Automatic quality control of digitally reconstructed radiograph computation and comparison with standard methods

12 pagesInternational audienceConformal radiotherapy helps to deliver an accurate and effective cancer treatment by exactly targeting the tumor. In this purpose, softwares of the treatment planning system (TPS) compute every geometric parameters of the treatment. It is essential to control the quality of them because the TPS performances are directly connected with the precision on the treated region. The standard method to control them is to use physical test objects (PTOs).1,2 The use of PTOs introduces uncertainties in the quality assessment because of the CT scan. Another method to assess the quality of these softwares is to use digital test objects (DTOs).3-5 DTOs are exactly known in a continuous and a discrete way. Thus the assessment of the TPS quality can be more accurate and faster. The fact that the DTO characteristics are well known allows to calculate a theoretical result. The comparison of the TPS and this theoretical results leads to a quantitative assessment of the TPS softwares quality. This work presents the control of major quality criteria of digitally reconstructed radiograph (DRR) computation: ray divergence, ray incidence and spatial resolution. Fully automated methods to control these points have been developed. The same criteria have been tested with PTO and the quality assessments by the two methods have been compared. The DTO methods appeared to be much more accurate because computable

Digital phantoms for the evaluation of a software used for an automatic analysis of the Winston-Lutz test in image guided radiation therapy

11 pagesInternational audienceAccurate isocentre positioning of the treatment machine is essential for the radiation therapy process, especially in stereotactic radio surgery and in image guided radiation therapy. We present in this paper a new method to evaluate a software which is used to perform an automatic analysis of the Winston-Lutz1, 2 test used in order to determine position and size of the isocentre. The method consists of developing digital phantoms that simulate mechanical distortions of the treatment machine as well as misalignments of the positioning laser targeting the isocentre. These Digital Test Objects (DTOs) offer a detailed and profound evaluation of the software and allow determining necessary adjustments which lead to high precision and therefore contributes to a better treatment targeting

Review of Cervix Cancer Classification Using Radiomics on Diffusion-Weighted Imaging

Magnetic Resonance Imaging (MRI) is one of the most used imaging modalities for the identification and quantification of various types of cancers. MRI image analysis is mostly conducted by experts relying on the visual interpretation of the images and some basic semiquantitative parameters. However, it is well known that additional clinical information is available in these images and can be harvested using the field of radiomics. This consists of the extraction of complex unexplored features from these images that can provide underlying functions in disease process. In this paper, we provide a review of the application of radiomics to extract relevant information from MRI Diffusion Weighted Imaging (DWI) for the classification of cervix cancer. The main research findings are the presentation of the state of the art of this application with the description of its main steps and related challenges

Automatisation du contrôle de qualité d'une installation d'imagerie de repositionnement en radiothérapie externe

On-board imagers mounted on a radiotherapy treatment machine are very effective devices that improve the geometric accuracy of radiation delivery. However, a precise and regular quality control program is required in order to achieve this objective. Our purpose consisted of developing software tools dedicated to an automatic image quality control of IGRT devices used in external radiotherapy: 2D-MV mode for measuring patient position during the treatment using high energy images, 2D-kV mode (low energy images) and 3D Cone Beam Computed Tomography (CBCT) MV or kV mode, used for patient positioning before treatment. Automated analysis of the Winston & Lutz test was also proposed. This test is used for the evaluation of the mechanical aspects of treatment machines on which additional constraints are carried out due to the on-board imagers additional weights. Finally, a technique of generating digital phantoms in order to assess the performance of the proposed software tools is described. Software tools dedicated to an automatic quality control of IGRT devices allow reducing by a factor of 100 the time spent by the medical physics team to analyze the results of controls while improving their accuracy by using objective and reproducible analysis and offering traceability through generating automatic monitoring reports and statistical studies.Les imageurs embarqués sur les appareils de traitement par radiothérapie sont des dispositifs très efficaces pour améliorer la précision géométrique des irradiations. Cependant, ils ne sont pertinents que s'ils font l'objet de contrôles de performances précis et réguliers. Notre propos a donc été de développer des solutions logicielles permettant d'automatiser l'analyse des images de contrôle de qualité des principaux modes utilisés en imagerie de repositionnement : le mode 2D-MV qui mesure l'image radiante bidimensionnelle haute énergie transmise par le patient pendant le traitement, les modes 2D-kV (images bidimensionnelles basse énergie) et mode 3D (images tridimensionnelles MV ou kV) qui permettent de réajuster la position du patient avant de traiter. Nous avons également automatisé l'analyse des images du test de Winston & Lutz utilisé pour évaluer la précision mécanique des appareils de traitement sur lesquels s'exercent des contraintes supplémentaires dues au poids supplémentaire de l'imageur embarqué. Enfin nous avons développé et mis en oeuvre une méthodologie originale et performante utilisant des objetstest numériques pour évaluer les performances des solutions logicielles mises au point. Au bilan, l'automatisation des contrôles de qualité des imageurs embarqués avec les solutions logicielles développées dans le cadre de ces travaux de thèse permet de réduire d'un facteur de 100 le temps consacré par l'équipe de physique médicale à l'analyse des résultats des contrôles tout en améliorant leur précision grâce à l'utilisation d'analyses objectives et reproductibles et leur traçabilité grâce à l'édition automatique de rapports de contrôle et d'études statistiques

Evaluation of two Software Tools Dedicated for an Automatic Analysis of the CT Scanner Image Spatial Resolution

Abstract—An evaluation of two software tools dedicated for an automatic analysis of the CT scanner image spatial resolution is presented in this paper. The methods evaluated consist of calculating the modulation transfer function of the CT scanners; the first uses an image of an impulse source, while the second method proposed by DROEGE uses an image of cyclic bar patterns. Two Digital Test Objects are created to this purpose. These DTOs are then blurred by doing a convolution with a two dimensional Gaussian function (PSF), which has a well known FWHM. The evaluation process consists then of comparing the Fourier transform of the PSF in one hand, and the two mentioned methods in the other hand. T I

Software Tools Dedicated for an Automatic Analysis of the CT Scanner Quality Control’s Images

This paper deals with the CT scanner images quality control, which is an important part of the quality control process of the CT scanner, which consists of making measurement in images of dedicated phantoms. Standard methods consist of scan explorations of phantoms that contain different specific patterns 1, 2. These methods rely on manual measurements with graphics tools in corresponding images (density, position, length…) or automatic measurements developed in softwares 3, 4 that use some masks to determine the region of interest (ROI). The problem of these masks is that they may produce wrong results in case of misalignment of the phantom. We propose a new method that consists, firstly of developing software tools that are capable of performing an automated analysis of CT images of three standard phantoms, LAP 5, GEMS 6 and CATPHAN600 7, in terms of slice thickness, spatial resolution, low and high level contrast, noise and uniformity. The method we have developed is completely automatic because it uses some protocols and special treatments in the images to automatically detect the position and the size of the ROI. Secondly, to test the performances of our software tools, we develop two digital phantoms which reproduce the exact geometry and composition of the physical phantoms, i.e. some perfect CT images of the real phantoms, and a complete set of distorted digital phantoms which represent the “perfect ” phantom distorted by noise and blur calibrated functions to test the performances of our automated analysis software

Deep Learning Approaches for Automatic Quality Assurance of Magnetic Resonance Images Using ACR Phantom

Abstract Background In recent years, there has been a growing trend towards utilizing Artificial Intelligence (AI) and machine learning techniques in medical imaging, including for the purpose of automating quality assurance. In this research, we aimed to develop and evaluate various deep learning-based approaches for automatic quality assurance of Magnetic Resonance (MR) images using the American College of Radiology (ACR) standards. Methods The study involved the development, optimization, and testing of custom convolutional neural network (CNN) models. Additionally, popular pre-trained models such as VGG16, VGG19, ResNet50, InceptionV3, EfficientNetB0, and EfficientNetB5 were trained and tested. The use of pre-trained models, particularly those trained on the ImageNet dataset, for transfer learning was also explored. Two-class classification models were employed for assessing spatial resolution and geometric distortion, while an approach classifying the image into 10 classes representing the number of visible spokes was used for the low contrast. Results Our results showed that deep learning-based methods can be effectively used for MR image quality assurance and can improve the performance of these models. The low contrast test was one of the most challenging tests within the ACR phantom. Conclusions Overall, for geometric distortion and spatial resolution, all of the deep learning models tested produced prediction accuracy of 80% or higher. The study also revealed that training the models from scratch performed slightly better compared to transfer learning. For the low contrast, our investigation emphasized the adaptability and potential of deep learning models. The custom CNN models excelled in predicting the number of visible spokes, achieving commendable accuracy, recall, precision, and F1 scores

Nouvelle méthode automatique de contrôle de qualité des systèmes de planification géométrique des traitements en radiothérapie externe conformationnelle

National audienceEn radiothérapie externe conformationnelle, les systèmes de planification des traitements (TPS : Treatment Planning System) sont au cœur du processus décisionnel. Ils assistent le radiothérapeute, le physicien et le dosimétriste dans la définition de chacun des paramètres du traitement. La qualité des outils logiciels développés sur le TPS va donc directement influencer la précision des traitements préparés sur cette console. Il est alors impératif de contrôler les performances des systèmes de planification des traitements par radiothérapie. Pour se faire nous proposons une solution innovante de contrôle de qualité des outils de planification géométrique des TPS (simulation virtuelle) basée sur l'utilisation d'Objets-Tests-Numériques (OTN). Ces OTN viennent se substituer avantageusement aux fantômes physiques traditionnellement utilisés pour ce type de contrôles de qualité. En effet les contrôles qui découlent de l'utilisation de fantômes physiques sont longs, incomplets et imprécis. Par ailleurs, ils nécessitent une acquisition tomographique préalable à leur chargement sur les consoles informatiques à tester. Enfin, l'évaluation des résultats des tests effectués avec ces fantômes repose principalement sur l'utilisation des outils graphiques du TPS testé. Nous présentons ici le concept global de l'utilisation d'OTN pour le contrôle de qualité des plateformes de traitement d'image, et en particulier des solutions de contrôle de qualité pour quatre opérations effectuées par les TPS : la délinéation automatique des structures, l'application automatique de marges d'expansion ou d'érosion, le réglage automatique de la position de l'isocentre et la conformation automatique du collimateur classique ou multi-lames. La méthode « OTN » que nous avons mise au point permet d'assurer un contrôle de qualité automatique, rapide, précis et complet des outils géométriques des TPS. Les avantages de notre méthode sont nombreux : 1. Les OTN directement conçus et générés en voxels au format DICOM présentent une géométrie « parfaite », non détériorée par une acquisition préalable par le scanner. Ainsi, les tests produits sont plus précis car ils reflètent la qualité intrinsèque du TPS et non pas celle du couple scanner-TPS. 2. Les OTN peuvent être produits beaucoup plus facilement que les fantômes physiques grâce au logiciel OTN-Creator que nous avons développé (logiciel de conception 3D vs. machine outils). Cela nous permet de tester beaucoup plus en profondeur les outils logiciels développés par les TPS en ayant recours à de nombreux OTN. 3.Les résultats des tests sont comparés à des étalons conçus et produits sur la forme d'OTN (OTN de sortie de référence) grâce à l'utilisation d'algorithmes analytiques de référence. Ainsi toutes les données manipulées pour le contrôle de qualité sont numériques (OTN d'entrée, OTN de sortie de référence, résultat du traitement de l'OTN d'entrée par le TPS) ce qui nous permet de déployer de nombreux outils logiciels d'analyse automatique des résultats des contrôles de qualité. Ce procédé permet de réduire de manière importante la durée des opérations de contrôle tout en améliorant leur précision grâce à l'utilisation de méthodes objectives. Deux TPS (Eclipse de Varian et Advantage Sim de GEMS) ont été contrôlés avec la méthode que nous proposons. L'évaluation de leurs performances a révélé quelques erreurs dans le fonctionnement des outils logiciels, et a mis en avant certaines de leurs particularités. Cette mise en œuvre a également été l'occasion de comparer notre méthode de contrôle de qualité basée sur des OTN avec la méthode classique basée sur les objets-tests physiques

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN’s performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance