Physics in Medicine & Biology 2022. **First two authors contributed equally. Last two authors are co-senior authors. arXiv admin note: substantial text overlap with arXiv:2106.03705
Dose volume histogram (DVH) metrics are widely accepted evaluation criteria in the clinic. However, incorporating these metrics into deep learning dose prediction models is challenging due to their non-convexity and non-differentiability. We propose a novel moment-based loss function for predicting 3D dose distribution for the challenging conventional lung intensity modulated radiation therapy (IMRT) plans. The moment-based loss function is convex and differentiable and can easily incorporate DVH metrics in any deep learning framework without computational overhead. The moments can also be customized to reflect the clinical priorities in 3D dose prediction. For instance, using high-order moments allows better prediction in high-dose areas for serial structures. We used a large dataset of 360 conventional lung patients with 2Gy $\times$ 30 fractions to train the deep learning (DL) model using clinically treated plans. We trained a UNet-like CNN architecture using computed tomography (CT), planning target volume (PTV) and organ-at-risk contours (OAR) as input to infer corresponding voxel-wise 3D dose distribution. We evaluated three different loss functions: (1) Mean Absolute Error (MAE) Loss, (2) MAE + DVH Loss, and (3) the proposed MAE + Moments Loss. The quality of the predictions was compared using different DVH metrics as well as dose-score and DVH-score, recently introduced by the AAPM knowledge-based planning grand challenge. Model with (MAE + Moment) loss function outperformed the model with MAE loss by significantly improving the DVH-score (11%, p$<$0.01) while having similar computational cost. It also outperformed the model trained with (MAE+DVH) by significantly improving the computational cost (48%) and the DVH-score (8%, p$<$0.01). The code, models, docker container, and Google Colab project are available on our DoseRTX GitHub (https://github.com/nadeemlab/DoseRTX).
Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Fudan University（复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）；São Carlos School of Engineering, University of São Paulo（圣保罗大学圣卡洛斯工程学院）；Ribeirão Preto School of Medicine, University of São Paulo（圣保罗大学里贝朗普雷图医学院）
Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows
School of Mathematical Sciences, Capital Normal University（首都师范大学数学学院）；Centre for Mathematical Imaging Techniques, University of Liverpool（利物浦大学数学成像技术中心）；Department of Mathematics and Statistics, University of Strathclyde（斯特拉思克莱德大学数学与统计学系）
LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction
The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen United Imaging Research Institute of Innovative Medical Equipment（深圳联合创新医疗装备研究院）；Huazhong University of Science and Technology（华中科技大学）；Tencent YouTu Lab（腾讯优图实验室）
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Xian Jiaotong University（西安交通大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；West China Hospital（华西医院）
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy
Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding
School of Computer Science, Sichuan University（四川大学计算机学院）；Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University（四川大学华西医院肿瘤中心放射肿瘤科）
Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data
Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction
Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；Affiliated Hospital of Xiangnan University（湘南大学附属医院）；Jiangxi Cancer Hospital（江西省肿瘤医院）；Chenzhou Third People's Hospital（郴州第三人民医院）
DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy
School of Biomedical Engineering, Southern Medical University（南方医科大学生物医学工程学院）；Guangdong Provincial Key Laboratory of Medical Image Processing（广东省医学图像重点实验室）；Department of Radiation Therapy, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine（广州中医药大学第二附属医院放射治疗科）
Convolutional Neural Network to Restore Low-Dose Digital Breast Tomosynthesis Projections in a Variance Stabilization Domain
University of São Paulo（圣保罗大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Fudan University（复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（脑启发智能科学与技术研究院，复旦大学）；Department of Biomedical Engineering, Yale University（生物医学工程系，耶鲁大学）；Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute（生物医学成像中心，生物技术与跨学科研究所在生物医学工程系，罗切斯特理工学院）；Institute of Science and Technology for Brain-inspired Intelligence, MOE Frontiers Center for Brain Science and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Ministry of Education), Fudan University（脑启发智能科学与技术研究院，教育部脑科学前沿中心和计算神经科学与脑启发智能重点实验室，复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与脑启发技术中心）
PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising
School of Computer Science and Technology, Guangdong University of Technology（广东技术大学计算机科学与技术学院）；Zhongshan School of Medicine, Sun Yat-sen University（中山大学仲沙医学院）；School of Mathematics and Statistics, Guangdong University of Technology（广东技术大学数学与统计学院）；School of Mathematics (Zhuhai), Sun Yat-sen University（中山大学珠海数学学院）；Future Technology Institute, South China University of Technology（华南理工大学未来技术研究院）；School of Computer Science and Engineering, Huizhou University（惠州大学计算机科学与工程学院）
A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks
