A comparative evaluation of 3 different free-form deformable image registration and contour propagation methods for head and neck MRI : the case of parotid changes radiotherapy

Purpose: To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approachesthe commercial MIM, the open-source Elastix software, and an optimized version of it. Materials and Methods: Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness. Results: A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods. Conclusion: The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations

Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

Radiation-induced lung damage (RILD) is a common consequence of thoracic radiotherapy (RT). We present here a novel classification of the parenchymal features of RILD. We developed a deep learning algorithm (DLA) to automate the delineation of 5 classes of parenchymal texture of increasing density. 200 scans were used to train and validate the network and the remaining 30 scans were used as a hold-out test set. The DLA automatically labelled the data with Dice Scores of 0.98, 0.43, 0.26, 0.47 and 0.92 for the 5 respective classes. Qualitative evaluation showed that the automated labels were acceptable in over 80% of cases for all tissue classes, and achieved similar ratings to the manual labels. Lung registration was performed and the effect of radiation dose on each tissue class and correlation with respiratory outcomes was assessed. The change in volume of each tissue class over time generated by manual and automated segmentation was calculated. The 5 parenchymal classes showed distinct temporal patterns We quantified the volumetric change in textures after radiotherapy and correlate these with radiotherapy dose and respiratory outcomes. The effect of local dose on tissue class revealed a strong dose-dependent relationship We have developed a novel classification of parenchymal changes associated with RILD that show a convincing dose relationship. The tissue classes are related to both global and local dose metrics, and have a distinct evolution over time. Although less strong, there is a relationship between the radiological texture changes we can measure and respiratory outcomes, particularly the MRC score which directly represents a patient’s functional status. We have demonstrated the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible

Real-time intrafraction motion monitoring in external beam radiotherapy

© 2019 Institute of Physics and Engineering in Medicine. Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT

Intensity modulated radiation therapy and arc therapy: validation and evolution as applied to tumours of the head and neck, abdominal and pelvic regions

Intensiteitsgemoduleerde radiotherapie (IMRT) laat een betere controle over de dosisdistributie (DD) toe dan meer conventionele bestralingstechnieken. Zo is het met IMRT mogelijk om concave DDs te bereiken en om de risico-organen conformeel uit te sparen. IMRT werd in het UZG klinisch toegepast voor een hele waaier van tumorlocalisaties. De toepassing van IMRT voor de bestraling van hoofd- en halstumoren (HHT) vormt het onderwerp van het eerste deel van deze thesis. De planningsstrategie voor herbestralingen en bestraling van HHT, uitgaande van de keel en de mondholte wordt beschreven, evenals de eerste klinische resultaten hiervan. IMRT voor tumoren van de neus(bij)holten leidt tot minstens even goede lokale controle (LC) en overleving als conventionele bestralingstechnieken, en dit zonder stralingsgeïnduceerde blindheid. IMRT leidt dus tot een gunstiger toxiciteitprofiel maar heeft nog geen bewijs kunnen leveren van een gunstig effect op LC of overleving. De meeste hervallen van HHT worden gezien in het gebied dat tot een hoge dosis bestraald werd, wat erop wijst dat deze “hoge dosis” niet volstaat om alle clonogene tumorcellen uit te schakelen. We startten een studie op, om de mogelijkheid van dosisescalatie op geleide van biologische beeldvorming uit te testen. Naast de toepassing en klinische validatie van IMRT bestond het werk in het kader van deze thesis ook uit de ontwikkeling en het klinisch opstarten van intensiteitgemoduleerde arc therapie (IMAT). IMAT is een rotationele vorm van IMRT (d.w.z. de gantry draait rond tijdens de bestraling), waarbij de modulatie van de intensiteit bereikt wordt door overlappende arcs. IMAT heeft enkele duidelijke voordelen ten opzichte van IMRT in bepaalde situaties. Als het doelvolume concaaf rond een risico-orgaan ligt met een grote diameter, biedt IMAT eigenlijk een oneindig aantal bundelrichtingen aan. Een planningsstrategie voor IMAT werd ontwikkeld, en type-oplossingen voor totaal abdominale bestraling en rectumbestraling werden onderzocht en klinisch toegepast

A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified

Medical physics challenges in clinical MR-guided radiotherapy

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization