KEYWORDS: Picture Archiving and Communication System, 3D modeling, Visualization, Visual process modeling, Artificial intelligence, Telecommunications, 3D visualizations, Data modeling, Image processing, 3D image processing
This paper proposed a new generation PACS (Picture Archiving and Communication System) based on artificial intelligent visualization. It is developed from our GRIDPACS (patent number: US8805890), which combined with IHE XDS-I profile, to implement images communication, storage and display. It also uses 3D anatomical visualization model to extract multi-source data from PACS/RIS/HIS/EMR, to express patient disease location, size and severity, which was introduced as Visual Patent (VP) at previous SPIE Medical Imaging (SPIE MI 2018). It can integrate the training model of AI Imaging Diagnosis, to mark the focus and display the disease trends. The system not only has the original PACS functions, but also realizes the man-machine interaction (images and electronic medical record information between radiologist and patient) in a personalized, fast, comprehensive, quantitative and easy-to-understand way. It can be used in various medical institutions, image diagnostic centers, and imaging cloud, to support the healthy development of imaging technology in China.
In the AI training, the data set is always divided into training set and test set at random, but the clinical image data from hospitals is different from the public data set. The division of public data set is reasonably divided and evenly distributed after many experiments. Accurate understanding of the data distribution directly affects the training model quality. So we proposed a new method of dividing clinical data set based on distance metric learning of the Gaussian mixture model to obtain more reasonable data set divisions. The distance metric learning based on deep neural network, first embeds data into a new metric space, then in the metric space uses in-depth mining based on data characteristics, calculates the distance between samples, finally compares the differences. The method can accurately know the data distribution characteristics to a certain extent. Under the condition of understanding the data distribution characteristics, more reasonable divisions can be obtained. That can greatly affect the accuracy and generalization performance of the models we trained.
KEYWORDS: Medical imaging, Artificial intelligence, Clouds, Data communications, Internet, Picture Archiving and Communication System, Image processing, Mobile devices, Cancer, Document imaging
This paper proposed a new approach to design medical imaging-sharing service network based on professional medical imaging center (PMIC). PMIC is famous for advanced imaging modalities and expert resources. The network connects clinics, hospitals and PMICs to provide collaborative diagnosis, consultation, mobile expert consulting and medical imaging artificial intelligence (AI) analysis services through Internet. It allows patients to be registered in hospital and examined in PMIC. It provides to schedule and view patients exam from mobile devices. It also provides AI analysis for some specific kinds of medical images such as carotid plaque and mammary cancer, to help doctors get accurate conclusions. The network is flexible to use three layers architecture with secure messaging and data communication: data source, service cloud and service provider. It has been deployed in Guangzhou Huyun Medical Imaging Diagnosis Center since July 2018 to provide services for the First People’s Hospital of Guangzhou.
In this presentation, we presented a new approach to design cloud-based image sharing network for collaborative imaging diagnosis and consultation through Internet, which can enable radiologists, specialists and physicians locating in different sites collaboratively and interactively to do imaging diagnosis or consultation for difficult or emergency cases. The designed network combined a regional RIS, grid-based image distribution management, an integrated video conferencing system and multi-platform interactive image display devices together with secured messaging and data communication. There are three kinds of components in the network: edge server, grid-based imaging documents registry and repository, and multi-platform display devices. This network has been deployed in a public cloud platform of Alibaba through Internet since March 2017 and used for small lung nodule or early staging lung cancer diagnosis services between Radiology departments of Huadong hospital in Shanghai and the First Hospital of Jiaxing in Zhejiang Province.
KEYWORDS: Data processing, Visualization, Lithium, Imaging systems, Medical imaging, Picture Archiving and Communication System, Image processing, Medical diagnostics, Diagnostics, Virtual point source
We have innovatively introduced Visual Patient (VP) concept and method visually to represent and index patient imaging diagnostic records (IDR) in last year SPIE Medical Imaging (SPIE MI 2017), which can enable a doctor to review a large amount of IDR of a patient in a limited appointed time slot. In this presentation, we presented a new approach to design data processing architecture of VP system (VPS) to acquire, process and store various kinds of IDR to build VP instance for each patient in hospital environment based on Hadoop distributed processing structure. We designed this system architecture called Medical Information Processing System (MIPS) with a combination of Hadoop batch processing architecture and Storm stream processing architecture. The MIPS implemented parallel processing of various kinds of clinical data with high efficiency, which come from disparate hospital information system such as PACS, RIS LIS and HIS.
Online peer to peer medical consultation between doctors such as physicians and specialists in China has a broad market demand and has been continuously accepted. For some difficult diseases, electronic medical records with medical images are required to present to both sides at same time during the consultation so that both sides can manipulate the records interactively to understand the medical meanings of the records, especially images. Here, we presented design of a teleconsultation system integrated with a cloud-based collaborative image sharing network to provide online peer-to-peer medical consultation for difficult cases with multi-media medical records including DICOM images. The presented teleconsultation system provides bidirectional interactive manipulations on images presented to peer-to-peer sides and has been used for small lung nodule diagnosis services between Huadong hospital in Shanghai and Jiaxing First Hospital in Zhejiang Province through Internet.
The benign and malignant differential diagnosis of small pulmonary nodules (diameter < 20 mm) found in lung CT images is big challenges for most of radiologists. Here, we presented our preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning Convolutional Neural Network (CNN). The 921 cases with small benign and malignant pulmonary nodules confirmed by pathology were collected from three data sources and were used to train and validate the CNN. The preliminary results of AUCs of ROC curves for differentiating benign and malignant pulmonary small nodules with various types and sizes of solid, semi-solid and ground glass nodules were presented and discussed.
KEYWORDS: Electronic imaging, Diagnostics, Visualization, Virtual point source, 3D visualizations, Picture Archiving and Communication System, 3D displays, Medical diagnostics, Medicine, Radiology
Purpose:
Due to the generation of a large number of electronic imaging diagnostic records (IDR) year after year in a digital hospital, The IDR has become the main component of medical big data which brings huge values to healthcare services, professionals and administration. But a large volume of IDR presented in a hospital also brings new challenges to healthcare professionals and services as there may be too many IDRs for each patient so that it is difficult for a doctor to review all IDR of each patient in a limited appointed time slot. In this presentation, we presented an innovation method which uses an anatomical 3D structure object visually to represent and index historical medical status of each patient, which is called Visual Patient (VP) in this presentation, based on long term archived electronic IDR in a hospital, so that a doctor can quickly learn the historical medical status of the patient, quickly point and retrieve the IDR he or she interested in a limited appointed time slot.
Method:
The engineering implementation of VP was to build 3D Visual Representation and Index system called VP system (VPS) including components of natural language processing (NLP) for Chinese, Visual Index Creator (VIC), and 3D Visual Rendering Engine.There were three steps in this implementation: (1) an XML-based electronic anatomic structure of human body for each patient was created and used visually to index the all of abstract information of each IDR for each patient; (2)a number of specific designed IDR parsing processors were developed and used to extract various kinds of abstract information of IDRs retrieved from hospital information systems; (3) a 3D anatomic rendering object was introduced visually to represent and display the content of VIO for each patient.
Results:
The VPS was implemented in a simulated clinical environment including PACS/RIS to show VP instance to doctors. We setup two evaluation scenario in a hospital radiology department to evaluate whether radiologists accept the VPS and how the VP impact the radiologists’ efficiency and accuracy in reviewing historic medical records of the patients. We got a statistical results showing that more than 70% participated radiologist would like to use the VPS in their radiological imaging services. In comparison testing of using VPS and RIS/PACS in reviewing historic medical records of the patients, we got a statistical result showing that the efficiency of using VPS was higher than that of using PACS/RIS.
New Technologies and Results to be presented:
This presentation presented an innovation method to use an anatomical 3D structure object, called VP, visually to represent and index historical medical records such as IDR of each patient and a doctor can quickly learn the historical medical status of the patient through VPS. The evaluation results showed that VPS has better performance than RIS-integrated PACS in efficiency of reviewing historic medical records of the patients.
Conclusions:
In this presentation, we presented an innovation method called VP to use an anatomical 3D structure object visually to represent and index historical IDR of each patient and briefed an engineering implementation to build a VPS to implement the major features and functions of VP. We setup two evaluation scenarios in a hospital radiology department to evaluate VPS and achieved evaluation results showed that VPS has better performance than RIS-integrated PACS in efficiency of reviewing historic medical records of the patients.
Content-Based medical image retrieval (CBMIR) is been highly active research area from past few years. The retrieval
performance of a CBMIR system crucially depends on the feature representation, which have been extensively studied by
researchers for decades. Although a variety of techniques have been proposed, it remains one of the most challenging
problems in current CBMIR research, which is mainly due to the well-known “semantic gap” issue that exists between
low-level image pixels captured by machines and high-level semantic concepts perceived by human[1]. Recent years have
witnessed some important advances of new techniques in machine learning. One important breakthrough technique is
known as “deep learning”. Unlike conventional machine learning methods that are often using “shallow” architectures,
deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple
stages of transformation and representation. This means that we do not need to spend enormous energy to extract features
manually. In this presentation, we propose a novel framework which uses deep learning to retrieval the medical image to
improve the accuracy and speed of a CBIR in integrated RIS/PACS.
In medical imaging informatics, content-based image retrieval (CBIR) techniques are employed to aid radiologists in the retrieval of images with similar image contents. CBIR uses visual contents, normally called as image features, to search images from large scale image databases according to users’ requests in the form of a query image. However, most of current CBIR systems require a distance computation of image character feature vectors to perform query, and the distance
computations can be time consuming when the number of image character features grows large, and thus this limits the
usability of the systems. In this presentation, we propose a novel framework which uses a high dimensional database to index the image character features to improve the accuracy and retrieval speed of a CBIR in integrated RIS/PACS.
KEYWORDS: Imaging systems, Medicine, Diagnostics, Data modeling, Medical imaging, Picture Archiving and Communication System, Surgery, Systems modeling, Data backup, Image processing
To improve healthcare service quality with balancing healthcare resources between large and
small hospitals, as well as reducing costs, each district health administration in Shanghai with more than 24 million citizens has built image-enabled electronic healthcare records (iEHR) system to share patient
medical records and encourage patients to visit small hospitals for initial evaluations and preliminary
diagnoses first, then go to large hospitals to have better specialists’ services. We implemented solution for
iEHR systems, based on the IHE XDS-I integration profile and installed the systems in more than 100
hospitals cross three districts in Shanghai and one city in Jiangsu Province in last few years. Here, we give operational results of these systems in these four districts and evaluated the performance of the
systems in servicing the regional collaborative imaging diagnosis.
IHE XDS-I profile proposes an architecture model for cross-enterprise medical image sharing, but there are only a few clinical implementations reported. Here, we investigate three pilot studies based on the IHE XDS-I profile to see whether we can use this architecture as a foundation for image sharing solutions in a variety of health-care settings. The first pilot study was image sharing for cross-enterprise health care with federated integration, which was implemented in Huadong Hospital and Shanghai Sixth People’s Hospital within the Shanghai Shen-Kang Hospital Management Center; the second pilot study was XDS-I–based patient-controlled image sharing solution, which was implemented by the Radiological Society of North America (RSNA) team in the USA; and the third pilot study was collaborative imaging diagnosis with electronic health-care record integration in regional health care, which was implemented in two districts in Shanghai. In order to support these pilot studies, we designed and developed new image access methods, components, and data models such as RAD-69/WADO hybrid image retrieval, RSNA clearinghouse, and extension of metadata definitions in both the submission set and the cross-enterprise document sharing (XDS) registry. We identified several key issues that impact the implementation of XDS-I in practical applications, and conclude that the IHE XDS-I profile is a theoretically good architecture and a useful foundation for medical image sharing solutions across multiple regional health-care providers.
We had designed a semantic searching engine (SSE) for radiological imaging to search both reports and images in RIS-integrated PACS environment. In this presentation, we present evaluation results of this SSE about how it impacting the radiologists’ behaviors in reporting for different kinds of examinations, and how it improving the performance of retrieval and usage of historical images in RIS-integrated PACS.
In order to enable multiple disciplines of medical researchers, clinical physicians and biomedical engineers working together in a secured, efficient, and transparent cooperative environment, we had designed an e-Science platform for biomedical imaging research and application cross multiple academic institutions and hospitals in Shanghai and presented this work in SPIE Medical Imaging conference held in San Diego in 2012. In past the two-years, we implemented a biomedical image chain including communication, storage, cooperation and computing based on this e-Science platform. In this presentation, we presented the operating status of this system in supporting biomedical imaging research, analyzed and discussed results of this system in supporting multi-disciplines collaboration cross-multiple institutions.
One key problem for continuity of patient care is identification of a proper method to share and exchange patient medical records among multiple hospitals and healthcare providers. This paper focuses in the imaging document component of medical record. The XDS-I (Cross- Enterprise Document Sharing – Image) Profile based on the IHE IT-Infrastructure extends and specializes XDS to support imaging “document” sharing in an affinity domain. We present three studies about image sharing solutions based on IHE XDS-I Profile. The first one is to adopt the IHE XDS-I profile as a technical guide to design image and report sharing mechanisms between hospitals for regional healthcare service in Shanghai. The second study is for collaborating image diagnosis in regional healthcare services. The latter study is to investigate the XDS-I based clearinghouse for patient controlled image sharing in the RSNA Image Sharing Network Project. We conclude that the IHE XDS/XDS-I profiles can be used as the foundation to design medical image document sharing for Various Healthcare Services.
KEYWORDS: Biomedical optics, Medical research, Medical imaging, Data modeling, Data acquisition, Image storage, Data storage, Image processing, Data centers, Internet
As there are urgent demands to bring medical imaging research and clinical service together more closely to solve the problems related to disease discover and medical research, a new imaging informatics infrastructure need to be developed to promote multiple disciplines of medical researchers and clinical physicians working together in a secured and efficient cooperative environment. In this presentation, we outline our work of building Biomedical Imaging Informatics “e-Science” platform integrated with high performance image sharing, collaborating and computing to support multi-disciplines translational biomedical imaging research in multiple affiliated hospitals and academic institutions in Shanghai.
In order to enable multiple disciplines of medical researchers, clinical physicians and biomedical engineers working together in a secured, efficient, and transparent cooperative environment, we had designed an e-Science platform for biomedical imaging research and application cross multiple academic institutions and hospitals in Shanghai by using grid-based or cloud-based distributed architecture and presented this work in SPIE Medical Imaging conference held in San Diego in 2012. However, when the platform integrates more and more nodes over different networks, the first challenge is that how to monitor and maintain all the hosts and services operating cross multiple academic institutions and hospitals in the e-Science platform, such as DICOM and Web based image communication services, messaging services and XDS ITI transaction services. In this presentation, we presented a system design and implementation of intelligent monitoring and management which can collect system resource status of every node in real time, alert when node or service failure occurs, and can finally improve the robustness, reliability and service continuity of this e-Science platform.
KEYWORDS: Biomedical optics, Medical research, Medical imaging, Image transmission, Image storage, Data centers, Information science, Data modeling, Image retrieval, Biomedical engineering
More and more image informatics researchers and engineers are considering to re-construct imaging and informatics
infrastructure or to build new framework to enable multiple disciplines of medical researchers, clinical physicians
and biomedical engineers working together in a secured, efficient, and transparent cooperative environment. In this
presentation, we show an outline and our preliminary design work of building an e-Science platform for biomedical
imaging and informatics research and application in Shanghai. We will present our consideration and strategy on
designing this platform, and preliminary results. We also will discuss some challenges and solutions in building this
platform.
KEYWORDS: Image transmission, Network security, Computer security, Biomedical optics, Local area networks, Medical imaging, Image retrieval, Medical research, Binary data, Data communications
In designing of e-Science platform for biomedical imaging research and application cross multiple academic institutions
and hospitals, it needs to find out the best communication protocol to transmit various kinds of biomedical images
acquired from Shanghai Synchrotron Radiation Source (SSRS), micro-PET, Micro-CT which includes both types of
DICOM and non-DICOM images. In this presentation, we presented several image communication scenarios required in
e-Science platform and several possible image communication protocols, and then tested and evaluated the performance
of these image communication protocols in e-Science data flows to find out which protocol is the best candidate to be
used in e-Science platform for the purpose for security, communication performance, easy implementation and
management.
KEYWORDS: Medicine, Medical imaging, Imaging systems, Picture Archiving and Communication System, Image retrieval, Diagnostics, Document imaging, Data centers, Physics, Data backup
We designed the image-enabled EHR sharing solution (i-EHR) for cross-enterprise and cross-domain with
SOA architecture and combined the grid-based image management and distribution capability, which are
compliant with IHE XDS-I/XCA integration profiles. We selected one districts with four hospitals and two
hospital groups as image sharing pilot testing bed. Our approach presented in this presentation uses
peer-to-peer mode to share and exchange image data cross enterprise PACSs and domains, which provides
single point of services to local systems so it is easy to integrate with different vendor's PACS and easy to
deploy to different hospitals to implement the i-EHR.
KEYWORDS: Medicine, Picture Archiving and Communication System, Image retrieval, Image processing, Electronic design automation, Data modeling, Systems modeling, Data backup, Imaging systems, Physics
Shanghai is piloting to develop an EHR system to solve the problems of medical document sharing for
collaborative healthcare, the solution of which is considering to following IHE XDS (cross-enterprise document
sharing) and XCA (cross-community access) technical profiles as well as combined with grid storage for images.
The first phase of the project targets text and image documents sharing cross four local domains or communities,
each of which consists of multiple hospitals. The prototype system was designed and developed with
service-oriented architecture (SOA) and Event-Driven Architecture (EDA), basing on IHE XDS.b and XCA
profiles, and consists of four level components: one central city registry; the multiple domain registries, each of
which is for one local domain or community; the multiple repositories corresponding to multiple local domain
registries; and multiple document source agents, each of which is located in each hospital to provide the patient
healthcare information. The system was developed and tested for performance evaluation including data
publication, user query and image retrieval. The results are extremely positive and demonstrate that the designed
EHR solution based on SOA with grid concept can scale effectively to serve medical document sharing
cross-domain or community in a large city.
Usually, there were multiple clinical departments providing imaging-enabled healthcare
services in enterprise healthcare environment, such as radiology, oncology, pathology, and
cardiology, the picture archiving and communication system (PACS) is now required to
support not only radiology-based image display, workflow and data flow management, but
also to have more specific expertise imaging processing and management tools for other
departments providing imaging-guided diagnosis and therapy, and there were urgent demand
to integrate the multiple PACSs together to provide patient-oriented imaging services for
enterprise collaborative healthcare. In this paper, we give the design method and
implementation strategy of developing grid-based PACS (Grid-PACS) for a hospital with
multiple imaging departments or centers. The Grid-PACS functions as a middleware between
the traditional PACS archiving servers and workstations or image viewing clients and provide
DICOM image communication and WADO services to the end users. The images can be
stored in distributed multiple archiving servers, but can be managed with central mode. The
grid-based PACS has auto image backup and disaster recovery services and can provide best
image retrieval path to the image requesters based on the optimal algorithms. The designed
grid-based PACS has been implemented in Shanghai Huadong Hospital and been running for
two years smoothly.
A number of hospitals in Shanghai are piloting the development of an EHR solution based on a grid concept with a
service-oriented architecture (SOA). The first phase of the project targets the Diagnostic Imaging domain and allows
seamless sharing of images and reports across the multiple hospitals. The EHR solution is fully aligned with the IHE
XDS-I integration profile and consists of the components of the XDS-I Registry, Repository, Source and Consumer
actors. By using SOA, the solution uses ebXML over secured http for all transactions with in the grid. However,
communication with the PACS and RIS is DICOM and HL7 v3.x. The solution was installed in three hospitals and one
date center in Shanghai and tested for performance of data publication, user query and image retrieval. The results are
extremely positive and demonstrate that the EHR solution based on SOA with grid concept can scale effectively to
server a regional implementation.
KEYWORDS: Picture Archiving and Communication System, Computer security, Information security, Surgery, Computing systems, Medicine, Databases, Network security, Control systems, Magnetic resonance imaging
As a governmental regulation, Health Insurance Portability and Accountability Act (HIPAA) was issued to protect the
privacy of health information that identifies individuals who are living or deceased. HIPAA requires security services
supporting implementation features: Access control; Audit controls; Authorization control; Data authentication; and
Entity authentication. These controls, which proposed in HIPAA Security Standards, are Audit trails here. Audit trails
can be used for surveillance purposes, to detect when interesting events might be happening that warrant further
investigation. Or they can be used forensically, after the detection of a security breach, to determine what went wrong
and who or what was at fault. In order to provide security control services and to achieve the high and continuous
availability, we design the HIPAA-Compliant Automatic Monitoring System for RIS-Integrated PACS operation. The
system consists of two parts: monitoring agents running in each PACS component computer and a Monitor Server
running in a remote computer. Monitoring agents are deployed on all computer nodes in RIS-Integrated PACS system to
collect the Audit trail messages defined by the Supplement 95 of the DICOM standard: Audit Trail Messages. Then the
Monitor Server gathers all audit messages and processes them to provide security information in three levels: system
resources, PACS/RIS applications, and users/patients data accessing. Now the RIS-Integrated PACS managers can
monitor and control the entire RIS-Integrated PACS operation through web service provided by the Monitor Server.
This paper presents the design of a HIPAA-compliant automatic monitoring system for RIS-Integrated PACS Operation,
and gives the preliminary results performed by this monitoring system on a clinical RIS-integrated PACS.
Severe acute respiratory syndrome (SARS) is a respiratory illness that had been reported in Asia, North America, and Europe in last spring. Most of the China cases of SARS have occurred by infection in hospitals or among travelers. To protect the physicians, experts and nurses from the SARS during the diagnosis and treatment procedures, the infection control mechanisms were built in SARS hospitals. We built a Web-based interactive teleradiology system to assist the radiologists and physicians both in side and out side control area to make image diagnosis. The system consists of three major components: DICOM gateway (GW), Web-based image repository server (Server), and Web-based DICOM viewer (Viewer). This system was installed and integrated with CR, CT and the hospital information system (HIS) in Shanghai Xinhua hospital to provide image-based ePR functions for SARS consultation between the radiologists, physicians and experts inside and out side control area. The both users inside and out side the control area can use the system to process and manipulate the DICOM images interactively, and the system provide the remote control mechanism to synchronize their operations on images and display.
KEYWORDS: Synthetic aperture radar, Surgery, Internet, Control systems, Picture Archiving and Communication System, Data modeling, Data archive systems, Data communications, Telecommunications, Image processing
We developed a Web-based system to interactively display image-based electronic patient records (EPR) for intranet and Internet collaborative medical applications. The system consists of four major components: EPR DICOM gateway (EPR-GW), Image-based EPR repository server (EPR-Server), Web Server and EPR DICOM viewer (EPR-Viewer). We have successfully used this system two times for the teleconsultation on Severe acute respiratory syndrome (SARS) in Shanghai Xinhua Hospital and Shanghai Infection Hospital. During the consultation, both the physicians in infection control area and the experts outside the control area could interactively study, manipulate and navigate the EPR of the SARS patients to make more precise diagnosis on images with this system assisting. This presentation gave a new approach to create and manage image-based EPR from actual patient records, and also presented a way to use Web technology and DICOM standard to build an open architecture for collaborative medical applications.
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