Advanced Radiotherapy Techniques

Latest papers

• Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring

  Galib Ahmed, Istiak Ahmed, Aritra Islam Saswato, Asib Mostakim Fony, Kazi Shahriar Sanjid, Md. Tanzim Hossain, Md. Anwarul Islam, Md. Nishan Khan, Md. Misbah Khan, Labiba Faiza Karim, Jobaer Rahman, S M Hasibul Hoque, Rahnuma Shahrin Rista, Kamruzzaman Rumman, Md Arifur Rahman, Syed Md. Akram Hussain, Mohammad Ashrafuzzaman Khan, M. Monir Uddin · 16 September 2026

  We developed DAMM-Net++, a 2.5D architecture for thoracic OAR and target volume segmentation that addresses three persistent challenges in radiotherapy auto-contouring: inter-slice surface incoherence, systematic failure on small low-contrast targets, and the absence of per-case reliability signals.…

• SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation

  Tomas Guija-Valiente, Blanca Rodriguez-Gonzalez, Norberto Malpica · 9 September 2026

  In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, current scenarios often rely on simplified perturbations that poorly capture complex, patient-specific variability. We propose SynthRCT, a scalable cond…

• BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstruction

  Chen Cheng, Michael Ferraro, James Grover, David E J Waddington, Emily Hewson · 7 September 2026

  To enable accurate and rapid photon control point and proton beamlet dose calculation in the DoseRAD2026 challenge, we present BEAM3R, a dose estimation framework operating in beam's-eye-view (BEV). Our core innovation combines a Mamba-3 state-space depth-sequence core with physics-based transport c…

• Improving Clinical Target Volume Segmentation Accuracy using Anatomical Priors and Active Learning for the AGITG TOPGEAR Clinical Trial

  Phillip Chlap, Mark Lee, Trevor Leong, Matthew Field, Jason Dowling, Hang Min, Julie Chu, Jennifer Tan, Phillip K. Tran, Tomas Kron, Annette Haworth, Martin A. Ebert, Shalini K. Vinod, Lois Holloway · 4 September 2026

  Training deep learning-based medical image segmentation models is challenging with limited curated datasets. For AGITG TOPGEAR, a gastric cancer trial, the Clinical Target Volume (CTV) is complex and defined by multiple anatomical landmarks, making upfront training data preparation difficult for an …

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

  Lukas Zimmermann, Hermann Fuchs, Attila Simk\'o, Gerd Heilemann · 2 September 2026

  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…

• DoseBridge: Denoising Diffusion Bridge Model for Dose Prediction in Lung Intensity-Modulated Proton Therapy

  Zerun Zhang, Xiaoda Cong, Xiangkun Xu, Peter Y. Chen, Xuanfeng Ding · 12 August 2026

  Most radiotherapy dose-prediction models use only CT images and anatomical structures, although intensity-modulated proton therapy (IMPT) dose also depends strongly on beam geometry and available clinical datasets are often small. We present DoseBridge, a denoising diffusion bridge model that uses t…

• Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation

  Alzahra Altalib, Chunhui Li, Christopher Hamill Taylor, Sankar Pillai, Alessandro Perelli · 11 August 2026

  During standard radiotherapy planning, repeated CT acquisitions are often required for patient registration, verification, and adaptive planning, resulting in increased cumulative X-ray dose. To mitigate this, low-dose cone-beam CT (CBCT) is routinely acquired during treatment delivery. However, CBC…

• Agentic AI-driven Immersive Simulation: A Knowledge-Aware Virtual Training Platform forHigh Dose Rate (HDR) Brachytherapy

  Ronghua Xu, Kepha Barasa, Manoj Kumal, Xinyun Liu, Weihua Zhou, Xin Qian · 11 August 2026

  The convergence of the Metaverse and Large Language Model (LLM)-based AI agent is catalyzing a shift toward autonomous, immersive, and personalized pedagogical frameworks in medical education. This paper presents a novel agentic AI-driven immersive simulation specifically designed for High Dose Rate…

• Automatic Patient-Specific Microwave Ablation Planning Accelerated by a Physics-Guided Deep Learning Model

  Seonaeng Cho, Minjee Seo, Minju Seol, Juil Park, Joon Ho Kwon, Kyungho Yoon · 5 August 2026

  Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its therapeutic outcome strongly depends on patient-specific planning of antenna insertion trajectory, power, and treatment duration. Accurate numerical simulation can provide physically reliable ablation pred…

• Anticipatory Digital Twins for Online Head-and-Neck Adaptive Proton Therapy via Foundation-Model Registration

  Yizhou Wu, Yuheng Li, Xiaofeng Yang, Chih-Wei Chang · 4 August 2026

  Head-and-neck (HN) proton therapy is highly sensitive to anatomical change over a 4-to-6-week course, as tumor shrinkage, weight loss, and setup variation can misposition the Bragg peak near critical organs such as the parotids, oral cavity, brainstem, and spinal cord, leading to target underdosing …

• Effect of User-Prompted Priors on Semi-Automated Cancer Lesion Segmentation in Whole-Body Computed Tomography

  Isac Stark, Johan Öfverstedt, Elin Lundström, Simon Ekström, Håkan Ahlström, Joel Kullberg · 28 July 2026

  In clinical oncology studies, metastatic cancer is commonly evaluated using "Response Evaluation Criteria in Solid Tumors" (RECIST), in which the diameter of up to five lesions is measured and followed over the course of treatment. However, RECIST shows limited correlation with overall survival. Tot…

• From Data Completeness to Data Sufficiency: A Task-Driven Imaging Framework for Intraoperative CBCT under Quality-Time-Dose Trade-offs

  Yi Jia, Rongjun Ge, Yang Chen, Yan Xi, Wenjun Xia · 9 July 2026

  Mobile C-arm cone-beam computed tomography (CBCT) has been widely used for real-time intraoperative 3D imaging. However, current practice often mechanically applies the fan-beam CT criterion of "180° plus fan angle" in pursuit of "data completeness" in reconstruction. This review argues that, under …

• WING: A Window-Prior-Based Generative Network with Gated Inception for Cross-Modality CT Synthesis

  Siyuan Mei, Yan Xia, Yipeng Sun, Siming Bayer, Zirong Li, Chengze Ye, Daiqi Liu, Fuxin Fan, Yixing Huang, Andreas Maier · 8 July 2026

  Generating CT volumes from MRI and CBCT can improve treatment planning in adaptive radiotherapy while avoiding additional radiation exposure. However, direct regression of CT intensities is challenged by the inherently high dynamic range and long-tailed distributions, thereby averaging out sparse ye…

• Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI

  James Grover, Emily A. Hewson, Andrew Phair, Michael Ferraro, Hilary L. Byrne, Paul Keall, Michael G. Jameson, David E. J. Waddington · 2 July 2026

  Image-guided therapies, including radiotherapy, biopsy and deep brain stimulation, rely on real-time targeting of anatomical structures. However, in the presence of motion, imaging latencies create a temporal misalignment between observed and true anatomy, compromising treatment accuracy. Artificial…

• Reliability-Aware CT-MRI Registration: A Quality Engineering Framework with Stability Analysis and Risk Classification

  Nisreen Albzour · 2 July 2026

  Multimodal CT-MRI registration is central to image-guided radiotherapy, surgical navigation, and diagnostic workflows, but most pipelines report only aggregate quality metrics without per-case reliability signals. We propose a reliability-aware framework that converts registration quality into Green…

• Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images

  Changwoo Song · 16 June 2026

  Four-dimensional computed tomography (4DCT) captures the full respiratory cycle of thoracic anatomy, yet current Internal Target Volume contouring workflows process each phase in isolation, discarding temporal coherence and leaving contours vulnerable to phase-specific artifacts. We present a lightw…

• Catching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning

  Mustafa Kadhim, Viktor Rogowski, Emilia Persson, Camila Gonzalez, Andr\'e Haraldsson, Sofie Ceberg, Mikael Nilsson, Malin K\"ugele, Sven B\"ack, Christian Jamtheim Gustafsson · 15 June 2026

  Artificial intelligence is increasingly integrated into radiotherapy workflows, yet such pipelines remain vulnerable to out-of-distribution image data that may introduce unexpected behavior in clinical tasks. Deep learning-based anomaly detection for pelvic magnetic resonance imaging (MRI) remains l…

• An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer

  Cedric Hemon, Delphine Lebret, Jean-Claude Nunes, Valentin Boussot, Karine Peignaux, Nathalie Mesgouez-Nebout, Chantal Hanzen, Antoine Simon, Anaïs Barateau, Renaud de Crevoisier, Caroline Lafond · 10 June 2026

  Background and purpose: oART enables daily plan adaptation to interfraction anatomical variations, but cumulative dose estimation remains limited by DIR, segmentation, and anatomical uncertainties. We introduce IMPACT-DoseAcc, an uncertainty-aware dose accumulation framework, within IMPACT for seman…

• A practical probabilistic framework for deformable image registration uncertainty in radiotherapy dose propagation

  Stefan Heldmann, Sven Kuckertz, Nasim Givehchi, Thomas Coradi, Mikel Byrne, Ben Archibald-Heeren, Nils Papenberg · 9 June 2026

  Deformable image registration (DIR) is widely used in radiotherapy for dose propagation and accumulation, but uncertainty in the underlying deformation can substantially affect clinically relevant dose estimates. We present a practical probabilistic framework for propagating DIR uncertainty to voxel…

• Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement

  Lixuan Chen, Zhongnan Liu, Jesse Hamilton, James M. Balter, Jeong Joon Park, Liyue Shen · 4 June 2026

  Prospective reconstruction is crucial in many clinical applications such as MRI-guided radiotherapy, which demands accurate image reconstruction and fast motion estimation from currently acquired measurements. However, prospective reconstruction remains challenging due to ultra-sparse sampling and s…

• A Fast and Generic Energy-Shifting Transformer for Hybrid Monte Carlo Radiotherapy Calculation

  Chi-Hieu Pham, Didier Benoit, Vincent Bourbonne, Ulrike Schick, Dimitris Visvikis, Julien Bert · 27 May 2026

  We introduce a novel learning framework for accelerated Monte Carlo (MC) dose calculation termed Energy-Shifting. This approach leverages deep learning to synthesize highly complex polyenergetic dose distributions directly from simple monoenergetic inputs under identical beam configurations. Unlike …

• Catching MRI outliers: unsupervised detection and localization of MRI artefacts and clinical anomalies using deep learning

  Mustafa Kadhim, Viktor Rogowski, Emilia Persson, Camila Gonzalez, Andr\'e Haraldsson, Sofie Ceberg, Mikael Nilsson, Malin K\"ugele, Sven B\"ack, Christian Jamtheim Gustafsson · 26 May 2026

  Artificial intelligence is increasingly integrated into radiotherapy workflows, yet such pipelines remain vulnerable to out-of-distribution image data that may introduce unexpected behavior in clinical tasks. Deep learning-based anomaly detection for pelvic magnetic resonance imaging (MRI) remains l…

• Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization

  Isabella Poles, Simon Arberet, Riqiang Gao, Martin Kraus, Marco D. Santambrogio, Florin C. Ghesu, Ali Kamen, Dorin Comaniciu · 14 May 2026

  Volumetric Modulated Arc Therapy (VMAT) is a cornerstone of modern radiation therapy, enabling highly conformal tumor irradiation and healthy-tissue sparing. Yet, its planning solves inverse and nested optimization for multi-leaf collimators, monitor units and dose parameters, while enforcing their …

• Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report

  Viktor Rogowski, Maarten L. Terpstra, Niklas Wahl, Florian Kamp, Erik van der Bijl, Arthur Jr. Galapon, Christopher Kurz, Bowen Xin, Zhengxiang Sun, Hollie Min, Gregg Belous, Jason Dowling, Yan Xia, Siyuan Mei, Fuxin Fan, Arthur Longuefosse, Javier Sequeiro Gonzalez, Miguel Diaz Benito, Alvaro Garcia Martin, Fabien Baldacci, Valentin Boussot, C\'edric H\'emon, Jean-Claude Nunes, Jean-Louis Dillenseger, Zhiyuan Zhang, Jinghua Cai, Han Bing, Tan Zuopeng, Ricardo Brioso, Daniele Loiacono, Guillaume Landry, Adrian Thummerer, Matteo Maspero · 14 May 2026

  Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density information. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron density, and cone-beam CT (CBCT) requires c…

• ScribbleDose: Scribble-Guided Dose Prediction in Radiotherapy

  Zhenxi Zhang, Yitao Zhuang, Yao Pu, Peixin Yu, Zirong Li, Yan Xia, Hui Li, Bin Li, Fuchen Zheng, Ge Ren · 13 May 2026

  Anatomical structure masks are widely adopted in radiotherapy dose prediction, as they provide explicit geometric constraints that facilitate structure-dose coupling. However, conventional manual delineation of these masks requires precise annotation of structure boundaries relevant to radiotherapy,…