Abstraction and specification in program development

This paper provides an overview of image registration and data fusion techniques used in radiation therapy, and examples of their use. They are used at all stages of the patient management process; for initial diagnosis and staging, during treatment planning and delivery, and after therapy to help monitor the patients' response to treatment. Most treatment planning systems now support some form of interactive or automated image registration and provide tools for mapping information, such as tissue outlines and computed dose from one imaging study to another. To complement this, modern treatment delivery systems offer means for acquiring and registering 2D and 3D image data at the treatment unit to aid patient setup. Techniques for adapting and customizing treatments during the course of therapy using 3D and 4D anatomic and functional imaging data are currently being introduced into the clinic. These techniques require sophisticated image registration and data fusion technology to accumulate properly the delivered dose and to analyse possible physiological and anatomical changes during treatment. Finally, the correlation of radiological changes after therapy with delivered dose also requires the use of image registration and fusion techniques.

Image registration and data fusion in radiation therapy

The purpose of this study was to investigate the number of intermediate states required to adequately approximate the clinically relevant cumulative dose to deforming/moving thoracic anatomy in four-dimensional (4D) conformal radiotherapy that uses 6 MV photons to target tumors. Four patients were involved in this study. For the first three patients, computed tomography images acquired at exhale and inhale were available; they were registered using B-spline deformation model and the computed transformation was further used to simulate intermediate states between exhale and inhale. For the fourth patient, 4D-acquired, phase-sorted datasets were available and each dataset was registered with the exhale dataset. The exhale-inhale transformation was also used to simulate intermediate states in order to compare the cumulative doses computed using the actual and the simulated datasets. Doses to each state were calculated using the Dose Planning Method (DPM) Monte Carlo code and dose was accumulated for scoring on the exhale anatomy via the transformation matrices for each state and time weighting factors. Cumulative doses were estimated using increasing numbers of intermediate states and compared to simpler scenarios such as a '2-state' model which used only the exhale and inhale datasets or the dose received during the average phase of themore » breathing cycle. Dose distributions for each modeled state as well as the cumulative doses were assessed using dose volume histograms and several treatment evaluation metrics such as mean lung dose, normal tissue complication probability, and generalized uniform dose. Although significant 'point dose' differences can exist between each breathing state, the differences decrease when cumulative doses are considered, and can become less significant yet in terms of evaluation metrics depending upon the clinical end point. This study suggests that for certain ''clinical'' end points of importance for lung cancer, satisfactory predictions of accumulated total dose to be received by the distorting anatomy can be achieved by calculating the dose to but a few (or even simply the average) phases of the breathing cycle.« less

/pdf/how-extensive-of-a-4d-dataset-is-needed-to-estimate-hzpuqlzqk3.pdf

How extensive of a 4D dataset is needed to estimate cumulative dose distribution plan evaluation metrics in conformal lung therapy

In lung cancer screening, benign and malignant nodules can be classified through nodule growth assessment by the registration and, then, subtraction between follow-up computed tomography scans. During the registration, the volume of nodule regions in the floating image should be preserved, whereas the volume of other regions in the floating image should be aligned to that in the reference image. However, ground glass opacity (GGO) nodules are very elusive to automatically segment due to their inhomogeneous interior. In other words, it is difficult to automatically define the volume-preserving regions of GGO nodules. In this paper, we propose an accurate and fast nonrigid registration method. It applies the volume-preserving constraint to candidate regions of GGO nodules, which are automatically detected by gray-level cooccurrence matrix (GLCM) texture analysis. Considering that GGO nodules can be characterized by their inner inhomogeneity and high intensity, we identify the candidate regions of GGO nodules based on the homogeneity values calculated by the GLCM and the intensity values. Furthermore, we accelerate our nonrigid registration by using Compute Unified Device Architecture (CUDA). In the nonrigid registration process, the computationally expensive procedures of the floating-image transformation and the cost-function calculation are accelerated by using CUDA. The experimental results demonstrated that our method almost perfectly preserves the volume of GGO nodules in the floating image as well as effectively aligns the lung between the reference and floating images. Regarding the computational performance, our CUDA-based method delivers about 20× faster registration than the conventional method. Our method can be successfully applied to a GGO nodule follow-up study and can be extended to the volume-preserving registration and subtraction of specific diseases in other organs (e.g., liver cancer).

GGO Nodule Volume-Preserving Nonrigid Lung Registration Using GLCM Texture Analysis

The performance of the ANIMAL ! Automated Nonlinear Image Matching and Anatomical Labeling" nonlinear registration algorithm for registration of thoracic 4D CT images was investigated. The algorithm was modified to minimize the incidence of deformation vector discontinuities that occur during the registration of lung images. Registrations were performed between the inhale and exhale phases for five patients. The registration accuracy was quantified by the cross-correlation of transformed and target images and distance to agreement ! DTA" measured based on anatomical landmarks and triangulated surfaces constructed from manual contours. On average, the vector DTA between transformed and target landmarks was 1.6 mm. Comparing transformed and target 3D triangulated surfaces derived from planning contours, the average target volume ! GTV" centerof-mass shift was 2.0 mm and the 3D DTA was 1.6 mm. An average DTA of 1.8 mm was obtained for all planning structures. All DTA metrics were comparable to inter observer uncertainties established for landmark identification and manual contouring. © 2007 American Association of Physicists in Medicine. # DOI: 10.1118/1.2795824$

Quantification of accuracy of the automated nonlinear image matching and anatomical labeling (ANIMAL) nonlinear registration algorithm for 4D CT images of lung.

In previous work on SafeJava we presented a type system extension to the Java source language that statically prevents data races and deadlocks in multithreaded programs. SafeJava is expressive enough to support common programming patterns, its type checking is fast and scalable, and it requires little programming overhead. SafeJava thus offers a promising approach for making multithreaded programs more reliable. This paper presents a corresponding type system extension for the Java virtual machine language (JVML). We call the resulting language SafeJVML. Well-typed SafeJVML programs are guaranteed to be free of data races and deadlocks. Designing a corresponding type system for JVML is important because most Java code is shipped in the JVML format. Designing acorresponding type system for JVML is nontrivial because of important differences between Java and JVML. In particular, the absence of block structure in JVML programs and the fact that they do not use named local variables the way Java programs do make the type systems for Java and JVML significantly different. For example, verifying absence of races and deadlocks in JVML programs requires performing an alias analysis, something that was not necessary for verifying absence of races and deadlocks in Java programs. This paper presents static and dynamic semantics for Safe JVML. It also includes a proof that the SafeJVML type system is sound and that it prevents data races and deadlocks. To the best of our knowledge, this is the first type system for JVML that statically ensures absence of synchronization errors.

/pdf/a-type-system-for-preventing-data-races-and-deadlocks-in-the-b4clrxhpd5.pdf

A type system for preventing data races and deadlocks in the java virtual machine language: 1

Glass box software model checking incorporates novel techniques to identify similarities in the state space of a model checker and safely prune large numbers of redundant states without explicitly checking them. It is significantly more efficient than other software model checking approaches for checking certain kinds of programs and program properties.This paper presents Pipal, a system for modular glass box software model checking. Extending glass box software model checking to perform modular checking is important to further improve its scalability. It is nontrivial because unlike traditional software model checkers such as Java PathFinder (JPF) and CMC, a glass box software model checker does not check every state separately---instead, it checks a large set of states together in each step. We present a solution and demonstrate Pipal's effectiveness on a variety of programs.

/pdf/efficient-modular-glass-box-software-model-checking-4fvkereijk.pdf

Efficient modular glass box software model checking

Model checking is a formal verification technique that exhaustively tests a piece of hardware or software on all possible inputs (usually up to a given size) and on all possible nondeterministic schedules. For hardware, model checkers have successfully verified fairly complex finite state control circuits with up to a few hundred bits of state information; but not circuits in general that have large data paths or memories. Similarly, for software, model checkers have primarily verified control-oriented programs with respect to temporal properties; but not much work has been done to verify data-oriented programs with respect to complex data-dependent properties. 
This dissertation presents glass box software model checking , a novel software model checking approach that achieves a high degree of state space reduction in the presence of complex data-dependent properties. Our key insight is that there are classes of operations that affect a program's state in similar ways. By discovering these similarities, we dramatically reduce the state space of our model checker by checking each class of states in a single step. To achieve this state space reduction, we employ a dynamic analysis to detect similar state transitions and a static analysis to check the entire class of transitions. These analyses employ a symbolic execution technique that increases their effectiveness. 
We also present a modular extension to glass box software model checking, to further improve the efficiency of checking large programs composed of multiple modules. In a modular checking approach program modules are replaced with abstract implementations, which are functionally equivalent but vastly simplified versions of the modules. 
We also apply the glass box model checking technique to the problem of checking type soundness. Since proving type soundness can be extremely difficult, a model checking approach takes a considerable burden off the language designer. 
Our experimental results indicate that glass box model checking is efficient and effective at checking a variety of programs and properties, including program invariants, equivalence to an abstraction, and type soundness. Comparisons with other model checking techniques show that our technique is more efficient at checking these programs and properties.

Glass box software model checking

Image Registration and Data Fusion for Radiotherapy Treatment Planning

Purpose: Existing methods for deformable image registration typically use homogeneous regularization to encourage global smoothness. Less work has been done to incorporate voxel‐level tissue‐specific elasticity information. Ignoring differences in elasticity can, however, result in non‐physiological registrations, such as bone warping. We propose an approach to incorporate tissue rigidity information using a spatial variant regularization. Method and Materials: Regularized image registration algorithms estimate the deformation by minimizing a cost function, consisting of a dis‐similarity metric and regularization. To account for tissue‐type‐dependent rigidity information, we incorporate into the cost function a non‐rigidity penalty: an integral of stiffness index for local deformation weighted by spatial variant regularization factor depending on tissue type. For CT data, a simple monotonic increasing function of the CT number is used as a rigidity index for local tissue type. A necessary and sufficient condition for stiff local deformation is derived, and the local non‐stiffness is measured by the deviation of local Jacobian from orthnormality using the Frobenius norm. Tensor B‐Splines are used to parameterize the deformation field. A multi‐resolution scheme and gradient‐based approach are applied for optimization. Performance was accessed by registering 3D thorax CT‐images obtained from different breathing phases. Results: Experiments with clinical data demonstrate higher accuracy for inhale‐exhale thorax CT registration with the proposed approach. We observe comparable intensity match as the unregularized approaches, but more physiologically reasonable results with respect to different tissue types; in particular bone warping phenomena is eliminated in general. Conclusion: This work provides a way to incorporate tissue‐type‐dependent information into deformable registration framework with regularization design. Inference from image intensity avoids explicit segmentation, and is robust to partial volume effect. Our formulation based on local Jacobian and Frobenius norm provides analytical expression for the regularization and its derivative. More physiological results are achieved with minor computation expense. Supported by NIH P01‐CA59827.

TU‐C‐ValA‐04: Deformable Registration Using Regularization That Accommodates Local Tissue Rigidity

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Michael Roberson

Author Tools

Chat about Author