Fluence Map Prediction with Deep Learning: A Transformer-based Approach

• URL: http://arxiv.org/abs/2511.08645v1
• Date: Thu, 13 Nov 2025 01:01:24 GMT
• Title: Fluence Map Prediction with Deep Learning: A Transformer-based Approach
• Authors: Ujunwa Mgboh, Rafi Sultan, Dongxiao Zhu, Joshua Kim,
• Abstract summary: This study presents a deep learning framework that accelerates fluence map generation while maintaining clinical quality.<n>An end-to-end 3D Swin-UNETR network was trained to predict nine-beam fluence maps directly from volumetric CT images and anatomical contours.<n>The proposed model achieved an average R2 of 0.95 +/- 0.02, MAE of 0.035 +/- 0.008, and gamma passing rate of 85 +/- 10 percent (3 percent / 3 mm) on the test set.
• Score: 4.362977319915193
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate fluence map prediction is essential in intensity-modulated radiation therapy (IMRT) to maximize tumor coverage while minimizing dose to healthy tissues. Conventional optimization is time-consuming and dependent on planner expertise. This study presents a deep learning framework that accelerates fluence map generation while maintaining clinical quality. An end-to-end 3D Swin-UNETR network was trained to predict nine-beam fluence maps directly from volumetric CT images and anatomical contours using 99 prostate IMRT cases (79 for training and 20 for testing). The transformer-based model employs hierarchical self-attention to capture both local anatomical structures and long-range spatial dependencies. Predicted fluence maps were imported into the Eclipse Treatment Planning System for dose recalculation, and model performance was evaluated using beam-wise fluence correlation, spatial gamma analysis, and dose-volume histogram (DVH) metrics. The proposed model achieved an average R^2 of 0.95 +/- 0.02, MAE of 0.035 +/- 0.008, and gamma passing rate of 85 +/- 10 percent (3 percent / 3 mm) on the test set, with no significant differences observed in DVH parameters between predicted and clinical plans. The Swin-UNETR framework enables fully automated, inverse-free fluence map prediction directly from anatomical inputs, enhancing spatial coherence, accuracy, and efficiency while offering a scalable and consistent solution for automated IMRT plan generation.

Related papers

• Radiology Report Generation with Layer-Wise Anatomical Attention [35.18016233072556]
  We introduce a compact image-to-text architecture that generates Findings section of chest X-ray reports.<n>The model combines a frozen Self-Distillation with No Labels v3 (DINOv3) Vision Transformer (ViT) encoder with a Generative Pre-trained Transformer 2 (GPT-2) decoder.
  arXiv  Detail & Related papers  (2025-12-18T18:17:57Z)
• Detection and Localization of Subdural Hematoma Using Deep Learning on Computed Tomography [3.5333115085185614]
  Subdural hematoma (SDH) is a common neurosurgical emergency, with increasing incidence in aging populations.<n>There remains a need for transparent, high-performing systems that integrate multimodal clinical and imaging information.<n>We developed a multimodal deep-learning framework that integrates structured clinical variables, a 3D convolutional neural network trained on CT volumes, and a 2D segmentation model for SDH detection and localization.
  arXiv  Detail & Related papers  (2025-12-10T07:37:42Z)
• Patient-specific AI for generation of 3D dosimetry imaging from two 2D-planar measurements [39.75222230566146]
  We explore the use of patient specific reinforced learning to generate 3D activity maps from two 2D planar images.<n>Being able to generate 3D activity maps from planar scintigraphy opens the gate for new dosimetry applications.
  arXiv  Detail & Related papers  (2025-10-24T11:46:51Z)
• Depth-Sequence Transformer (DST) for Segment-Specific ICA Calcification Mapping on Non-Contrast CT [38.85617601239779]
  Conventional 3D models are forced to process downsampled volumes or isolated patches.<n>We reformulate the 3D challenge as a textbfParallel Probabilistic Landmark localization task along the 1D axial dimension.<n>We propose the textbfDepth-Sequence Transformer (DST), a framework that processes full-resolution CT volumes as sequences of 2D slices.
  arXiv  Detail & Related papers  (2025-07-10T23:12:12Z)
• Physics-Guided Radiotherapy Treatment Planning with Deep Learning [27.620227899075022]
  We propose a two-stage, physics-guided deep learning pipeline for radiotherapy planning.<n>We train and evaluate our approach on 133 prostate cancer patients treated with a uniform 2-arc VMAT protocol.
  arXiv  Detail & Related papers  (2025-06-23T19:44:56Z)
• Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy [3.2139860661520347]
  Denoising low-statistics Monte Carlo (MC) dose maps is proposed to enable fast, high-quality dose generation.<n> IMPT plans and 3D CT images from 80 H&N patients were used to generate noisy and high-statistics dose maps.<n>The model achieved MAEs of 0.195 (H&N), 0.120 (lung), 0.172 (breast), and 0.376 Gy[RBE] (prostate) across all sites.
  arXiv  Detail & Related papers  (2025-06-04T21:37:15Z)
• Explainable Anatomy-Guided AI for Prostate MRI: Foundation Models and In Silico Clinical Trials for Virtual Biopsy-based Risk Assessment [3.5408411348831232]
  We present a fully automated, anatomically guided deep learning pipeline for prostate cancer (PCa) risk stratification using routine MRI.<n>The pipeline integrates three key components: an nnU-Net module for segmenting the prostate gland and its zones on axial T2-weighted MRI; a classification module based on the DiceedPT Swin Transformer foundation model, fine-tuned on 3D patches with optional anatomical priors and clinical data; and a VAE-GAN framework for generating counterfactual heatmaps that localize decision-driving image regions.
  arXiv  Detail & Related papers  (2025-05-23T14:40:09Z)
• Continuous sPatial-Temporal Deformable Image Registration (CPT-DIR) for motion modelling in radiotherapy: beyond classic voxel-based methods [28.526710503853877]
  Current deformable image registration (DIR) implementations rely on discrete volumetric motion representation.<n>We propose a novel approach using implicit neural representation (INR) for continuous modeling of patient anatomical motion.<n>CPT-DIR significantly enhances registration and accuracy, automation, and speed.
  arXiv  Detail & Related papers  (2024-05-01T10:26:08Z)
• Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction [1.283897253352624]
  We propose an end-to-end approach for automated prognosis prediction using histology slides stained with H&E and HPS. We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline. We exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer (ViT) in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction.
  arXiv  Detail & Related papers  (2023-11-17T03:32:11Z)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation [34.22415353209505]
  We propose a Differentiable Topology-Preserved Distance Transform (DTPDT) framework to improve the performance of airway segmentation. A Topology-Preserved Surrogate (TPS) learning strategy is first proposed to balance the training progress within-class distribution. A Convolutional Distance Transform (CDT) is designed to identify the breakage phenomenon with superior sensitivity and minimize the variation of the distance map between the predictionand ground-truth.
  arXiv  Detail & Related papers  (2022-09-17T15:47:01Z)
• CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans [36.093580055848186]
  Lung Adenocarcinoma (LAUC) has recently been the most prevalent. Timely and accurate knowledge of the invasiveness of lung nodules leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. The primary imaging modality to assess and predict the invasiveness of LAUCs is the chest CT. In this paper, a predictive transformer-based framework, referred to as the "CAE-Transformer", is developed to classify LAUCs.
  arXiv  Detail & Related papers  (2021-10-17T04:37:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.