Download or read book An Algorithm to Improve Deformable Image Registration Accuracy in Challenging Cases of Locally advanced Non small Cell Lung Cancer written by Christopher Logan Guy and published by . This book was released on 2017 with total page 727 pages. Available in PDF, EPUB and Kindle. Book excerpt: A common co-pathology of large lung tumors located near the central airways is collapse of portions of lung due to blockage of airflow by the tumor. Not only does the lung volume decrease as collapse occurs, but fluid from capillaries also fills the space no longer occupied by air, greatly altering tissue appearance. During radiotherapy, typically administered to the patient over multiple weeks, the tumor can dramatically shrink in response to the treatment, restoring airflow to the lung sections which were collapsed when therapy began. While return of normal lung function is a positive development, the change in anatomy presents problems for future radiation sessions since the treatment was planned on lung geometry which is no longer accurate. The treatment must be adapted to the new lung state so that the radiation continues to accurately target the tumor while safely avoiding healthy tissue. However, to account for the dose delivered previously, correspondences of anatomy between the former image when the lung was collapsed and the re-expanded lung in a current image must be obtained. This process, known as deformable image registration, is performed by registration software. Most registration algorithms assume that identical anatomy is contained in the images and that intensities of corresponding image elements are similar; both assumptions are untrue when collapsed lung re-expands. This work was to develop an algorithm which accurately registers images in the presence of lung expansion. The lung registration method matched CT images of patients aided by vessel enhancement and information of individual lobe boundaries. The algorithm was tested on eighteen patients with lung collapse using physician-specified correspondences to measure registration error. The image registration algorithm developed in this work which was designed for challenging lung patients resulted in accuracy comparable to that of other methods when large lung changes are absent.

Download or read book Adaptive Radiation Therapy written by X. Allen Li and published by CRC Press. This book was released on 2011-01-27 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern medical imaging and radiation therapy technologies are so complex and computer driven that it is difficult for physicians and technologists to know exactly what is happening at the point-of-care. Medical physicists responsible for filling this gap in knowledge must stay abreast of the latest advances at the intersection of medical imaging an

Download or read book A Comparison of Deformable Image Registration Algorithms and Their Applicability to 4DCT Lung Images written by Brian Francis Loughery and published by . This book was released on 2012 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lung cancer is the most rampant killer among cancer types, taking more American lives per year than automobile accidents. This staggering annual death toll, along with our citizenry's unwillingness to cease smoking, has lead to a research emphasis on lung cancer treatment techniques. Real-time treatment of a lung tumor requires accurate tracking of both the Planning Target Volume and the nearby healthy radiosensitive tissues. An imaging modality called 4DCT (Four-Dimensional Computed Tomography) provides accurate tracking by taking a 3D image of the lungs at each phase of the breathing cycle. Once these images are taken, a method is required to link the many phases together and track changes between them. This method is called Image Registration. Standard techniques of Image Registration track these changes using rigid motions. Lungs move in a more complicated fashion than rigid methods can handle. A more detailed form of registration, Deformable Image Registration, is required for 4DCT tracking. Many deformable image registration algorithms exist. One must be chosen. The purpose of this paper is to experimentally compare four well-studied deformable image registration algorithms (Horn-Schunk Optical Flow, Thirion's Demons, and Yang's Inverse-Consistent versions of each) as they apply to 4DCT DICOM lung images. These four algorithms were coded in MATLAB with only user-created functionality. The algorithms, codes, and functions are explained in complete detail and alternative methods are provided. The algorithms were applied to ten patient data sets. Standard conditions for parameters were taken from previous recommendations. Tests were performed using Sum of Square Differences measurements to determine each algorithm's quantitative potential. The algorithms were also timed, and their final resulting images were saved for qualitative analysis. The results demonstrate that the optimal choice for Deformable Image Registration in 4DCT DICOM lung images, especially for small deformations, is the Inverse-Consistent version of Thirion's Demons algorithm. Inverse-Consistent Demons provided high quality images, a satisfactory computation speed, and the best quantitative potential.

Download or read book High performance Deformable Image Registration Algorithms for Manycore Processors written by James Shackleford and published by Morgan Kaufmann. This book was released on 2013 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: High Performance Deformable Image Registration Algorithms for Manycore Processors develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. Extracting useful information from 2D/3D data is essential to realizing key technologies underlying our daily lives. Examples include autonomous vehicles and humanoid robots that can recognize and manipulate objects in cluttered environments using stereo vision and laser sensing and medical imaging to localize and diagnose tumors in internal organs using data captured by CT/MRI scans. This book demonstrates: How to redesign widely used image registration algorithms so as to best expose the underlying parallelism available in these algorithms How to pose and implement the parallel versions of the algorithms within the single instruction, multiple data (SIMD) model supported by GPUs Programming "tricks" that can help readers develop other image processing algorithms, including registration algorithms for the GPU

Download or read book Artificial Intelligence in Radiation Oncology and Biomedical Physics written by Gilmer Valdes and published by CRC Press. This book was released on 2023-08-14 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics. AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. Finally, a futuristic look at the role of AI in radiation oncology is provided. This book brings medical physicists and radiation oncologists up to date with the most novel applications of machine learning to medical physics. Practitioners will appreciate the insightful discussions and detailed descriptions in each chapter. Its emphasis on clinical applications reaches a wide audience within the medical physics profession.

Download or read book Carbon Ion Radiotherapy written by Hirohiko Tsujii and published by Springer Science & Business Media. This book was released on 2013-12-25 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book serves as a practical guide for the use of carbon ions in cancer radiotherapy. On the basis of clinical experience with more than 7,000 patients with various types of tumors treated over a period of nearly 20 years at the National Institute of Radiological Sciences, step-by-step procedures and technological development of this modality are highlighted. The book is divided into two sections, the first covering the underlying principles of physics and biology, and the second section is a systematic review by tumor site, concentrating on the role of therapeutic techniques and the pitfalls in treatment planning. Readers will learn of the superior outcomes obtained with carbon-ion therapy for various types of tumors in terms of local control and toxicities. It is essential to understand that the carbon-ion beam is like a two-edged sword: unless it is used properly, it can increase the risk of severe injury to critical organs. In early series of dose-escalation studies, some patients experienced serious adverse effects such as skin ulcers, pneumonitis, intestinal ulcers, and bone necrosis, for which salvage surgery or hospitalization was required. To preclude such detrimental results, the adequacy of therapeutic techniques and dose fractionations was carefully examined in each case. In this way, significant improvements in treatment results have been achieved and major toxicities are no longer observed. With that knowledge, experts in relevant fields expand upon techniques for treatment delivery at each anatomical site, covering indications and optimal treatment planning. With its practical focus, this book will benefit radiation oncologists, medical physicists, medical dosimetrists, radiation therapists, and senior nurses whose work involves radiation therapy, as well as medical oncologists and others who are interested in radiation therapy.

Download or read book Imaging in non small cell lung cancer written by Yiyan Liu and published by Frontiers Media SA. This book was released on 2023-04-19 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Lung Imaging and Computer Aided Diagnosis written by Ayman El-Baz and published by CRC Press. This book was released on 2011-08-23 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lung cancer remains the leading cause of cancer-related deaths worldwide. Early diagnosis can improve the effectiveness of treatment and increase a patient’s chances of survival. Thus, there is an urgent need for new technology to diagnose small, malignant lung nodules early as well as large nodules located away from large diameter airways because the current technology—namely, needle biopsy and bronchoscopy—fail to diagnose those cases. However, the analysis of small, indeterminate lung masses is fraught with many technical difficulties. Often patients must be followed for years with serial CT scans in order to establish a diagnosis, but inter-scan variability, slice selection artifacts, differences in degree of inspiration, and scan angles can make comparing serial scans unreliable. Lung Imaging and Computer Aided Diagnosis brings together researchers in pulmonary image analysis to present state-of-the-art image processing techniques for detecting and diagnosing lung cancer at an early stage. The book addresses variables and discrepancies in scans and proposes ways of evaluating small lung masses more consistently to allow for more accurate measurement of growth rates and analysis of shape and appearance of the detected lung nodules. Dealing with all aspects of image analysis of the data, this book examines: Lung segmentation Nodule segmentation Vessels segmentation Airways segmentation Lung registration Detection of lung nodules Diagnosis of detected lung nodules Shape and appearance analysis of lung nodules Contributors also explore the effective use of these methodologies for diagnosis and therapy in clinical applications. Arguably the first book of its kind to address and evaluate image-based diagnostic approaches for the early diagnosis of lung cancer, Lung Imaging and Computer Aided Diagnosis constitutes a valuable resource for biomedical engineers, researchers, and clinicians in lung disease imaging.

Download or read book Markerless Lung Tumor Trajectory Estimation from Rotating Cone Beam Computed Tomography Projections written by Shufei Chen and published by . This book was released on 2016 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Respiration introduces large tumor motion in the thoracic region which influences treatment outcome for lung cancer patients. Tumor motion management techniques require characterization of temporal tumor motions because tumor motion varies patient to patient, day to day and cycle to cycle. This work develops a markerless algorithm to estimate 3 dimensional (3D) lung-tumor trajectories on free breathing cone beam computed tomography (CBCT) projections, which are 2 dimensional (2D) sequential images rotating about an axis and are used to reconstruct 3D CBCT images. A gold standard tumor trajectory is required to guide the algorithm development and estimate the tumor detection accuracy for markerless tracking algorithms. However, a sufficient strategy to validate markerless tracking algorithms is lacking. A validation framework is developed based on fiducial markers. Markers are segmented and marker trajectories are xiv obtained. The displacement of the tumor to the marker is calculated and added to the segmented marker trajectory to generate reference tumor trajectory. Markerless tumor trajectory estimation (MLTM) algorithm is developed and improved to acquire tumor trajectory with clinical acceptable accuracy for locally advanced lung tumors. The development is separate into two parts. The first part considers none tumor deformation. It investigates shape and appearance of the template, moreover, a constraint method is introduced to narrow down the template matching searching region for more precise matching results. The second part is to accommodate tumor deformation near the end of the treatment. The accuracy of MLTM is calculated and compared against 4D CBCT, which is the current standard of care. In summary, a validation framework based on fiducial markers is successfully built. MLTM is successfully developed with or without the consideration of tumor deformation with promising accuracy. MLTM outperforms 4D CBCT in temporal tumor trajectory estimation.

Download or read book Image Guided Radiotherapy of Lung Cancer written by James D. Cox and published by CRC Press. This book was released on 2007-09-20 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lung cancer is the leading cause of cancer death in the United States, but IGRT (image guided radiation therapy) offers the possibility of more aggressive and enhanced treatments. The only available source on the subject that emphasizes new imaging techniques, and provides step-by-step treatment guidelines for lung cancer, this source helps clinici

Download or read book A Neural Network Approach to Deformable Image Registration written by Elizabeth McKenzie and published by . This book was released on 2021 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deformable image registration (DIR) is an important component of a patient's radiation therapy treatment. During the planning stage it combines complementary information from different imaging modalities and time points. During treatment, it aligns the patient to a reproducible position for accurate dose delivery. As the treatment progresses, it can inform clinicians of important changes in anatomy which trigger plan adjustment. And finally, after the treatment is complete, registering images at subsequent time points can help to monitor the patient's health. The body's natural non-rigid motion makes DIR a complex challenge. Recently neural networks have shown impressive improvements in image processing and have been leveraged for DIR tasks. This thesis is a compilation of neural network-based approaches addressing lingering issues in medical DIR, namely 1) multi-modality registration, 2) registration with different scan extents, and 3) modeling large motion in registration. For the first task we employed a cycle consistent generative adversarial network to translate images in the MRI domain to the CT domain, such that the moving and target images were in a common domain. DIR could then proceed as a synthetically bridged mono-modality registration. The second task used advances in network-based inpainting to artificially extend images beyond their scan extent. The third task leveraged axial self-attention networks' ability to learn long range interactions to predict the deformation in the presence of large motion. For all these studies we used images from the head and neck, which exhibit all of these challenges, although these results can be generalized to other parts of the anatomy.The results of our experiments yielded networks that showed significant improvements in multi-modal DIR relative to traditional methods. We also produced network which can successfully predict missing tissue and demonstrated a DIR workflow that is independent of scan length. Finally, we trained a network whose accuracy is a balance between large and small motion prediction, and which opens the door to non-convolution-based DIR. By leveraging the power of artificial intelligence, we demonstrate a new paradigm in deformable image registration. Neural networks learn new patterns and connections in imaging data which go beyond the hand-crafted features of traditional image processing. This thesis shows how each step of registration, from the image pre-processing to the registration itself, can benefit from this exciting and cutting-edge approach.

Download or read book Markerless Lung Tumor Tracking Algorithms for Image Guided Radiation Therapy written by Timothy MIchael Rozario and published by . This book was released on 2015 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: According to a report published by the American Cancer Society in 2015, lung cancer accounts for almost 27% of all cancer related deaths in the United States. In the case of non-small cell lung cancer, radiation therapy "RT" is the common choice of treatment after surgery and it is often used in combination with surgery and/or chemotherapy for curative and palliative purposes. The emphasis in RT (external beam) is to deliver optimal high intensity tumoricidal doses to the lung tumor site while minimizing the deleterious effects to the neighboring healthy tissue. Imaging techniques are used to track the tumor displacements in order to improve the planning and treatment stages. However, there is no single imaging technique that achieves high-precision RT due to the following limiting factors: motion artifacts caused by respiratory-induced tumor motion, distortion and blurriness caused by radiation scatter, poor soft tissue resolution caused by strong surrounding signals and global discrepancies caused by the different imaging modalities. Hence, high-precision RT is paramount in gaining local control of the tumor, reducing the acute complications during the treatment, minimizing long-term complications, and ultimately ensuring the patient safety. Presented here are algorithmic models for computing lung tumors accurately on different imaging modalities such as conventional cone beam CT kV projection images and EPID MV projection images with beams-eye-view geometry using markerless strategies. Incorporated is a novel tile-shifting technique into the subtraction and fusion methods to locate the tumor on every projection image using simulated DRRs as reference. The lung tumor tracking models have been applied to over 7000 raw images and retrospectively to 5 multi-fractionated stereotactic body radiation therapy patients with high success despite the presence of strong surrounding signals. Other works that are reported encompass phylogenetic networks and computational geometry. We have published two software tools namely AOSE Angle Optimized Spring Embedder and drawTree ver 1.0 that draw maximized angular resolution graphs embedded in the plane and cost-effective swap-enabled orthogonal trees, respectively. Two other works that have been published in conjunction with this dissertation by Dr. Sergey Bereg are 1) The smallest Maximum-Weight Circle for Weighted points in the plane at The International Conference on Computational Science and Its Applications (ICCSA), Banff, Canada, 2015, and 2) A Decentralized Geometric Approach for the Formation Keeping in Unmanned Aircraft Navigation at The International Conference on Unmanned Aircraft Systems (ICUAS), Denver, USA, 2015.

Download or read book Utilizing Deformable Image Registration in Evaluation of Anatomic Changes Over the Course of Lung Cancer Stereotactic Body Radiation Therapy and the Accumulated Dosimetric Deviation written by 麥志恆 and published by . This book was released on 2017 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Medical Image Registration written by Joseph V. Hajnal and published by CRC Press. This book was released on 2001-06-27 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid

Download or read book Medical Imaging Informatics written by Alex A.T. Bui and published by Springer Science & Business Media. This book was released on 2009-12-01 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.

Download or read book Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning written by Rahul Paul and published by . This book was released on 2020 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lung cancer has a high incidence and mortality rate. The five-year relative survival rate for all lung cancers is 18%. Due to the high mortality and incidence rate of lung cancer worldwide, early detection is essential. Low dose Computed Tomography (CT) is a commonly used technique for screening, diagnosis, and prognosis of non-small cell lung cancer (NSCLC). The National Lung Screening Trial (NLST) compared low-dose helical computed tomography (LDCT) and standard chest radiography (CXR) for three annual screens and reported a 20% relative reduction in lung cancer mortality for LDCT compared to CXR. As such, LDCT screening for lung cancer is an effective way of mitigating lung cancer mortality and is the only imaging option for those at high risk. Lung cancer screening for high-risk patients often detects a large number of indeterminate pulmonary nodules, of which only a subset will be identified as cancer. As such, reliable and reproducible biomarkers determining which indeterminate pulmonary nodules will be identified as cancer would have significant translational implications as a therapeutic method to enhance lung cancer screening for nodule detection. Radiomics is an approach to extract high-dimensional quantitative features from medical images, which can be used individually or merged with clinical data for predictive and diagnostic analysis. Quantitative radiomics features (size, shape, and texture) extracted from lung CT scans have been shown to predict cancer incidence and prognosis. Deep learning is an emerging machine learning approach, which has been applied to the classification and analysis of various cancers and tumors. To generate generic features (blobs, edges, etc.) from an image, different convolutional kernels are applied over the input image, and then those generic feature-based images are passed through some fully connected neural layers. This category of the neural network is called a convolutional neural network (CNN), which has achieved high accuracy on image data. With the advancement of deep learning and convolutional neural networks (CNNs), deep features can be utilized to analyze lung CTs for prognosis prediction and diagnosis. In this dissertation, deep learning-based approaches were presented for lung nodule malignancy prediction. A subset of cases from the NLST was chosen as a dataset in our study. We experimented with three different pre-trained CNNs for extracting deep features and used five different classifiers. Experiments were also conducted with deep features from different color channels of a pre-trained CNN. Selected deep features were combined with radiomics features. Three CNNs were designed and trained. Combinations of features from pre-trained, CNNs trained on NLST data, and classical radiomics were used to build classifiers. The best accuracy (76.79%) was obtained using feature combinations. An area under the receiver operating characteristic curve of 0.87 was obtained using a CNN trained on an augmented NLST data cohort. After that, each of the three CNNs was trained using seven different seeds to create the initial weights. These enabled variability in the CNN models, which were combined to generate a robust, more accurate ensemble model. Augmenting images using only rotation and flipping and training with images from T0 yielded the best accuracy to predict lung cancer incidence at T2 from a separate test cohort (Accuracy = 90.29%; AUC = 0.96) based on an ensemble 21 models. From this research, five conclusions were obtained, which will be utilized in future research. First, we proposed a simple and effective CNN architecture with a small number of parameters useful for smaller (medical) datasets. Second, we showed features obtained using transfer learning with all the channels of a pre-trained CNN performed better than features extracted using any single channel and we also constructed a new feature set by fusing quantitative features with deep features, which in turn enhanced classification performance. Third, ensemble learning with deep neural networks was a compelling approach that accurately predicted lung cancer incidence at the second screening after the baseline screen, mostly two years later. Fourth, we proposed a method for deep features to have a recognizable definition via semantic or quantitative features. Fifth, deep features were dependent on the scanner parameters, and the dependency was removed using pixel size based normalization.

Download or read book Incorporating Sheet likeness Information in Intensity based Lung CT Image Registration written by Yang Wook Kim and published by . This book was released on 2013 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image registration is a useful technique to measure the change between two or more images. Lung CT image registration is widely used an non-invasive method to measure the lung function changes. Non-invasive lung function measurement accuracy highly depends on lung CT image registration accuracy. Improving the registration accuracy is an important issue. In this thesis, we propose incorporating information of the anatomical structure of the lung (fissures) as an additional cost function of the lung CT image registration. The intensity-based similarity measurement method (sum of the squared tissue volume differences) is also used to complement lung tissue information matching. However, since fissures are hard to segment, a sheet-likeness filter is applied to detect fissure-like structures. Sheet-likeness is used as an additional cost function of the intensity-based registration. The registration accuracy is verified by the visual assessment and landmark error measurement. The landmark error measurement can show an improvement of the proposed algorithm.