Research And Practice Of Medical Imaging Cloud Services Platform

Thanks to the development of medical imaging, recent decade witnessed the rapid progress of medical imageology. Advanced techniques and equipments such as 320-slice spiral CT, ultra-high field MRI, molecular imaging, functional imaging, multi-modality image fusion brought convenience to doctors meanwhile led to some significant problems:1) Expensive medical devices in many hospitals were regarded as the reflection of hospitals' scale and medical technology; however, they contributed to high medical expenditure as well.2) Given patients' film which carried limited information could not serve as the basis of diagnosis when they transferred to another hospital, repeated checking resulted in patients'extra medical expense.3) Though there were advanced image devices such as 64-slice spiral CT in some rural hospitals, lack of specialized talents and low utilization rate of the device made the advantages of these advanced devices not in full display.4) Deficiency of funds, equipments, techniques and talents in basic medical and health institutions generated the overcrowding of large-scaled hospitals. The imbalance of medical recourses was partly responsible for the difficulty of medical treatment and expensive medical expense.5) The difficulty existing in image diagnosis required profound knowledge and rich experience of doctors. Increasingly newly-developed devices presented even greater challenges to the teaching image diagnosis. Traditional teaching methods and devices could not satisfy the swelling need of student's recruitment.6) Large image data need to be preserved in long-term, while domestic hospitals were in general short of remote disaster tolerant system and backup measures, potential natural disasters such as fire, earthquake or tsunami would cause the total loss of these images, which would in turn lead to irreparable consequences.The sharing of regional medical resources and coordination of medical process through network functioned as a remarkable means of overcoming the difficulty of medical treatment and expensive medical expense. Regional medical imaging coordination was an significant part of regional healthcare and the establishment of regional imaging services platform could benefit tele-radiology, virtual radiology department, remote teaching, remote redundancy, personal medical record hosting, typical case query, content-based image retrieval, etc., which was of great importance for the balance of medical recourses, improvement of diagnosis accuracy in basic medical institutes and lower medical expense.The application of traditional PACS in the construction of large-scaled regional medical images center was confronted with great economic and technical challenges.1) High expenditure. There were much more data in PACS than those in HIS or LIS. The volume of image data in PACS in a large-scaled first-class third-level hospital would be on the order of terabytes or dozens of terabytes each year and medical image data in a certain region would be on the order of hundreds of terabytes to even several petabytes. It is essential to cover all the images in certain region since regional medical imaging services platform provides remote redundancy, image hosting services, etc. However, the application of traditional FC SAN in the construction of large-scaled storage system would lead to high expenditure.2) Limited performance and scalability. The throughput and processing capacity of FC SAN could not satisfy the need of large data processing and transfer. Even the increase of bandwidth would be achieved through more channels available, the architecture of which was complicated and expensive. Meanwhile, it was difficult to maintain the consistency of directory structure of the overall system with extended storage devices. Though virtual storage products existed in the market, which virtualized many storage devices into a storage pool, most of them were not compatible with products from other companies due to its technology limitation.3) Limited availability. The commonly-employed storage model of "online, near-line, offline" in PACS could save the expense and guarantee diagnosis performance but it was at the expense of limited availability since it was difficult to obtain offline images.4) Absence of systematic software. The present full-PACS software is suitable for high-speed, stable and safe campus network. Provided in pure internet access with limited bandwidth, poor stability and firewall blocking system, such kind of software is difficult to meet application need.The rapidly developing cloud computing technologies turned out to be an effective approach to the construction of cost-efficient, high-performance, flexible and resilient regional medical imaging services platform. The presented project describes the construction of regional medical imaging services platform with cloud computing through various transfer media to provide services for SaaS-based tele-applications of medical imaging. The key point of the research goes to high-performance, reliable, scalable distributed storage architecture and parallel processing which were the basis of and key to regional medical imaging services platform.As a world-widely largest search engine and cloud computing provider, Google was the first confronted with how to process large volumes of data (petabytes). It created GFS and MapReduce instead of applying traditional data store architecture and high performance computing. Through the accumulation of storing and computing capacity of thousands of common servers, it realized the efficient processing of large volumes of data, which proved to be a big success. Apache Hadoop is an open source implementation of Google GFS and MapReduce, which turned to be the most influential open source cloud computing platform and in wide application. Taken the characteristics of Hadoop and the requirement of medical imaging cloud platform into consideration, the basic structure of medical imaging cloud computing platform was designed with Hadoop HDFS and MapReduceThe benefits of Hadoop HDFS were presented as follows:1) It was particularly designed for rapid large volumes of data (petabytes) storing and processing, which was testified by Yahoo, FaceBook, Amzon, Baidu, Taobao, etc.2) High scalability. The increase of servers could realize the linear increase of storage capacity, disc I/O throughput rate and calculating capacity.3) High redundancy. Each data block will be stored in three different nodes.4) It is designed for streaming access, which is suitable for the long-term storage of medical images.5) Besides information storage capacity, MapReduce, coexisting with HDFS, could aggregate CPU power across the nodes for data-intensive applications such as image preprocessing, format converting, image fusion, content-based image retrieval (CBIR), three dimensional reconstruction.Some problems existed in the construction of medical image storage system with Hadoop. Hadoop was designed for large files with default block size of 64MB, by contrast, frequently-seen medial images such as CT, MRI were about 500 KB each and there were about 100 to 200 images each scan. Suppose a sea of small files were stored in HDFS, excessive meta data would contribute to too much RAM consumption of NameNode and eventually decreased the performance of whole cluster. In addition, HDFS was not appropriate for the real-time application requiring low latency since its writing performed more weakly than its reading did, which was not suitable for the real-time application of PACS.To solve this problem, with Sequence File in Hadoop, a S-DICOM was specifically designed. Since with Key/Value pair, all images from one patient within a study can be combined into one serialized file, thus, the process performance was improved significantly and RAM of NameNode had no risk of large consumption. Data with Key/Value pair served as the best input data format which was beneficial to medical-image-based data intensive applications. Though HDFS itself was not appropriate for real-time application, it was economical, highly scalable, reliable and with high performance. Since traditional FC SAN was suitable for quick access to small files, a kind of "online, archived" two-level model medical image storage architecture combined with the benefits of both FC SAN and HDFS, which was called HMISA(Hybrid Medical Image Storage Architecture) was specifically designed. Medical images produced within one year stored in FC SAN with original format, which could fulfill the requirement of low latency of reading film and writing diagnosis reports. Those produced over one year would be converted into S-DICOM format hosting in HDFS. With components of S-DICOM File Operator(SDFO), reading and writing of images in substructure were shielded, which provided interface to query, reading and writing for upper layer applications and components.Distributed computing framework of MapReduce built in Hadoop had shielded some complicated details such as job scheduling, fault tolerance, load balance, which reduced the difficulty existing in the development of distributed computing system. Meanwhile, with the strategy of moving computation to the data, MapReduce achieved high data locality which in turn resulted in high performance and it was the best choice for distributed processing of data-intensive application. We designed some MapReduce-based distributed computing program for medical image processing such as converting file format from DICOM to JPEG, batch de-identification, batch thumbnail generation, distributed importing and query of access logs, and its influence on performance was tested in testing cluster, the result of which indicated that with MapReduce cluster the calculation capacity of each storage node was in full display and the performance of large data processing was improved greatly with scale-out.To sum up, with traditional technology, the construction of regional medial imaging services platform was confronted with great challenges such as funds, technology. Newly-developed cloud computing and service model was a practicable approach to construct an economical, reliable and scalable regional medical imaging services platform.