Development of an integrated biomechanics informatics system with knowledge discovery and decision support tools for research of injury prevention and performance enhancement.

The field of biomechanics involves integrating a variety of data types such as waveform, video, discrete, and performance. These different sources of data must be efficiently and accurately associated to provide meaningful feedback to athletes, coaches, and healthcare professionals to prevent injury and improve rehabilitation/performance. There are many challenges in biomechanics research such as data storage, standardization, review, sharing, and accessibility. Data is stored in different formats, structures, and locations such as physical hard drives or Dropbox/Google Drive, leading to issues during sharing and collaboration. Data is reviewed and analyzed through different software applications that need to be downloaded and installed locally before they are available for use. An integrated biomechanics informatics system (IBIS) built based on the core principles in medical imaging informatics provides a solution to many of these challenges. The system provides a secure web-based platform that will be accessible remotely for authenticated users to upload, share, and download data. The web-based application includes built-in data viewers that are streamlined for reviewing multimedia data and decision support/knowledge discovery tools. These tools include automatic foot contact detection for pre-processing, built-in statistical analysis applications for longitudinal and cross-study analysis, and a multi-institutional collaboration module. The IBIS system creates a centralized hub to support multi-institutional collaborative biomechanics research and analysis that is remotely accessible to all users including athletes, coaches, researchers, and clinicians generating a novel streamlined research workflow, data analysis, and knowledge discovery process.

Genetic Factors, Brain Atrophy, and Response to Rehabilitation Therapy After Stroke.

OBJECTIVE: Patients show substantial differences in response to rehabilitation therapy after stroke. We hypothesized that specific genetic profiles might explain some of this variance and, secondarily, that genetic factors are related to cerebral atrophy post-stroke. METHODS: The phase 3 ICARE study examined response to motor rehabilitation therapies. In 216 ICARE enrollees, DNA was analyzed for presence of the BDNF val66met and the ApoE ε4 polymorphism. The relationship of polymorphism status to 12-month change in motor status (Wolf Motor Function Test, WMFT) was examined. Neuroimaging data were also evaluated (n=127). RESULTS: Subjects were 61±13 years old (mean±SD) and enrolled 43±22 days post-stroke; 19.7% were BDNF val66met carriers and 29.8% ApoE ε4 carriers. Carrier status for each polymorphism was not associated with WMFT, either at baseline or over 12 months of follow-up. Neuroimaging, acquired 5±11 days post-stroke, showed that BDNF val66met polymorphism carriers had a 1.34-greater degree of cerebral atrophy compared to non-carriers (P=.01). Post hoc analysis found that age of stroke onset was 4.6 years younger in subjects with the ApoE ε4 polymorphism (P=.02). CONCLUSION: Neither the val66met BDNF nor ApoE ε4 polymorphism explained inter-subject differences in response to rehabilitation therapy. The BDNF val66met polymorphism was associated with cerebral atrophy at baseline, echoing findings in healthy subjects, and suggesting an endophenotype. The ApoE ε4 polymorphism was associated with younger age at stroke onset, echoing findings in Alzheimer's disease and suggesting a common biology. Genetic associations provide insights useful to understanding the biology of outcomes after stroke.

Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.

Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as glaucoma. Many recent works formulated the OD and OC segmentation as a pixel classification task. However, it is hard for these methods to explicitly model the spatial relations between the labels in the output mask. Furthermore, the proportion of the background, OD and OC are unbalanced which also may result in a biased model as well as introduce more noise. To address these problems, we developed an approach that follows a coarse-to-fine segmentation process. We start with a U-Net to obtain a rough segmenting boundary and then crop the area around the boundary to form a boundary contour centered image. Second, inspired by sequence labeling tasks in natural language processing, we regard the OD and OC segmentation as a sequence labeling task and propose a novel fully convolutional network called SU-Net and combine it with the Viterbi algorithm to jointly decode the segmentation boundary. We also introduced a geometric parameter-based data augmentation method to generate more training samples in order to minimize the differences between training and test sets and reduce overfitting. Experimental results show that our method achieved state-of-the-art results on 2 datasets for both OD and OC segmentation and our method outperforms most of the ophthalmologists in terms of achieving agreement out of 6 ophthalmologists on the MESSIDOR dataset for both OD and OC segmentation. In terms of glaucoma screening, we achieved the best cup-to-disc ratio (CDR) error and area under the ROC curve (AUC) for glaucoma classification on the Drishti-GS dataset.

Stroke Lesions in a Large Upper Limb Rehabilitation Trial Cohort Rarely Match Lesions in Common Preclinical Models.

BACKGROUND: Stroke patients with mild-moderate upper extremity motor impairments and minimal sensory and cognitive deficits provide a useful model to study recovery and improve rehabilitation. Laboratory-based investigators use lesioning techniques for similar goals. OBJECTIVE: To determine whether stroke lesions in an upper extremity rehabilitation trial cohort match lesions from the preclinical stroke recovery models used to drive translational research. METHODS: Clinical neuroimages from 297 participants enrolled in the Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) study were reviewed. Images were characterized based on lesion type (ischemic or hemorrhagic), volume, vascular territory, depth (cortical gray matter, cortical white matter, subcortical), old strokes, and leukoaraiosis. Lesions were compared with those of preclinical stroke models commonly used to study upper limb recovery. RESULTS: Among the ischemic stroke participants, median infarct volume was 1.8 mL, with most lesions confined to subcortical structures (61%) including the anterior choroidal artery territory (30%) and the pons (23%). Of ICARE participants, <1% had lesions resembling proximal middle cerebral artery or surface vessel occlusion models. Preclinical models of subcortical white matter injury best resembled the ICARE population (33%). Intracranial hemorrhage participants had small (median 12.5 mL) lesions that best matched the capsular hematoma preclinical model. CONCLUSIONS: ICARE subjects are not representative of all stroke patients, but they represent a clinically and scientifically important subgroup. Compared with lesions in general stroke populations and widely studied animal models of recovery, ICARE participants had smaller, more subcortically based strokes. Improved preclinical-clinical translational efforts may require better alignment of lesions between preclinical and human stroke recovery models.

Knowledge-driven decision support for assessing dose distributions in radiation therapy of head and neck cancer.

PURPOSE: Clinical data that are generated through routine radiation therapy procedures can be leveraged as a source of knowledge to provide evidence-based decision support for future patients. Treatment planning in radiation therapy often relies on trial-and-error iterations, experience, judgment calls and general guidelines. The authors present a knowledge-driven decision support system that assists clinicians by reducing some of the uncertainties associated with treatment planning and provides quantified empirical estimates to help minimize the radiation dose to healthy critical structures surrounding the tumor. METHODS: A database of retrospective DICOM RT data fuels a decision support engine, which assists clinicians in selecting dose constraints and assessing dose distributions. The first step is to quantify the spatial relationships between the tumor and surrounding critical structures through features that account for distance, volume, overlap, location, shape and orientation. These features are used to identify database cases that are anatomically similar to the new patient. The dose profiles of these database cases can help clinicians to estimate an acceptable dose distribution for the new case, based on empirical evidence. Since database diversity is essential for good system performance, an infrastructure for multi-institutional collaboration was also conceptualized in order to pave the way for data sharing of protected health information. RESULTS: A set of 127 retrospective test cases was collected from a single institution in order to conduct a leave-one-out evaluation of the decision support module. In 72 % of these retrospective test cases, patients with similar tumor anatomy were also found to exhibit similar radiation dose distributions. This demonstrates the system's ability to successfully extract retrospective database cases that can estimate the new patient's dose distribution. CONCLUSION: The radiation therapy treatment planning decision support system presented here can assist clinicians in determining good dose constraints and assessing dose distributions by using knowledge gained from retrospective treatment plans.

Development of a novel imaging informatics-based system with an intelligent workflow engine (IWEIS) to support imaging-based clinical trials.

Imaging based clinical trials can benefit from a solution to efficiently collect, analyze, and distribute multimedia data at various stages within the workflow. Currently, the data management needs of these trials are typically addressed with custom-built systems. However, software development of the custom-built systems for versatile workflows can be resource-consuming. To address these challenges, we present a system with a workflow engine for imaging based clinical trials. The system enables a project coordinator to build a data collection and management system specifically related to study protocol workflow without programming. Web Access to DICOM Objects (WADO) module with novel features is integrated to further facilitate imaging related study. The system was initially evaluated by an imaging based rehabilitation clinical trial. The evaluation shows that the cost of the development of system can be much reduced compared to the custom-built system. By providing a solution to customize a system and automate the workflow, the system will save on development time and reduce errors especially for imaging clinical trials.

Design and development of an ethnically-diverse imaging informatics-based eFolder system for multiple sclerosis patients.

PURPOSE: MRI has been used to identify multiple sclerosis (MS) lesions in brain and spinal cord visually. Integrating patient information into an electronic patient record system has become key for modern patient care in medicine in recent years. Clinically, it is also necessary to track patients' progress in longitudinal studies, in order to provide comprehensive understanding of disease progression and response to treatment. As the amount of required data increases, there exists a need for an efficient systematic solution to store and analyze MS patient data, disease profiles, and disease tracking for both clinical and research purposes. METHOD: An imaging informatics based system, called MS eFolder, has been developed as an integrated patient record system for data storage and analysis of MS patients. The eFolder system, with a DICOM-based database, includes a module for lesion contouring by radiologists, a MS lesion quantification tool to quantify MS lesion volume in 3D, brain parenchyma fraction analysis, and provide quantitative analysis and tracking of volume changes in longitudinal studies. Patient data, including MR images, have been collected retrospectively at University of Southern California Medical Center (USC) and Los Angeles County Hospital (LAC). The MS eFolder utilizes web-based components, such as browser-based graphical user interface (GUI) and web-based database. The eFolder database stores patient clinical data (demographics, MS disease history, family history, etc.), MR imaging-related data found in DICOM headers, and lesion quantification results. Lesion quantification results are derived from radiologists' contours on brain MRI studies and quantified into 3-dimensional volumes and locations. Quantified results of white matter lesions are integrated into a structured report based on DICOM-SR protocol and templates. The user interface displays patient clinical information, original MR images, and viewing structured reports of quantified results. The GUI also includes a data mining tool to handle unique search queries for MS. System workflow and dataflow steps has been designed based on the IHE post-processing workflow profile, including workflow process tracking, MS lesion contouring and quantification of MR images at a post-processing workstation, and storage of quantitative results as DICOM-SR in DICOM-based storage system. The web-based GUI is designed to display zero-footprint DICOM web-accessible data objects (WADO) and the SR objects. SUMMARY: The MS eFolder system has been designed and developed as an integrated data storage and mining solution in both clinical and research environments, while providing unique features, such as quantitative lesion analysis and disease tracking over a longitudinal study. A comprehensive image and clinical data integrated database provided by MS eFolder provides a platform for treatment assessment, outcomes analysis and decision-support. The proposed system serves as a platform for future quantitative analysis derived automatically from CAD algorithms that can also be integrated within the system for individual disease tracking and future MS-related research. Ultimately the eFolder provides a decision-support infrastructure that can eventually be used as add-on value to the overall electronic medical record.

Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis.

Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant's structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant's non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.

Effective staging of fibrosis by the selected texture features of liver: Which one is better, CT or MR imaging?

PURPOSE: Texture patterns of hepatic fibrosis are one of the important biomarkers to diagnose and classify chronic liver disease from initial to end stage on computed tomography (CT) or magnetic resonance (MR) images. Computer-aided diagnosis (CAD) of liver cirrhosis using texture features has become popular in recent research advances. To date, however, properly selecting effective texture features and image parameters is still mostly undetermined and not well-defined. In this study, different types of datasets acquired from CT and MR images are investigated to select the optimal parameters and features for the proper classification of fibrosis. METHODS: A total of 149 patients were scanned by multi-detector computed tomography (MDCT) and 218 patients were scanned using 1.5T and 3T superconducting MR scanners for an abdominal examination. All cases were verified by needle biopsies as the gold standard of our experiment, ranging from 0 (no fibrosis) to 5 (cirrhosis). For each case, at least four sequenced phase images are acquired by CT or MR scanners: pre-contrast, arterial, portal venous and equilibrium phase. For both imaging modalities, 15 texture features calculated from gray level co-occurrence matrix (GLCM) are extracted within an ROI in liver as one set of input vectors. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: non-cirrhosis or cirrhosis. In addition, 10 ROIs in the liver are manually selected in a disperse manner by experienced radiologist from each sequenced image and each of the 15 features are averaged across the 10 ROIs for each case to reduce the validation time. The number of input items is selected from the various combinations of 15 features, from which the accuracy rate (AR) is calculated by counting the percentage of correct answers on each combination of features aggregated to determine a liver stage score and then compared to the gold standard. RESULTS: According to the accuracy rate (AR) calculated from each combination, the optimal number of texture features to classify liver fibrosis degree ranges from 4 to 7, no matter which modality was utilized. The overall performance calculated by the average sum of maximum AR value of all 15 features is 66.83% in CT images, while 68.14%, and 71.98% in MR images, respectively; among the 15 texture features, mean gray value and entropy are the most commonly used features in all 3 imaging datasets. The correlation feature has the lowest AR value and was removed as an effective feature in all datasets. AR value tends to increase with the injection of contrast agency, and both CT and MR images reach the highest AR performance during the equilibrium phase. CONCLUSIONS: Comparing the accuracy of classification with two imaging modalities, the MR images have an advantage over CT images with regards to AR performance of the 15 selected texture features, while 3T MRI is better than 1.5T MRI to classify liver fibrosis. Finally, the texture analysis is more effective during equilibrium phase than in any of the other phased images.

Multidimensional Interactive Radiology Report and Analysis: Standardization of workflow and reporting for renal mass tracking and quantification.

A conventional radiology report primarily consists of a large amount of unstructured text, and lacks clear, concise, consistent and content-rich information. Hence, an area of unmet clinical need consists of developing better ways to communicate radiology findings and information specific to each patient. Here, we design a new workflow and reporting system that combines and integrates advances in engineering technology with those from the medical sciences, the Multidimensional Interactive Radiology Report and Analysis (MIRRA). Until recently, clinical standards have primarily relied on 2D images for the purpose of measurement, but with the advent of 3D processing, many of the manually measured metrics can be automated, leading to better reproducibility and less subjective measurement placement. Hence, we make use this newly available 3D processing in our workflow. Our pipeline is used here to standardize the labeling, tracking, and quantifying of metrics for renal masses.

Medical imaging informatics simulators: a tutorial.

PURPOSE: A medical imaging informatics infrastructure (MIII) platform is an organized method of selecting tools and synthesizing data from HIS/RIS/PACS/ePR systems with the aim of developing an imaging-based diagnosis or treatment system. Evaluation and analysis of these systems can be made more efficient by designing and implementing imaging informatics simulators. This tutorial introduces the MIII platform and provides the definition of treatment/diagnosis systems, while primarily focusing on the development of the related simulators. METHODS: A medical imaging informatics (MII) simulator in this context is defined as a system integration of many selected imaging and data components from the MIII platform and clinical treatment protocols, which can be used to simulate patient workflow and data flow starting from diagnostic procedures to the completion of treatment. In these processes, DICOM and HL-7 standards, IHE workflow profiles, and Web-based tools are emphasized. From the information collected in the database of a specific simulator, evidence-based medicine can be hypothesized to choose and integrate optimal clinical decision support components. Other relevant, selected clinical resources in addition to data and tools from the HIS/RIS/PACS and ePRs platform may also be tailored to develop the simulator. These resources can include image content indexing, 3D rendering with visualization, data grid and cloud computing, computer-aided diagnosis (CAD) methods, specialized image-assisted surgical, and radiation therapy technologies. RESULTS: Five simulators will be discussed in this tutorial. The PACS-ePR simulator with image distribution is the cradle of the other simulators. It supplies the necessary PACS-based ingredients and data security for the development of four other simulators: the data grid simulator for molecular imaging, CAD-PACS, radiation therapy simulator, and image-assisted surgery simulator. The purpose and benefits of each simulator with respect to its clinical relevance are presented. CONCLUSION: The concept, design, and development of these five simulators have been implemented in laboratory settings for education and training. Some of them have been extended to clinical applications in hospital environments.

A DICOM-based 2nd generation Molecular Imaging Data Grid implementing the IHE XDS-i integration profile.

PURPOSE: A Molecular Imaging Data Grid (MIDG) was developed to address current informatics challenges in archival, sharing, search, and distribution of preclinical imaging studies between animal imaging facilities and investigator sites. This manuscript presents a 2nd generation MIDG replacing the Globus Toolkit with a new system architecture that implements the IHE XDS-i integration profile. Implementation and evaluation were conducted using a 3-site interdisciplinary test-bed at the University of Southern California. METHODS: The 2nd generation MIDG design architecture replaces the initial design's Globus Toolkit with dedicated web services and XML-based messaging for dedicated management and delivery of multi-modality DICOM imaging datasets. The Cross-enterprise Document Sharing for Imaging (XDS-i) integration profile from the field of enterprise radiology informatics was adopted into the MIDG design because streamlined image registration, management, and distribution dataflow are likewise needed in preclinical imaging informatics systems as in enterprise PACS application. Implementation of the MIDG is demonstrated at the University of Southern California Molecular Imaging Center (MIC) and two other sites with specified hardware, software, and network bandwidth. RESULTS: Evaluation of the MIDG involves data upload, download, and fault-tolerance testing scenarios using multi-modality animal imaging datasets collected at the USC Molecular Imaging Center. The upload, download, and fault-tolerance tests of the MIDG were performed multiple times using 12 collected animal study datasets. Upload and download times demonstrated reproducibility and improved real-world performance. Fault-tolerance tests showed that automated failover between Grid Node Servers has minimal impact on normal download times. CONCLUSIONS: Building upon the 1st generation concepts and experiences, the 2nd generation MIDG system improves accessibility of disparate animal-model molecular imaging datasets to users outside a molecular imaging facility's LAN using a new architecture, dataflow, and dedicated DICOM-based management web services. Productivity and efficiency of preclinical research for translational sciences investigators has been further streamlined for multi-center study data registration, management, and distribution.

An observer study for a computer-aided reading protocol (CARP) in the screening environment for digital mammography.

RATIONALE AND OBJECTIVES: The aims of this study were to investigate improving work flow efficiency by shortening the reading time of digital mammograms using a computer-aided reading protocol (CARP) in the screening environment and to increase detection sensitivity using CARP, compared to the current protocol, commonly referred to as the quadrant view (QV). MATERIALS AND METHODS: A total of 200 cases were selected for a receiver-operating characteristic (ROC) study to evaluate two image display work flows, CARP and QV, in the screening environment. A Web-based tool was developed for scoring, reporting, and statistical analysis. Cases were scored for and stratified by difficulty. A total of six radiologists of differing levels of training ranging from dedicated mammographers to senior radiology residents participated. Each was timed while interpreting the 200 cases in groups of 50, first using QV and then, after a washout period, using CARP. The data were analyzed using ROC and κ analysis. Interpretation times were also assessed. RESULTS: Using QV, readers' average area under the ROC curve was 0.68 (range, 0.54-0.73). Using CARP, readers' average area under the ROC curve was 0.71 (range, 0.66-0.75). There was no statistically significant difference in reader performance using either work flow. However, there was a statistically significant reduction in the average interpretation time of negative cases from 64.7 seconds using QV to 58.8 seconds using CARP. CONCLUSIONS: CARP determines the display order of regions of interest depending on computer-aided detection findings. This is a variation of traditional computer-aided detection for digital mammography that has the potential to reduce interpretation times of studies with negative findings without significantly affecting sensitivity, thus allowing improved work flow efficiency in the screening environment, in which, in most settings, the majority of cases are negative.

Intelligent ePR system for evidence-based research in radiotherapy: proton therapy for prostate cancer.

PURPOSE: Proton therapy (PT) utilizes high energy particle proton beam to kill cancer cells at the target region for target cancer therapy. Due to the physical properties of the proton beam, PT delivers dose with higher precision and no exit dose compared to conventional radiotherapy. In PT, patient data are distributed among multiple systems, a hindrance to research on efficacy and effectiveness. A data mining method and a treatment plan navigator utilizing the infrastructure and data repository of a PT electronic patient record (ePR) was developed to minimize radiation toxicity and improve outcomes in prostate cancer treatment. MATERIALS/METHOD(S): The workflow of a proton therapy treatment in a radiation oncology department was reviewed, and a clinical data model and data flow were designed. A prototype PT ePR system with DICOM compliance was developed to manage prostate cancer patient images, treatment plans, and related clinical data. The ePR system consists of four main components: (1) Data Gateway; (2) ePR Server; (3) Decision Support Tools; and (4) Visualization and Display Tools. Decision support and visualization tools are currently developed based on DICOM images, DICOM-RT and DICOM-RT-ION objects data from prostate cancer patients treated with hypofractionation protocol proton therapy were used for evaluating ePR system effectiveness. Each patient data set includes a set of computed tomography (CT) DICOM images and four DICOM-RT and RT-ION objects. In addition, clinical outcomes data collected from PT cases were included to establish a knowledge base for outcomes analysis. RESULTS: A data mining search engine and an intelligent treatment plan navigator (ITPN) were developed and integrated with the ePR system. Evaluation was based on a data set of 39 PT patients and a hypothetical patient. CONCLUSIONS: The ePR system was able to facilitate the proton therapy workflow. The PT ePR system was feasible for prostate cancer patient treated with hypofractionation protocol in proton therapy. This ePR system improves efficiency in data collection and integration to facilitate outcomes analysis.

MIDG-Emerging grid technologies for multi-site preclinical molecular imaging research communities.

PURPOSE: Molecular imaging is the visualization and identification of specific molecules in anatomy for insight into metabolic pathways, tissue consistency, and tracing of solute transport mechanisms. This paper presents the Molecular Imaging Data Grid (MIDG) which utilizes emerging grid technologies in preclinical molecular imaging to facilitate data sharing and discovery between preclinical molecular imaging facilities and their collaborating investigator institutions to expedite translational sciences research. Grid-enabled archiving, management, and distribution of animal-model imaging datasets help preclinical investigators to monitor, access and share their imaging data remotely, and promote preclinical imaging facilities to share published imaging datasets as resources for new investigators. METHODS: The system architecture of the Molecular Imaging Data Grid is described in a four layer diagram. A data model for preclinical molecular imaging datasets is also presented based on imaging modalities currently used in a molecular imaging center. The MIDG system components and connectivity are presented. And finally, the workflow steps for grid-based archiving, management, and retrieval of preclincial molecular imaging data are described. RESULTS: Initial performance tests of the Molecular Imaging Data Grid system have been conducted at the USC IPILab using dedicated VMware servers. System connectivity, evaluated datasets, and preliminary results are presented. The results show the system's feasibility, limitations, direction of future research. CONCLUSIONS: Translational and interdisciplinary research in medicine is increasingly interested in cellular and molecular biology activity at the preclinical levels, utilizing molecular imaging methods on animal models. The task of integrated archiving, management, and distribution of these preclinical molecular imaging datasets at preclinical molecular imaging facilities is challenging due to disparate imaging systems and multiple off-site investigators. A Molecular Imaging Data Grid design, implementation, and initial evaluation is presented to demonstrate the secure and novel data grid solution for sharing preclinical molecular imaging data across the wide-area-network (WAN).

Evolving minimally invasive spine surgery: a surgeon's perspective on technological convergence and digital or control system.

Degenerated spinal disc and spinal stenosis are common problems requiring decompressive spinal surgery. Traditional open spinal discectomy is associated with significant tissue trauma, greater morbidity/complications, scarring, often longer term of convalescence, and even destabilization of the spine. Therefore, the pursuit of less traumatic minimally invasive spine surgery (MISS) began. The trend of spinal surgery is rapidly moving toward MISS. MISS is a technologically dependent surgery, and requires increased utilization of advanced endoscopic surgical instruments, imaging-video technology, and tissue modulation technology for performing spinal surgery in a digital operating room (DOR). It requires seamless connectivity and control to perform the surgical procedures in a precise and orchestrated manner. A new integrated DOR, the technological convergence and control system SurgMatix(R), was created in response to the need and to facilitate MISS with "organized control instead of organized chaos" in the endoscopic OR suite. It facilitates the performance, training, and further development of MISS.

A multimedia electronic patient record (ePR) system for image-assisted minimally invasive spinal surgery.

PURPOSE: This paper presents the concept of bridging the gap between diagnostic images and image-assisted surgical treatment through the development of a one-stop multimedia electronic patient record (ePR) system that manages and distributes the real-time multimodality imaging and informatics data that assists the surgeon during all clinical phases of the operation from planning Intra-Op to post-care follow-up. We present the concept of this multimedia ePR for surgery by first focusing on image-assisted minimally invasive spinal surgery as a clinical application. METHODS: Three clinical phases of minimally invasive spinal surgery workflow in Pre-Op, Intra-Op, and Post-Op are discussed. The ePR architecture was developed based on the three-phased workflow, which includes the Pre-Op, Intra-Op, and Post-Op modules and four components comprising of the input integration unit, fault-tolerant gateway server, fault-tolerant ePR server, and the visualization and display. A prototype was built and deployed to a minimally invasive spinal surgery clinical site with user training and support for daily use. SUMMARY: A step-by-step approach was introduced to develop a multimedia ePR system for imaging-assisted minimally invasive spinal surgery that includes images, clinical forms, waveforms, and textual data for planning the surgery, two real-time imaging techniques (digital fluoroscopic, DF) and endoscope video images (Endo), and more than half a dozen live vital signs of the patient during surgery. Clinical implementation experiences and challenges were also discussed.

Integration of computer-aided diagnosis/detection (CAD) results in a PACS environment using CAD-PACS toolkit and DICOM SR.

PURPOSE: Picture Archiving and Communication System (PACS) is a mature technology in health care delivery for daily clinical imaging service and data management. Computer-aided detection and diagnosis (CAD) utilizes computer methods to obtain quantitative measurements from medical images and clinical information to assist clinicians to assess a patient's clinical state more objectively. CAD needs image input and related information from PACS to improve its accuracy; and PACS benefits from CAD results online and available at the PACS workstation as a second reader to assist physicians in the decision making process. Currently, these two technologies remain as two separate independent systems with only minimal system integration. This paper describes a universal method to integrate CAD results with PACS in its daily clinical environment. METHODS: The method is based on Health Level 7 (HL7) and Digital imaging and communications in medicine (DICOM) standards, and Integrating the Healthcare Enterprise (IHE) workflow profiles. In addition, the integration method is Health Insurance Portability and Accountability Act (HIPAA) compliant. SUMMARY: The paper presents (1) the clinical value and advantages of integrating CAD results in a PACS environment, (2) DICOM Structured Reporting formats and some important IHE workflow profiles utilized in the system integration, (3) the methodology using the CAD-PACS integration toolkit, and (4) clinical examples with step-by-step workflows of this integration.

Computer-assisted detection (CAD) methodology for early detection of response to pharmaceutical therapy in tuberculosis patients.

The chest x-ray radiological features of tuberculosis patients are well documented, and the radiological features that change in response to successful pharmaceutical therapy can be followed with longitudinal studies over time. The patients can also be classified as either responsive or resistant to pharmaceutical therapy based on clinical improvement. We have retrospectively collected time series chest x-ray images of 200 patients diagnosed with tuberculosis receiving the standard pharmaceutical treatment. Computer algorithms can be created to utilize image texture features to assess the temporal changes in the chest x-rays of the tuberculosis patients. This methodology provides a framework for a computer-assisted detection (CAD) system that may provide physicians with the ability to detect poor treatment response earlier in pharmaceutical therapy. Early detection allows physicians to respond with more timely treatment alternatives and improved outcomes. Such a system has the potential to increase treatment efficacy for millions of patients each year.

Informatics in radiology: DICOM-RT-based electronic patient record information system for radiation therapy.

Comprehensive clinical imaging data and additional relevant information are crucial for the planning and delivery of radiation therapy in patients with cancer. Multiple stand-alone systems that make use of technologic advances in imaging, treatment planning, and treatment delivery acquire or generate key data during the course of radiation therapy. However, the data are scattered in various systems throughout the radiation therapy department, thereby compromising efficient clinical work flow. In 1997 and 1999, the Digital Imaging and Communications in Medicine (DICOM) standard was extended from radiology to radiation therapy with the ratification of seven DICOM-RT objects. These objects helped set the standard for (a) data integration and interoperability between radiation therapy equipment and information systems from different manufacturers, and (b) the use of DICOM diagnostic images in radiation therapy. More recently, key radiation therapy imaging and informatics data have been integrated to form an open-architecture comprehensive radiation therapy electronic patient record (ePR) system. The benefits of such a DICOM-RT-based ePR system are threefold: it can be used as a foundation for performing effective and efficient clinical services, as a common platform for radiation therapy data exchange and expert consultation, and for medical imaging informatics research in developing innovative decision support tools and a knowledge base for improved treatment with radiation therapy.