arXiv subjects

Attila Simkó

Publications and source records attributed to Attila Simkó.

5 recordsLinked to original sources

arXiv2026

PyDoseRT Proton: A GPU Pencil-Beam Engine with a Convolutional Residual-Correction Network for Fast Proton Dose Calculation

Architecture category. Hybrid method: a physics-based analytical pencil-beam (PB) dose engine followed by a 3-D convolutional residual-correction network (RepVGG-U-Net). We addressed the DoseRAD2026 proton dose-prediction task with PyDoseRT Proton, a GPU-accelerated engine implemented in PyTorch and augmented by a learned residual toward Monte Carlo (MC) accuracy. A double-Gaussian PB kernel was calibrated to GATE/Geant4 integrated depth doses in water in two stages: a classical per-energy curve fit, then a gradient-based fit of the full 3-D dose through the PyTorch physics engine as it retains a differentiable execution path for gradient-based optimization of dose-dependent objectives. The engine computes each beamlet on a beam's-eye-view (BEV) lattice with variance-preserving Gaussian splitting, an analytic nuclear halo, and a Fermi-Eyges heterogeneity term, then rotates the result into the patient frame. Additionally, a compact residual U-Net predicts an additive correction in BEV space. It is conditioned on voxelwise material-label embeddings, a discrete energy embedding and spot size. The same model was used for all anatomical sites (thoracic and abdominal). It was trained with a patient-space L1 objective emphasizing the scored high-dose region and multi-scale BEV deep supervision. The submitted CT configuration obtained preliminary-test beamlet MAE 0.0066, image-z IDD distance 0.0025, plan MAE 0.0049, 98.30\% gamma pass rate (1\%/1 mm), and DVH error 0.460.

physics.med-ph↗

arXiv2026

PyDoseRT Photon: Physics-Guided Pencil-Beam Dose Calculation with Neural Priors and Residual Correction for CT and MRI

We present a hybrid, physics-based analytical pencil-beam (PB) dose engine, augmented by two small frozen neural physics priors, followed by a 3-D convolutional residual-correction network (U-Net). We address the DoseRAD2026 (https://doserad2026.grand-challenge.org/) photon dose-prediction task with PyDoseRT Photon, a GPU PB engine implemented in PyTorch and corrected toward Monte Carlo (MC) accuracy in three learned stages of decreasing physical specificity. The engine reproduces the challenge's head-less MC source exactly where it can and models the patient with a beam-quality-indexed pencil kernel evaluated at the field's fluence-weighted radiological depth, and TERMA source scaling at the interaction site. Two tiny neural priors are trained through the frozen engine and then frozen themselves: a 39k-parameter 2-D fluence correction and a 48-parameter lateral heterogeneity correction mixing mass-conserving Gaussian redistribution operators. A compact 3-D U-Net (1.36M parameters) with a sequential refinement branch then predicts, per control point (CP) in the beam's-eye-view (BEV) frame, a bounded multiplicative gain and additive residual from seven channels. All learned stages are zero-initialized, so training starts from the analytical solution. For MRI, an nnU-Net regression model synthesizes a CT that enters the identical pipeline, with consecutively, the same trained corrector as the CT track. Design choices were driven by the challenge ranking, in which runtime carries double weight. The submitted method evaluated on our local CT and MR validation dataset achieved CP MAE 0.0086 and 0.0098, IDD distance 0.0011 and 0.0013, plan MAE 0.0024 and 0.0047, gamma pass rate (1%/1mm) 99.12% and 96.95%, with runtimes of 46s and 49s, respectively.

physics.med-ph↗

arXiv2026

MirrorNet: Can Medical Image Anonymization Really Protect Patient Identity?

Medical images are routinely de-identified---names, dates, and other metadata removed---and then shared for research, teaching, and public benchmarks under the assumption that this renders them anonymous. Such de-identification protects the metadata but not the pixels, and---apart from scans that directly contain facial structures---whether the image content itself identifies the patient has received little scrutiny. We investigate this question by learning a cycle-consistent correspondence between a cross-sectional medical image and a non-medical, patient-identifying image, using a pair of coupled, cycle-consistent variational autoencoders. From a held-out scan, the model recovers a recognisable likeness of the patient (identity-region MAE = 0.163); conversely, it synthesises a scan from such an image. These results indicate that a de-identified medical scan remains identifying---it is, in effect, a photograph of the patient---and that imaging data should be governed as biometric data rather than as anonymisable records. To support reproducibility, the code and trained models are shared at https://github.com/attilasimko/public-repository.

cs.CV↗

arXiv2025

A physics-informed, plug-and-play dose engine for gradient-based radiotherapy treatment planning

Radiotherapy treatment planning remains a time-intensive iterative process requiring expert intervention in commercial treatment planning system (TPS). While machine learning approaches have demonstrated promise, most remain depedent on TPS-based dose calculation or surrogate dose models, preventing direct optimization of deliverable treatment plan parameters. We propose PyDoseRT (PDRT), a physics-informed, GPU-accelerated dose engine implemented in PyTorch that computes dose distributions directly from treatment delivery parameters (i.e., MLC leaf positions, jaw positions, gantry angles, and monitor units). The engine preserves gradient information throughout the dose computation pipeline, enabling gradient-based optimization of hardware-constrained treatment plans without the reliance on a commercial TPS. PDRT was evaluated on 19 and 162 clinical VMAT prostate cancer plans from two hospitals (with different treatment machines). When recalculating clinical plans, PDRT achieved high 3D gamma pass rates (mean 96.8% for 2%/2 mm and 98.9% for 3%/3 mm, depending on cohort). All optimized plans converged to clinically acceptable solutions and passed deliverability verification when imported into a commercial TPS. This physics-informed framework eliminates TPS dependency for radiotherapy optimization research by enabling gradient-based planning while ensuring that delivery parameters remain in the machine-feasible range. The gradient-enabled dose engine allows exploration of novel optimization strategies and objective functions while maintaining clinical validity. The proposed approach provides a research platform for investigating real-time adaptive radiotherapy concepts, automated planning workflows, and TPS-independent optimization strategies, and democratizing radiotherapy research, by exposing gradient-enabled, hardware-aware, open-source dose computation.

physics.med-ph↗

arXiv2019

End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation

Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within the scope of the Kidney Tumor Segmentation Challenge 2019, that is aiming at combined kidney and tumor segmentation, this work proposes a novel combination of 3D U-Nets---collectively denoted TuNet---utilizing the resulting kidney masks for the consecutive tumor segmentation. The proposed method achieves a Sørensen-Dice coefficient score of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a five-fold cross-validation on the 210 patients available in the data.

eess.IV↗