper is equivalent by construction to any other equation, and no fitted value is renamed as a prediction. The paper is therefore self-contained against external benchmarks and exhibits no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim is a taxonomy of artifact sources, not a quantitative derivation. It rests on standard photoacoustic physics: the radiative transfer equation, the initial pressure relation p0 = Γ μa Φ, and the linear acoustic wave equation, all cited from the standard literature. The only non-standard premise is the didactic choice of isolating one broken assumption at a time in simulations, stated in Section 4. No numbers are fitted to data, and no new entities are introduced.

assumptions (4)
  • standard math Photoacoustic pressure is given by p0 = Γ(x) μa(x) Φ(x), where Γ is the Gruneisen parameter.
    Eq. 3; standard photoacoustic physics, referenced to Wang & Wu [5] and used throughout the artifact descriptions.
  • standard math The radiative transfer equation (Eq. 1) describes light transport in tissue.
    Section 3.1, Eq. 1; cited to Sandell & Zhu [24]. Standard model for light propagation in scattering media.
  • domain assumption Acoustic propagation obeys the linear wave equation (Eq. 5) with homogeneous initial conditions (Eq. 6).
    Section 3.3, Eqs. 5-6; assumes linear acoustics and no acoustic absorption in ideal simulations.
  • domain assumption Each artifact can be isolated by breaking exactly one assumption in an otherwise ideal simulation.
    Section 4: 'In order to isolate particular artifacts, the simulations are (unless otherwise noted) performed in ideal settings, but for the one difference giving rise to the artifact.' This is the methodological premise behind all figures; real imaging has multiple assumptions failing simultaneously.

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Cite this review

Pith. "Pith review of Artifacts in Photoacoustic Imaging: Origins and Mitigations." pith.science (2026). https://pith.science/paper/225L3ROJ

@misc{pith2026250412772,
  author       = {Pith},
  title        = {Pith review of: Artifacts in Photoacoustic Imaging: Origins and Mitigations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/225L3ROJ}},
  note         = {Machine review of arXiv:2504.12772}
}
read the original abstract

Photoacoustic imaging (PAI) is rapidly moving from the laboratory to the clinic, increasing the need to understand confounders which might adversely affect patient care. Over the past five years, landmark studies have shown the clinical utility of PAI, leading to regulatory approval of several devices. In this article, we describe the various causes of artifacts in PAI, providing schematic overviews and practical examples, simulated as well as experimental. This work serves two purposes: (1) educating clinical users to identify artifacts, understand their causes, and assess whether their impact, and (2) providing a reference of the limitations of current systems for those working to improve them. We explain how two aspects of PAI systems lead to artifacts: their inability to measure complete data sets, and embedded assumptions during reconstruction. We describe the physics underlying PAI, and propose a classification of the artifacts. The paper concludes by discussing possible advanced mitigation strategies.

Figures

Figures reproduced from arXiv: 2504.12772 by the authors.

Figure 1
Figure 1. Overview of the artifacts treated in this paper. They are arranged by the point at which they occur throughout the photoacoustic (PA) signal generation process: induced by (1) the patient or by the preparation of the patient; (2) the interactions of light with tissue; (3) the conversion of heat into sound energy; (4) the interactions of sound with tissue; (5) the detection of the sound waves. 4 [PITH_FULL_IMAGE:fig… view at source ↗
Figure 2
Figure 2. A visual guide of how we will showcase visual explanations of the artifacts. In this example, three vessels are used and we typically showcase different types of views. A: the segmentation map (a segmented color bar shows the considered tissue types). B: the absorption coefficient in a gray-scale color map. C: the initial pressure and in D: the reconstructed image, both with a pseudo-colored visualization using the … view at source ↗
Figure 3
Figure 3. Fluence decay artifact: leads signal loss with depth . Several blood vessels are embedded in soft tissue, with increasing distance from the illumination source (increasing depth). A: their absorption coefficients. B: The fluence decays due to absorption from both the background and the blood vessels themselves, resulting in differing PA signal levels in the reconstruction. C: vertical intensity profiles taken from t… view at source ↗
Figures from the paper (16 more)
Figure 4
Figure 4. Figure 4: Spectral coloring artifact: leads to unexpected signal difference between wavelengths. 3 blood vessels are embedded in soft tissue, under identical settings, except the optical wavelength changes (A&D: 500 nm, B&E, 763 nm). A&B: absorption coefficients. D&E: reconstruc…
Figure 5
Figure 5. Figure 5: Out-of-plane absorption artifact: leads to clutter. The assumption is that only tissue in the imaging plane or volume is illuminated, as in the case in A&D, where 3 spheres are imaged. The second column (B&E) shows the same case, but now with a 4th object outside the i…
Figure 6
Figure 6. Figure 6: Laser power variation artifact: leads to unexpected signal difference. It is often assumed that the laser power is constant between measurements, which might not always be the case. Three blood vessels are measured in identical settings, except the laser power varies, …
Figure 7
Figure 7. Figure 7: Long pulse duration artifact: leads to blurring. If the laser pulse is not short enough, confinement conditions are violated and PA signal is distorted. A: when imaging with a short enough pulse, the reconstruction is not distorted, B: increasing the pulse length will …
Figure 8
Figure 8. Figure 8: PA efficiency artifact: leads to unexpected signal difference. Often, PA efficiency is assumed to be homoge￾nous, but this may not always be the case. A: a PA image of a fat blob (left) and blood vessel (right) with heterogeneous PA efficiency coefficient (0.8 for fat,…
Figure 9
Figure 9. Figure 9: Sound speed artifact: leads to dislocation, splitting, and blurring. A&B: Simulation showing the effect of a sound speed heterogeneity when imaging a vascular network assuming constant background sound speed. A: Schematic of the vascular target with background sound sp…
Figure 10
Figure 10. Figure 10: Acoustic reflections artifact: leads to clutter. A: a schematic of a vascular target with a background sound speed of 1540 m/s and a heterogeneity (yellow disc, 1800 m/s). B: the image corresponding to A, reconstructed assuming a constant sound speed and therefore sho…
Figure 11
Figure 11. Figure 11: Acoustic attenuation artifact: leads signal loss with depth. A: Schematic of a vascular network in tissue which is homogeneous except for a region with higher absorption (yellow). B: Initial pressure, i.e. before acoustic absorption, C: Image reconstructed while there…
Figure 12
Figure 12. Figure 12: Example visualisation of various typical detector configurations for PAI devices. Three blood vessels are shown and images are reconstructed from simulations of the following detector configurations: A: a full-ring array, B: a sparse full-ring array, C: a semi-circula…
Figure 13
Figure 13. Figure 13: Limited view artifact: leads to clutter and signal loss. A vascular network is simulated under identical settings, except the detector opening angle of a spherical detector changes. As the detection region (the region that is enclosed by the detectors) becomes smaller…
Figure 14
Figure 14. Figure 14: Sparse view artifact: leads to clutter and signal loss. A vascular network is imaged under identical settings, except for the detector density changes. As the number of detectors becomes smaller (A: 298, B: 148, C: 74, D: 28) the image quality increasingly drops. The …
Figure 15
Figure 15. Figure 15: Limited temporal sampling artifact: leads to blurring, clutter and signal loss. 3 blood vessels are imaged under identical settings, except the temporal sampling rate changes. As the Nyquist sampling theorem is increasingly violated (A: 40MHz, B: 1MHz, C: 0.5 MHz), ar…
Figure 16
Figure 16. Figure 16: Limited frequency response artifact: leads to blurring, clutter and signal loss. A vascular network is imaged under identical settings, except the detector bandwidth changes. Transducers have a limited bandwidth, here modeled as a frequency domain Gaussian filter with…
Figure 17
Figure 17. Figure 17: Measurement noise artifact: leads to signal loss at low SNR, and clutter. A vascular network is imaged under identical settings, except noise is introduced in B and C. B: small clusters of blood are introduced (e.g. due to microbleeds), C: noise (zero-mean additive Ga…
Figure 18
Figure 18. Figure 18: Patient movement artifact: leads to clutter and blurring. Measurements are often repeated, e.g. to average and reduce random noise. The assumption is that the patient is stable and does not move. A: the assumption holds, where 3 (identical) time series of the vascular…
Figure 19
Figure 19. Figure 19: Patient preparation artifact: leads to clutter. A vascular network is imaged under identical settings, except for changes in quality of patient preparation. B: shows hairs that should have been removed before measurement, resulting in strong absorption and decreased i…

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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. Digital twins enable full-reference quality assessment of photoacoustic image reconstructions

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    Digital twins provide a simulated reference that enables full-reference quality comparison of photoacoustic image reconstructions on experimental data.

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.