scholarly journals Evaluation of Deformable Image Registration for Three-Dimensional Temporal Subtraction of Chest Computed Tomography Images

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ping Yan ◽  
Yoshie Kodera ◽  
Kazuhiro Shimamoto
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
Three Dimensional ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Temporal Changes ◽  
Transformation Model ◽  
Chest Computed Tomography ◽  
Temporal Subtraction ◽  
Computed Tomography Images

Purpose. To perform lung image registration for reducing misregistration artifacts on three-dimensional (3D) temporal subtraction of chest computed tomography (CT) images, in order to enhance temporal changes in lung lesions and evaluate these changes after deformable image registration (DIR). Methods. In 10 cases, mutual information (MI) lung mask affine mapping combined with cross-correlation (CC) lung diffeomorphic mapping was used to implement lung volume registration. With advanced normalization tools (ANTs), we used greedy symmetric normalization (greedy SyN) as a transformation model, which involved MI-CC-SyN implementation. The resulting displacement fields were applied to warp the previous (moving) image, which was subsequently subtracted from the current (fixed) image to obtain the lung subtraction image. Results. The average minimum and maximum log-Jacobians were 0.31 and 3.74, respectively. When considering 3D landmark distance, the root-mean-square error changed from an average of 20.82 mm for Pfixed to Pmoving to 0.5 mm for Pwarped to Pfixed. Clear shadows were observed as enhanced lung nodules and lesions in subtraction images. The lesion shadows showed lesion shrinkage changes over time. Lesion tissue morphology was maintained after DIR. Conclusions. DIR (greedy SyN) effectively and accurately enhanced temporal changes in chest CT images and decreased misregistration artifacts in temporal subtraction images.

scholarly journals Evaluation of Intensity- and Contour-Based Deformable Image Registration Accuracy in Pancreatic Cancer Patients

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1447 ◽  
Author(s):  
Yoshiki Kubota ◽  
Masahiko Okamoto ◽  
Yang Li ◽  
Shintaro Shiba ◽  
Shohei Okazaki ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Image Registration ◽  
Prone Position ◽  
Supine Position ◽  
Deformable Image Registration ◽  
Pancreatic Cancer Patient ◽  
Target Volume ◽  
Ct Images ◽  
Registration Accuracy ◽  
Computed Tomography Images

We aimed to clarify the accuracy of rigid image registration and deformable image registration (DIR) in carbon-ion radiotherapy (CIRT) for pancreatic cancer. Six patients with pancreatic cancer who were treated with passive irradiation CIRT were enrolled. Three registration patterns were evaluated: treatment planning computed tomography images (TPCT) to CT images acquired in the treatment room (IRCT) in the supine position, TPCT to IRCT in the prone position, and TPCT in the supine position to the prone position. After warping the contours of the original CT images to the destination CT images using deformation matrices from the registration, the warped delineated contours on the destination CT images were compared with the original ones using mean displacement to agreement (MDA). Four contours (clinical target volume (CTV), gross tumor volume (GTV), stomach, duodenum) and four registration algorithms (rigid image registration [RIR], intensity-based DIR [iDIR], contour-based DIR [cDIR], and a hybrid iDIR-cDIR ([hDIR]) were evaluated. The means ± standard deviation of the MDAs of all contours for RIR, iDIR, cDIR, and hDIR were 3.40 ± 3.30, 2.2 1± 2.48, 1.46 ± 1.49, and 1.46 ± 1.37 mm, respectively. There were significant differences between RIR and iDIR, and between RIR/iDIR and cDIR/hDIR. For the pancreatic cancer patient images, cDIR and hDIR had better accuracy than RIR and iDIR.

scholarly journals Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT

2019 ◽  
Vol 18 ◽  
pp. 153303381882118 ◽  
Author(s):  
Wannapha Nobnop ◽  
Imjai Chitapanarux ◽  
Somsak Wanwilairat ◽  
Ekkasit Tharavichitkul ◽  
Vicharn Lorvidhaya ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Nasopharyngeal Carcinoma ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Adaptive Radiotherapy ◽  
Deformation Field ◽  
Registration Accuracy ◽  
Deformation Method ◽  
Computed Tomography Images ◽  
Significant Difference

Introduction: The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images. Methods: The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma were used to evaluate the deformable image registration application. The deformable image registration image procedures were classified into 3 groups, including kilovoltage to kilovoltage, megavoltage to megavoltage, and kilovoltage to megavoltage. Three deformable image registration methods were employed using the deformable image registration and adaptive radiotherapy software. The validation was compared by volume-based, intensity-based, and deformation field analyses. Results: The use of different deformation methods greatly affected the deformable image registration accuracy from kilovoltage to megavoltage. The asymmetric transformation with the demon method was significantly better than other methods and illustrated satisfactory value for adaptive applications. The deformable image registration accuracy from kilovoltage to megavoltage showed no significant difference from the kilovoltage to kilovoltage images when using the appropriate method of registration. Conclusions: The choice of deformation method should be considered when applying the deformable image registration from kilovoltage to megavoltage images. The deformable image registration accuracy from kilovoltage to megavoltage revealed a good agreement in terms of intensity-based, volume-based, and deformation field analyses and showed clinically useful methods for nasopharyngeal carcinoma adaptive radiotherapy in tomotherapy applications.

scholarly journals Rapid screening for COVID-19 by applying artificial intelligence to chest computed tomography images: A feasibility study

2021 ◽  
Vol 5 ◽  
pp. 239920262110136
Author(s):  
Pedro Galván ◽  
José Fusillo ◽  
Felipe González ◽  
Oraldo Vukujevic ◽  
Luciano Recalde ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Pleural Effusion ◽  
Feasibility Study ◽  
Ct Images ◽  
Public Hospitals ◽  
Rt Pcr ◽  
Chest Computed Tomography ◽  
Computed Tomography Images ◽  
Respiratory Conditions

Aim: The aim of the study was to present the results and impact of the application of artificial intelligence (AI) in the rapid diagnosis of COVID-19 by telemedicine in public health in Paraguay. Methods: This is a descriptive, multi-centered, observational design feasibility study based on an AI tool for the rapid detection of COVID-19 in chest computed tomography (CT) images of patients with respiratory difficulties attending the country’s public hospitals. The patients’ digital CT images were transmitted to the AI diagnostic platform, and after a few minutes, radiologists and pneumologists specialized in COVID-19 downloaded the images for evaluation, confirmation of diagnosis, and comparison with the genetic diagnosis (reverse transcription polymerase chain reaction (RT-PCR)). It was also determined the percentage of agreement between two similar AI systems applied in parallel to study the viability of using it as an alternative method of screening patients with COVID-19 through telemedicine. Results: Between March and August 2020, 911 rapid diagnostic tests were carried out on patients with respiratory disorders to rule out COVID-19 in 14 hospitals nationwide. The average age of patients was 50.7 years, 62.6% were male and 37.4% female. Most of the diagnosed respiratory conditions corresponded to the age group of 27–59 years (252 studies), the second most frequent corresponded to the group over 60 years, and the third to the group of 19–26 years. The most frequent findings of the radiologists/pneumologists were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, diffuse ground glass opacity, hemidiaphragmatic paresis, calcified granuloma in the lower right lobe, bilateral pleural effusion, sequelae of tuberculosis, bilateral emphysema, and fibrotic changes, among others. Overall, an average of 86% agreement and 14% diagnostic discordance was determined between the two AI systems. The sensitivity of the AI system was 93% and the specificity 80% compared with RT-PCR. Conclusion: Paraguay has an AI-based telemedicine screening system for the rapid stratified detection of COVID-19 from chest CT images of patients with respiratory conditions. This application strengthens the integrated network of health services, rationalizing the use of specialized human resources, equipment, and inputs for laboratory diagnosis.

Correlation of displacement vector fields calculated by different deformable image registration algorithms with motion parameters in helical, axial and cone beam CT imaging

2019 ◽  
Vol 19 (3) ◽  
pp. 219-225
Author(s):  
Nesreen Alsbou ◽  
Salahuddin Ahmad ◽  
Imad Ali
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
Vector Fields ◽  
Displacement Vector ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Ct Imaging ◽  
Cone Beam ◽  
Helical Computed Tomography ◽  
Motion Parameters

AbstractAim:The purpose of this study is to investigate quantitatively the correlation of displacement vector fields (DVFs) from different deformable image registration (DIR) algorithms to register images from helical computed tomography (HCT), axial computed tomography (ACT) and cone beam computed tomography (CBCT) with motion parameters.Materials and methods:CT images obtained from scanning of the mobile phantom were registered with the stationary CT images using four DIR algorithms from the DIRART software: Demons, Fast-Demons, Horn–Schunck and Lucas–Kanade. HCT, ACT and CBCT imaging techniques were used to image a mobile phantom, which included three targets with different sizes (small, medium and large) that were manufactured from a water-equivalent material and embedded in low-density foam to simulate lung lesions. The phantom was moved with controlled cyclic motion patterns where a range of motion amplitudes (0–20 mm) and frequencies (0·125–0·5 Hz) were used.Results:The DVF obtained from different algorithms correlated well with motion amplitudes applied on the mobile phantom for CBCT and HCT, where the maximal DVF increased linearly with the motion amplitudes of the mobile phantom. In ACT, the DVF correlated less with motion amplitudes where motion-induced strong image artefacts and the DIR algorithms were not able to deform the ACT image of the mobile targets to the stationary targets. Three DIR algorithms produce comparable values and patterns of the DVF for certain CT imaging modality. However, DVF from Fast-Demons deviated strongly from other algorithms at large motion amplitudes.Conclusions:The local DVFs provide direct quantitative values for the actual internal tumour shifts that can be used to determine margins for the internal target volume that consider tumour motion during treatment planning. Furthermore, the DVF distributions can be used to extract motion parameters such as motion amplitude that can be extracted from the maximal or minimal DVF calculated by the different DIR algorithms and used in the management of the patient motion.

An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images

2018 ◽  
Vol 63 (4) ◽  
pp. 045026 ◽  
Author(s):  
R G J Kierkels ◽  
L A den Otter ◽  
E W Korevaar ◽  
J A Langendijk ◽  
A van der Schaaf ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Computed Tomography Images ◽  
Specific Evaluation

scholarly journals iCTCF: an integrative resource of chest computed tomography images and clinical features of patients with COVID-19 pneumonia

2020 ◽  
Author(s):  
Wanshan Ning ◽  
Shijun Lei ◽  
Jingjing Yang ◽  
Yukun Cao ◽  
Peiran Jiang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Severe Acute Respiratory Syndrome ◽  
Clinical Features ◽  
Clinical Status ◽  
Area Under The Curve ◽  
Ct Images ◽  
Chest Computed Tomography ◽  
Computed Tomography Images ◽  
Data Volume ◽  
Auc Value

Abstract The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was initially reported in Wuhan, China since December, 2019. Here, we reported a timely and comprehensive resource named iCTCF to archive 256,356 chest computed tomography (CT) images, 127 types of clinical features (CFs), and laboratory-confirmed SARS-CoV-2 clinical status from 1170 patients, reaching a data volume of 38.2 GB. To facilitate COVID-19 diagnosis, we integrated the heterogeneous CT and CF datasets, and developed a novel framework of Hybrid-learning for UnbiaSed predicTion of COVID-19 patients (HUST-19) to predict negative cases, mild/regular and severe/critically ill patients, respectively. Although both CT images and CFs are informative in predicting patients with or without COVID-19 pneumonia, the integration of CT and CF datasets achieved a striking accuracy with an area under the curve (AUC) value of 0.978, much higher than that when exclusively using either CT (0.919) or CF data (0.882). Together with HUST- 19, iCTCF can serve as a fundamental resource for improving the diagnosis and management of COVID-19 patients.Authors Wanshan Ning, Shijun Lei, Jingjing Yang, and Yukun Cao contributed equally to this work.

scholarly journals Immersive 3D virtual reality imaging in planning minimally invasive and complex adult cardiac surgery

2020 ◽  
Vol 1 (1) ◽  
pp. 62-70
Author(s):  
Amir H Sadeghi ◽  
Wouter Bakhuis ◽  
Frank Van Schaagen ◽  
Frans B S Oei ◽  
Jos A Bekkers ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Virtual Reality ◽  
Cardiac Surgery ◽  
Heart Surgery ◽  
Preoperative Planning ◽  
Operating Time ◽  
Three Dimensional ◽  
Cardiothoracic Surgery ◽  
Planning Tool ◽  
Computed Tomography Images

Abstract Aims Increased complexity in cardiac surgery over the last decades necessitates more precise preoperative planning to minimize operating time, to limit the risk of complications during surgery and to aim for the best possible patient outcome. Novel, more realistic, and more immersive techniques, such as three-dimensional (3D) virtual reality (VR) could potentially contribute to the preoperative planning phase. This study shows our initial experience on the implementation of immersive VR technology as a complementary research-based imaging tool for preoperative planning in cardiothoracic surgery. In addition, essentials to set up and implement a VR platform are described. Methods Six patients who underwent cardiac surgery at the Erasmus Medical Center, Rotterdam, The Netherlands, between March 2020 and August 2020, were included, based on request by the surgeon and availability of computed tomography images. After 3D VR rendering and 3D segmentation of specific structures, the reconstruction was analysed via a head mount display. All participating surgeons (n = 5) filled out a questionnaire to evaluate the use of VR as preoperative planning tool for surgery. Conclusion Our study demonstrates that immersive 3D VR visualization of anatomy might be beneficial as a supplementary preoperative planning tool for cardiothoracic surgery, and further research on this topic may be considered to implement this innovative tool in daily clinical practice. Lay summary Over the past decades, surgery on the heart and vessels is becoming more and more complex, necessitating more precise and accurate preoperative planning. Nowadays, operative planning is feasible on flat, two-dimensional computer screens, however, requiring a lot of spatial and three-dimensional (3D) thinking of the surgeon. Since immersive 3D virtual reality (VR) is an upcoming imaging technique with promising results in other fields of surgery, we aimed in this study to explore the additional value of this technique in heart surgery. Our surgeons planned six different heart operations by visualizing computed tomography scans with a dedicated VR headset, enabling them to visualize the patient’s anatomy in an immersive and 3D environment. The outcomes of this preliminary study are positive, with a much more reality-like simulation for the surgeon. In such, VR could potentially be beneficial as a preoperative planning tool for complex heart surgery.

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