In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. Motion models can be used to simulate motion patterns and assess anatomical robustness before delivery. Traditionally, such models are based on principal component analysis (PCA) and are either patient-specific (requiring several scans per patient) or population-based, applying the same deformations to all patients. We present a hybrid approach which, based on population data, allows to predict patient-specific inter-fraction variations for an individual patient. We propose a deep learning probabilistic framework that generates deformation vector fields (DVFs) warping a patient's planning computed tomography (CT) into possible patient-specific anatomies. This daily anatomy model (DAM) uses few random variables capturing groups of correlated movements. Given a new planning CT, DAM estimates the joint distribution over the variables, with each sample from the distribution corresponding to a different deformation. We train our model using dataset of 312 CT pairs from 38 prostate cancer patients. For 2 additional patients (22 CTs), we compute the contour overlap between real and generated images, and compare the sampled and ground truth distributions of volume and center of mass changes. With a DICE score of 0.86 and a distance between prostate contours of 1.09 mm, DAM matches and improves upon PCA-based models. The distribution overlap further indicates that DAM's sampled movements match the range and frequency of clinically observed daily changes on repeat CTs. Conditioned only on a planning CT and contours of a new patient without any pre-processing, DAM can accurately predict CTs seen during following treatment sessions, which can be used for anatomically robust treatment planning and robustness evaluation against inter-fraction anatomical changes.
Continuous sPatial-Temporal Deformable Image Registration (CPT-DIR) for motion modelling in radiotherapy: beyond classic voxel-based methods
Center for Proton Therapy, Paul Scherrer Institut（保罗·施特格研究所质子治疗中心）；Department of Computer Science, ETH Zürich（苏黎世联邦理工学院计算机科学系）；Department of Physics, ETH Zürich（苏黎世联邦理工学院物理系）；Department of Automation, Tsinghua Univeristy（清华大学自动化系）；S-Lab, Nanyang Technological University（南洋理工大学S实验室）
Purpose: CBCT-based adaptive radiotherapy requires daily images for accurate dose calculations. This study investigates the feasibility of applying a single convolutional network to facilitate CBCT-to-CT synthesis for head-and-neck, lung, and breast cancer patients. Methods: Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. CBCTs were registered to planning CTs according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an u…
Magnetic resonance imaging assessment of the suitability and consistency of radiotherapy treatment positioning achieved using intra-oral stents
Royal Brisbane and Women's Hospital（皇家布里斯班和妇女医院）；University of Queensland（昆士兰大学）；Queensland University of Technology（昆士兰科技大学）；Herston Biofabrication Institute, Metro North Hospital and Health Service（赫斯顿生物制造研究所，北都会区医院与健康服务）
In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference, mostly caused by inhomogeneities and electronic disequilibrium, is highly correlated with the dose distribution and the underlying anatomical tissue density. We hypothesize that a conversion scheme can be established to boost low-accuracy doses to high-accuracy, using intensity information obtained from computed tomography (CT) images. A deep learning-driven f…
Modeling of respiratory motion is important for a more accurate understanding and accounting of its effect on dose to cancers in the thorax and abdomen by radiotherapy. We have developed a model of respiration-induced organ motion in the thorax, without the commonly adopted assumption of repeatable breath cycles. The model describes the motion of a volume of interest within the patient, based on a reference 3-dimensional image (at end-expiration), and the diaphragm positions at different time points. The input data are respiration-correlated CT images of patients treated for nonsmall cell lung…
We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the Intensity-Modulated Radiation Therapy (IMRT) technique - that allows treating irregular and inhomogeneous tumors while reducing the radiation toxicity on healthy organs - is based on the optimization of the radiation beamlets intensities. The optimization aims to maximize the delivery of the therapy dose to cancer while avoiding the organs at risk to prevent their damage by the radiation. Here, we map the dose optimization p…
Glioblastoma are known to infiltrate the brain parenchyma instead of forming a solid tumor mass with a defined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infiltrated by tumor cells at low density appears normal on current imaging modalities. In clinical practice, a uniform margin is applied to account for microscopic spread of disease. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth: Anatomical barriers such as the falx cerebri repres…
Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI
Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia（悉尼大学医学与健康学院悉尼健康科学学院Image X研究所，悉尼，澳大利亚）；GenesisCare, Sydney, Australia（GenesisCare，悉尼，澳大利亚）；School of Clinical Medicine, Medicine & Health, University of New South Wales, Sydney, Australia（新南威尔士大学医学与健康学院临床医学系，悉尼，澳大利亚）；Centre for Medical Radiation Physics, School of Mathematics and Physics, University of Wollongong, Wollongong, Australia（伍伦贡大学数学与物理学院医学辐射物理中心，伍伦贡，澳大利亚）
Institute of Cancer Research（癌症研究所）；University College London（伦敦大学学院）
Effective transperineal ultrasound image guidance in prostate external beam radiotherapy requires consistent alignment between probe and prostate at each session during patient set-up. Probe placement and ultrasound image inter-pretation are manual tasks contingent upon operator skill, leading to interoperator uncertainties that degrade radiotherapy precision. We demonstrate a method for ensuring accurate probe placement through joint classification of images and probe position data. Using a multi-input multi-task algorithm, spatial coordinate data from an optically tracked ultrasound probe is…
TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy
UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of Maryland Medical Center（马里兰大学医学中心）；Central Arkansas Radiation Therapy Institute（中央阿肯色放射治疗研究所）
Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as CBCT, to meet the online demands of plan evaluation and adaptation. Deep learning (DL)-based automatic segmentation has gained great success in segmenting planning CT, but its applications to CBCT yielded inferior results due to the low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT s…
Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Princess Màxima Center for Pediatric Oncology（玛克西玛公主儿科肿瘤中心）；Princess Margaret Cancer Centre（玛格丽特公主癌症中心）
To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed from limited information. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs ($n$=142) of rec…
TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
Sichuan Cancer Hospital & Institute（四川省肿瘤医院研究所）；University of Electronic Science and Technology of China（电子科技大学）；LMU University Hospital（LMU大学医院）；LMU Munich（慕尼黑大学）；Catharina Hospital（凯瑟琳医院）；Mater Olbia Hospital（奥尔比亚 Mater 医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Czech Technical University in Prague（布拉格捷克理工大学）；University of Sydney（悉尼大学）；Fondazione Policlinico Universitario Agostino Gemelli, IRCCS（阿戈斯蒂诺·杰梅利大学综合医院基金会 IRCCS）；Abano Terme Hospital（阿巴诺泰尔梅医院）；GenesisCare；St Vincent’s Hospital（圣文森特医院）；Amsterdam UMC location Vrije Universiteit Amsterdam（阿姆斯特丹自由大学医学中心）；Technical University Eindhoven（埃因霍温理工大学）
It has long been known that radiation biology plays an important role and it is necessary for radiotherapy treatments. The radiation effects on normal and malignant tissues after exposure range from a femtosecond to months and years thereafter [1,2]. Therefore, to optimize treatment, it is crucial to explain and understand these mechanisms [3-5]. Providing a conceptual basis for radiotherapy and identifying the mechanisms and processes that underlie the tumor and normal tissue responses to irradiation can help to explain the observed phenomena [6]. Examples include understanding hypoxia, reoxy…
Dose distribution (depthwise and laterally) to organs outside the radiotherapy treatment field can be significant and therefore is of clinical interest from the radiation protection point of view. In the present work, measurements were performed in a locally fabricated polystyrene phantom using TLD chips (LiF-100) for different teletherapy units (Cobalt-60 gamma ray, 120 kVp X-ray and 250 kVp X-ray) to estimate the dose distribution at distances up to 40 cm from the field edge along the central axes of the field size. Finally, the dose distribution for Cobalt-60 beam energy is parameterized as…
With the continued evolution of modern radiation therapy towards high precision delivery of high therapeutic doses to the tumour while optimally sparing surrounding healthy tissue, imaging becomes a crucial component to identify the intended target, properly position it at the treatment site and, in more advanced research applications, visualize the treatment delivery. This contribution reviews the main role of imaging in modern external beam radiation therapy, with special emphasis on emerging ion beam therapy techniques, which aim at exploiting the favourable properties of ion interaction in…
Magnetic resonance imaging (MRI) is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearity (GNL) limit anatomical accuracy, potentially compromising the quality of tumour treatments. In addition, slow MR acquisition and reconstruction limit the potential for real-time image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for real-time MR-guided radiotherapy applications. We…
Current image-guided prostate radiotherapy often relies on the use of implanted fiducials or transducers for target localization. Fiducial or transducer insertion requires an invasive procedure that adds cost and risks for bleeding, infection, and discomfort to some patients. We are developing a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kV X-ray images without the need for daily cone-beam computed tomography (CBCT). A deep learning model was first trained by using several thousand annotated projection X-ray images. T…
Voxel-Based Conversion of Hypofractionated Radiotherapy Dose Distributions to 2 Gy-Equivalent OAR Constraints: Proof-of-Concept Demonstrating the Radiobiological Benefits of Hypofractionation in a Prostate Radiotherapy Case
There is evidence that the population of cells that experience fluctuating oxygen levels are more radioresistant than chronically hypoxic ones and hence, this population may determine radiotherapy (RT) response, in particular for hypofractionated RT, where reoxygenation may not be as prominent. A considerable effort has been devoted to examining the impact of hypoxia on hypofractionated RT; however, much less attention has been paid to cyclic hypoxia specifically and the role its kinetics may play in determining the efficacy of these treatments. Here, a simple mathematical model of cyclic hypo…
The International Electrotechnical Commission (IEC) has previously defined standard rotation operators for positive gantry, collimator and couch rotations for the radiotherapy DICOM coordinate system that is commonly used by treatment planning systems. Coordinate transformations to the coordinate systems of commonly used Monte Carlo (MC) codes (BEAMnrc/DOSXYZnrc and VMC++) have been derived and published in the literature. However, these coordinate transformations disregard patient orientation during the computed tomography (CT) scan, and assume the most commonly used 'head first, supine' orie…
Medical Center University of Freiburg（弗赖堡大学医学中心）；University of Freiburg（弗赖堡大学）；German Cancer Consortium (DKTK)（德国癌症研究联盟（DKTK））
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally we summarised the most recent developments. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second …
Aim: To examine and quantify set-up errors in patient positioning in head-and-neck radiotherapy and to investigate the impact of the choice of reference isocentre -- on the patient neck or patient skull -- on the magnitude of set-up errors. Materials and methods: Set-up position corrections obtained using online kV 2D/2D matching were recorded automatically for every treatment fraction. 3413 treatment records for 117 patients treated with volumetric modulated arc therapy (VMAT) during 2013 and 2014 on a single treatment machine in our clinic were analysed. In 79 treatment plans the reference i…
Adaptive Radiotherapy (ART) is an emerging technique for treating cancer patients which facilitates higher delivery accuracy and has the potential to reduce toxicity. However, ART is also resource-intensive, requiring extra human and machine time compared to standard treatment methods. In this analysis, we sought to predict the subset of node-negative cervical cancer patients who benefit the most from ART. CT images, initial plan data, and on-treatment Cone-Beam CT (CBCT) images for 20 retrospective cervical cancer patients were used to simulate doses from daily non-adaptive and adaptive techn…
The limits of TDF (time, dose, and fractionation) and linear quadratic models have been known for a long time. Medical physicists and physicians are required to provide fast and reliable interpretations regarding the delivered doses or any future prescriptions relating to treatment changes. We therefore propose a calculation interface under the GNU license to be used for equivalent doses, biological doses, and normal tumor complication probability (Lyman model). The methodology used draws from several sources: the linear-quadratic-linear model of Astrahan, the repopulation effects of Dale, and…
MRI-guided cardiac radiotherapy using a 1.5 T MR-linac: surveying emerging patterns of care
University Medical Center Utrecht（乌得勒支大学医学中心）；University of Texas MD Anderson Cancer Center（德克萨斯大学安德森癌症中心）；Allegheny Health Network（阿勒格尼健康网络）；GenesisCare（创生医疗）
Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present an atlas-based approach which learns a dose prediction model for each patient (atlas) in a training database, and then learns to match novel patients to the most relevant atlases. The method creates a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces any requirement for specifying dose-volume objectives for conveying the goals of tr…
Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT), require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo physics simulations are recognised to be one of the most accurate tools to predict the dose delivered in a target tissue but can be very time consuming and therefore prohibitive for treatment planning. Faster dose prediction algorithms are usually developed for clinically deployed treatments only. In this work, we explore a new approach for fast and accurate dose esti…
Radiation therapy has remained as one of the main cancer treatment modalities and a highly cost-effective single modality treatment of cancer care. Typical regimens for fractionated external beam radiotherapy comprise a constant dose administered on weekdays, and no radiation on weekends. However, every patient has a tumor with distinct properties depending on intra-tumor heterogeneity, aggressiveness, and interactive properties with other cells that may make it more resistant or sensitive to radiation treatment. Accordingly, the concept of personalized cancer treatment is emerging to speciali…
The biological effect of one single radiation dose on a living tissue has been described by several radiobiological models. However, the fractionated radiotherapy requires to account for a new magnitude: time. In this paper we explore the biological consequences posed by the mathematical prolongation of a model to fractionated treatment. Nonextensive composition rules are introduced to obtain the survival fraction and equivalent physical dose in terms of a time dependent factor describing the tissue trend towards recovering its radioresistance (a kind of repair coefficient). Interesting (known…
Cherenkov Imaged Bio-morphological Features Verify Patient Positioning with Deformable Tissue Translocation in Breast Radiotherapy
Dartmouth College（达特茅斯学院）；Dartmouth Cancer Center（达特茅斯癌症中心）；Dartmouth Health（达特茅斯健康）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Missouri-Columbia（密苏里大学哥伦比亚分校）
SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy
University Medical Center Groningen, University of Groningen（格罗宁根大学医学中心，格罗宁根大学）；Radboud University Medical Center（拉德堡德大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）
The purpose of this study is to examine in a clinical setting a novel formulation of objective functions for intensity-modulated radiotherapy treatment plan multicriteria optimization (MCO) that we suggested in a recent study. The proposed objective functions are extended with dynamic multileaf collimator (DMLC) delivery constraints from the literature, and a tailored interior point method is described to efficiently solve the resulting optimization formulation. In a numerical planning study involving three patient cases, DMLC plans Pareto optimal to the MCO formulation with the proposed objec…
Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent generative adversarial network (cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy. A retrospective study was performed using CT images and 0.35T MR images of 12 patients with liver (n=8) and non-liver abdominal (n=4) cancer. CT images were deformably registered to the c…
We present a microscopic mechanism that accounts for the outward burst of "cold" ion species (IS) in a high-energy particle track due to coupling with ''hot" non-ion species (NIS). IS refers to radiolysis products of ionized molecules, whereas NIS refers to non-ionized excitations of molecules in a medium. The interaction is mediated by a quantized field of acoustic phonons, a channel that allows conversion of thermal energy of NIS to kinetic energy of IS, a flow of heat from the outer to the inner core of the track structure. We demonstrate the coexistence of "hot" NIS with "cold" IS in the r…
Monte Carlo (MC) methods provide the most accurate to-date dose calculations in heterogeneous media and complex geometries, and this spawns increasing interest in incorporating MC calculations into treatment planning quality assurance process. This involves MC dose calculations for clinically produced treatment plans. To perform these calculations, a number of treatment plan parameters specifying radiation beam and patient geometries need to be transferred to MC codes, such as BEAMnrc and DOSXYZnrc. Extracting these parameters from DICOM files is not a trivial task, one that has previously bee…
