REVIEW 3 major objections 5 minor 18 references
PlanTUS: A heuristic tool for prospective planning of transcranial ultrasound transducer placements
T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash
Pith's one-line read PlanTUS offers a fast, open-source way to pick ultrasound transducer placements for brain targets.
desk verdict A genuinely useful open-source TUS planning heuristic, but the 'very good overlap' claim needs quantitative validation before acceptance. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The carrying object is the idealized straight-line beam trajectory. For each scalp point, PlanTUS casts a ray perpendicular to the local skin surface; the ray's intersection length with the target mask and the tilt needed to redirect it through the target center are the primary ranking variables. Skull-thickness maps and the angular deviation between skin and skull normals act as warnings about likely aberration, and the transducer's minimum and maximum focal depth define the reachable region on the scalp. These geometric surrogates are what allow the tool to rank positions in seconds.
What would settle it
Run full-wave acoustic simulations (for example with k-Wave or BabelBrain) for both top-ranked and explicitly rejected scalp positions across a cohort of twenty or more subjects and several target regions, and test whether the simulated focus-target overlap is consistently higher for the top-ranked positions; a systematic failure of that ordering would refute the heuristic's predictive value.
Extended reading notes
Core claim
The central claim is that simple geometric quantities measured from the scalp surface can pre-screen transducer placements well enough that expensive acoustic simulations only need to be run for a handful of candidates. PlanTUS computes, for each scalp point, the distance to the target center, the length of intersection between the target mask and a straight ray launched perpendicular to the skin, the tilt angle required to make that ray cross the target center, and the local angular deviation between skin and skull surfaces, and it marks avoidance zones around the ears, eyes, nose, and air-filled sinuses. The paper argues that placements chosen from these maps yield very good overlap between the simulated acoustic focus and the target, illustrating this with examples for the nucleus accumbens, thalamus, amygdala, subgenual anterior cingulate cortex, and ventromedial prefrontal cortex. It further claims the tool detects when a target is not reachable at all with a given transducer's focal depth and steering capabilities, information that is useful before equipment is purchased or a study is designed.
Load-bearing premise
The tool's rankings rest on treating the ultrasound beam as a straight ray entering the skull perpendicular to the skin, ignoring the bending and slowing caused by skull and brain tissue; if that idealized path misrepresents the real acoustic path for a given skull region or target, the heuristic may rank placements incorrectly.
Editorial extensions
If this is right
- Researchers can quickly screen a cohort's MRI scans for whether a target is reachable with a given transducer before acquiring hardware or starting a study.
- The exported placements can be loaded directly into acoustic simulation and neuronavigation software, so the heuristic choice becomes the starting point for rigorous wave-based validation and MR-guided delivery.
- For targets or transducer configurations that are not feasible, PlanTUS can reveal that before any simulation effort is spent, as in the ventromedial prefrontal cortex example with no lateral steering.
- Maps over template or group-average heads allow feasibility checks very early in study planning, with the caveat that standard-space heads may underestimate reachability relative to individual anatomy.
Reading between the lines
- A systematic validation study that compares PlanTUS's top-ranked and bottom-ranked placements against full-wave acoustic simulations across many subjects and targets would tell how often the heuristic ordering matches true focus-target overlap.
- The same per-scalp-point scoring could be extended from single transducers to multi-transducer arrays by combining individual position costs with an objective for constructive interference at the target.
- Template-based feasibility maps are a conservative screen rather than a verdict: because the standard head is larger than typical heads, an 'unreachable' label in standard space should not be treated as definitive for an individual.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript introduces PlanTUS, an open-source heuristic tool for prospective planning of transcranial ultrasound transducer placements. Using an individual T1-weighted MRI and a target mask, together with transducer-specific parameters (focal depths, aperture, tilt limits, FWHM values), PlanTUS computes scalp-surface metrics such as target distance, intersection of an idealized straight beam with the target, required transducer tilt, skin-to-skull angular deviation, and skull thickness. It visualizes these in Connectome Workbench, exports placements to acoustic simulation and neuronavigation software, and demonstrates example use cases for nucleus accumbens, thalamus, amygdala, subgenual anterior cingulate cortex, and ventromedial prefrontal cortex, including template and HCP datasets. The authors claim that PlanTUS-selected placements 'principally result in a very good overlap between the simulated acoustic focus and brain target.'
Significance. If the central claim is supported, PlanTUS would fill a practical gap: it is fast, fully open-source, built on widely used tools (SimNIBS, FSL, FreeSurfer, Connectome Workbench), and designed to narrow the large space of possible transducer positions down to a tractable set for full-wave simulation. The compatibility with k-Plan, k-Wave, BabelBrain, Localite, and BrainSight is a concrete strength, and the feasibility-check use case on template data is a useful contribution for study planning. However, the evidence for the key claim is currently only qualitative and selected; the heuristic is explicitly acknowledged to be a simplification, and no systematic quantitative validation is provided. The significance of the tool is therefore contingent on either tightening the claim to 'heuristic pre-selection requiring simulation validation' or adding a quantitative evaluation against full-wave acoustic simulations.
major comments (3)
- [§3.2 and §4 (Conclusion)] The conclusion states that 'PlanTUS-selected transducer placements principally result in a very good overlap between the simulated acoustic focus and brain target,' but §3.2 supports this only with four hand-picked examples and visual inspection. There is no quantitative outcome measure such as Dice overlap, centroid distance, or percentage of the simulated pressure field inside the target mask, and no comparison with alternative or randomly selected placements. As written, the central claim is not supported by the reported evidence; please either provide a systematic quantitative evaluation across targets, participants, and candidate positions, or revise the conclusion to the more modest claim that PlanTUS identifies promising positions that require simulation-based validation.
- [§2.2 and §3.2] The heuristic in §2.2 uses an idealized straight beam trajectory perpendicular to the skin surface, ignoring refraction, phase aberration, and skull attenuation. The manuscript never establishes that this ray-geometric metric ranks transducer positions correctly relative to the full-wave simulated acoustic focus. The amygdala case in §3.2 is the only place where a deviation between skin and skull curvature is acknowledged, and even there the conclusion is qualitative ('the simulated acoustic focus still overlaps with the target'). The direction and magnitude of ranking errors introduced by the straight-beam assumption are unknown. Please include a quantitative comparison between the heuristic metrics (e.g., beam-target intersection, required tilt) and the simulated focus-target overlap for a range of positions, including positions where the heuristic predicts a miss or a large tilt.
- [§3.1 and §3.2 (simplified focus overlay)] The 'simplified, ideal acoustic focus' shown in the right panels of Figure 2A is derived from the same idealized geometry used for the heuristic metrics, so its overlay on the target mask cannot serve as independent evidence for the accuracy of the heuristic. Only the k-Plan simulation results (Figure 1D and the simulated pressure fields in Figure 2A) can provide such evidence. The text should clearly distinguish the ideal-focus overlay from the simulated acoustic pressure field, and the quantitative validation requested above should be based on the simulated fields.
minor comments (5)
- [§3.2 (Figure 2A)] The left metric maps in Figure 2A are informative, but please ensure that all color bars are visible, labeled with units, and use consistent scales across rows so that comparisons between targets are not misleading.
- [§2.3 (Code availability)] The code availability statement would be strengthened by providing a versioned release, a DOI, or a specific commit hash for reproducibility, in addition to the project URL.
- [References] In the reference list, the k-Wave entry is duplicated: 'Bradley E. Treeby, Treeby, B. E., Ben Cox, & Cox, B. T.' should be reformatted to a single standard citation.
- [§3.2 (fourth row)] The statement that sonication of the ventromedial prefrontal cortex is 'not possible with a transducer that has no lateral steering capabilities' is conditional on the specific transducer parameters used; please make that conditionality explicit in the main text as well as in the figure caption.
- [General] Minor typographical and formatting issues include inconsistent spacing in the abstract ('PlanTUS:' and missing spaces around references) and the absence of line numbers in the submitted PDF; these are presentation-only issues.
Circularity Check
No significant circularity: PlanTUS is a heuristic planning tool whose outputs are validated against independent acoustic simulations, and the central claim rests on illustrative examples rather than on any fit or self-citation chain.
full rationale
The paper derives no fitted parameters and makes no prediction from a fitted model. The heuristic metrics in Section 2.2 (distance, idealized beam intersection, required transducer tilt, and skin-skull angular deviation) are computed directly from the user-provided target mask and anatomical segmentation, and the same mask is used for evaluating overlap with the simulated focus; this is the definition of targeting rather than a fitted-input prediction. The validation in Sections 3.1 and 3.2 uses independently simulated acoustic pressure fields from k-Plan, as shown in Figure 1C-D and Figure 2A, so the conclusion that 'PlanTUS-selected transducer placements principally result in a very good overlap between the simulated acoustic focus and brain target' is an inductive generalization from a small set of hand-picked examples, not a mathematically forced consequence of the heuristic. The idealized straight-beam simplification and the lack of a systematic quantitative comparison with full-wave simulations are evidence-quality limitations that belong to correctness risk, not circularity. References to prior work by the authors (Murphy et al. 2025, Darmani et al. 2022) provide background on TUS factors and are not load-bearing for the tool's validity, and no uniqueness theorem is invoked. Therefore no circular step reduces the paper's central claim to its own inputs.
Assumptions & free parameters
free parameters (1)
- Transducer calibration inputs (focal depth, aperture, FLHM)
assumptions (3)
- domain assumption Charm segmentation of the T1-weighted MRI accurately extracts the skin and skull surfaces.
- domain assumption The ultrasound beam can be modeled as an idealized straight ray traveling perpendicular to the skin surface, ignoring refraction by the skull and brain tissue.
- domain assumption The simplified acoustic focus representation derived from user-supplied FLHM values adequately approximates the true focal shape for overlap evaluation.
Cite this review
Pith. "Pith review of PlanTUS: A heuristic tool for prospective planning of transcranial ultrasound transducer placements." pith.science (2026). https://pith.science/paper/NTEGOBJ5
@misc{pith2026250622563,
author = {Pith},
title = {Pith review of: PlanTUS: A heuristic tool for prospective planning of transcranial ultrasound transducer placements},
year = {2026},
howpublished = {\url{https://pith.science/paper/NTEGOBJ5}},
note = {Machine review of arXiv:2506.22563}
}
read the original abstract
Low-intensity focused transcranial ultrasonic stimulation (TUS) offers unique depth and precision in non-invasive brain stimulation. Effective administration of TUS, however, requires precise placement of transducers to ensure selective neural target exposure and engagement. This process is constrained by individual skull anatomy, hardware limitations, as well as computational and time costs of running exhaustive acoustic simulations to optimize transducer placement. To address these challenges, we introduce PlanTUS, a fast, open-source tool designed to heuristically identify feasible transducer positions, accounting for individual anatomical and hardware constraints. It visualizes relevant metrics on the scalp, such as target accessibility, required transducer tilt, and skull thickness, allowing users to identify and export potential transducer positions compatible with both acoustic simulation and neuronavigation software. In addition to individualized planning, PlanTUS facilitates feasibility evaluations during study planning, offering practical utility to researchers seeking to optimize TUS delivery with greater efficiency and precision.
Reference graph
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Reviewed August 6, 2026 · model on record in the stance chip above.
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