Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2604.06280

DosimeTron: Automating Personalized Monte Carlo Radiation Dosimetry in PET/CT with Agentic AI

Eleftherios Tzanis·Michail E. Klontzas·Antonios Tzortzakakis
View original source ↗Read source document ↗Export metadata ↓

Abstract

Purpose: To develop and evaluate DosimeTron, an agentic AI system for automated patient-specific MC internal radiation dosimetry in PET/CT examinations. Materials and Methods: In this retrospective study, DosimeTron was evaluated on a publicly available PSMA-PET/CT dataset comprising 597 studies from 378 male patients acquired on three scanner models (18-F, n = 369; 68-Ga, n = 228). The system uses GPT-5.2 as its reasoning engine and 23 tools exposed via four Model Context Protocol servers, automating DICOM metadata extraction, image preprocessing, MC simulation, organ segmentation, and dosimetric reporting through natural-language interaction. Agentic performance was assessed using diverse prompt templates spanning single-turn instructions of varying specificity and multi-turn conversational exchanges, monitored via OpenTelemetry traces. Dosimetric accuracy was validated against OpenDose3D across 114 cases and 22 organs using Pearson's r, Lin's concordance correlation coefficient (CCC), and Bland-Altman analysis. Results: Across all prompt templates and all runs, no execution failures, pipeline errors, or hallucinated outputs were observed. Pearson's r ranged from 0.965 to 1.000 (median 0.997; all p < 0.001) and CCC from 0.963 to 1.000 (median 0.996). Mean absolute percentage difference was below 5% for 19 of 22 organs (median 2.5%). Total per-study processing time (SD) was 32.3 (6.0) minutes. Conclusion: DosimeTron autonomously executed complex dosimetry pipelines across diverse prompt configurations and achieved high dosimetric agreement with OpenDose3D at clinically acceptable processing times, demonstrating the feasibility of agentic AI for patient-specific Monte Carlo dosimetry in PET/CT.

Explore related subjects

physics.med-phcs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Eleftherios Tzanis, Michail E. Klontzas, Antonios Tzortzakakis. 2026-04-07. DosimeTron: Automating Personalized Monte Carlo Radiation Dosimetry in PET/CT with Agentic AI. https://arxiv.org/abs/2604.06280

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A digital-twin framework for forecasting treatment-day imaging with contour uncertainty in adaptive proton radiotherapy

Head-and-neck anatomy changes over a six-to-seven-week proton course, and the anatomy of a later week cannot be imaged when the plan is made. We present a digital-twin framework that forecasts a patient's treatment-day anatomy as an ensemble of predicted CTs with propagated contours and quantifies the uncertainty of the forecast contours. The twin is a library of previously treated patients with planning and weekly quality-assurance CTs (QACTs), made patient-specific by a two-step foundation-model deformable registration: a cross-patient field carries each library patient onto the current patient, and a longitudinal field, estimated in the current patient's frame, carries that patient's planning-to-QACT change onto the current patient's own planning CT. A library of 302 observations from 88 patients yields about 300 replicates per patient, each a deformation that occurred in a treated patient. The dispersion of the propagated contours, resolved by outward normal, is six-direction contour uncertainty in millimeters. This is uncertainty in the input to the forecast, which library patient the current patient follows, rather than in model parameters, and it is unchanged when the registration engine is exchanged. On ten patients with clinician contours on two QACTs, the library alone fixes the anisotropic shape of the uncertainty (4.5 to 6.2 mm); the first QACT narrows it by a factor of 3.2 to 3.6 without a contour being drawn; an approved contour improves the center but not the width. The estimate orders directions correctly but is not Gaussian-calibrated. A clinical target volume expansion is worked out as one application.

physics.med-ph↗
arXiv2026

On phase aberration estimation using common mid-angle speckle correlations

Phase aberrations, despite degrading ultrasound images, also encode valuable information about the spatial distribution of the speed of sound in tissue. In pulse-echo ultrasound, we can quantify them by exploiting speckle correlations. Among existing strategies, correlations between steered acquisitions that share a common mid-angle have proven particularly effective for inferring the speed of sound. Their phases can be linearly related to the phase aberrations undergone by both the incident and reflected wavefronts. This relationship has so far been demonstrated only through geometric arguments based on point reflectors. Here, we develop a rigorous theoretical formalism that extends this relationship to the speckle regime, completing the previously established linear model and clarifying its underlying assumptions. More importantly, we build on this formalism to analyze correlation-phase fluctuations arising from aberration-induced speckle decorrelation. The analysis reveals that phase variance is governed by the relative loss of coherence, which increases approximately linearly with the square of the correlation phases. Local correlation-phase estimates therefore become increasingly uncertain as their magnitude grows. Experimental measurements in a uniform tissue-mimicking phantom show excellent agreement with the predicted variance. Beyond providing a theoretical basis for advancing speed-of-sound imaging, this formalism establishes the accuracy limit of common-mid-angle correlation phases, offering a benchmark for evaluating more advanced aberration-estimation techniques.

physics.med-ph↗
arXiv2026

Buccal-Lingual Analysis and Inflammation Tracking in Oral Soft Tissues Using Quantitative Ultrasound: A Preclinical Study

Four out of 10 adults aged 30 years or older in the USA are impacted by periodontal diseases which span a spectrum of inflammatory conditions. Currently, a subjective, invasive and a semi-quantitative approach, termed bleeding on probing, is employed in clinics for inflammation assessment. The long-term goal of this study is to fill the current clinical diagnostic gap in dentistry by proposing quantitative ultrasound (QUS)-based biomarkers for inflammation diagnosis and monitoring. Here, as one of the early works in this area, we investigated two QUS parameters for characterizing periodontal inflammation in gingival tissues using a longitudinal preclinical porcine study: attenuation coefficient slope (ACS) and backscatter intensity (BSI). Our study included eight pigs imaged intraorally (24 MHz) at four bi-weekly timepoints from week 0 (healthy) to week 6 (post inflammation inoculation) at their interproximal sites of the third premolars (PM3-Mes) from all quadrants. Moreover, we compared gingival tissues surrounding a tooth at lingual/palatal side versus buccal side at the second molar (M2-Dis) in healthy condition. Our results showed that gingival ACS at all inflammation timepoints were significantly lower than healthy gingival ACS (1.69 \pm 0.53 dB/cm.MHz) using a mixed effect analysis (week 6: 1.08 \pm 0.25 dB/cm.MHz). Longitudinal comparison of BSI did not demonstrate any statistical significance. Gingival ACS and BSI at lingual/palatal versus buccal sides of M2-Dis did not exhibit any statistical significance. These ACSs were linearly correlated with R-squared = 0.89 and a correlation slope of 0.87. For BSI, Bland-Altman analysis demonstrated no difference in mean BSI, although variability was high. These findings highlight the promising potential of ultrasonography paired with QUS to complement current standard of care in dentistry.

physics.med-ph↗