New technological advancements including multislice CT scanners and functional MRI, have dramatically increased the size and number of digital images generated by medical imaging departments. Despite the fact that the cost of storage is dropping, the savings are largely surpassed by the increasing volume of data being generated. While local area network bandwidth within a hospital is adequate for timely access to imaging data, efficiently moving the data between institutions requires wide area network bandwidth, which has a limited availability at a national level. A solution to address those issues is the use of lossy compression as long as there is no loss of relevant information. The goal of this study was to determine levels at which lossy compression can be confidently used in diagnostic imaging applications. In order to provide a fair assessment of existing compression tools, we tested and compared the two most commonly adopted DISCOM compression algorithms: JPEG and JPEG-2000. We conducted an extensive pan-Canadian evaluation of lossy compression applied to seven anatomical areas and five modalities using two recognized techniques: objective methods or diagnostic accuracy and subjective assessment based on Just Noticeable Difference. By incorporating both diagnostic accuracy and subjective evaluation techniques, enabled us to define a range of compression for each modality and body part tested. The results of our study suggest that at low levels of compression, there was no significant difference between the performance of lossy JPEG and lossy JPEG 2000, and that they are both appropriate to use for reporting on medical images. At higher levels, lossy JPEG proved to be more effective than JPEG 2000 in some cases, mainly neuro CT. More evaluation is required to assess the effect of compression on thin slice CT. We provide a table of recommended compression ratios for each modality and anatomical area investigated, to be integrated in the Canadian Association of Radiologists standard for the use of lossy compression in medical imaging.

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Abstract. 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.

Abstract. The evolution of cloud computing is driving the next generation of medical imaging systems. However, privacy and security concerns have been consistently regarded as the major obstacles for adoption of cloud computing by healthcare domains. OpenID Connect, combining OpenID and OAuth together, is an emerging representational state transfer-based federated identity solution. It is one of the most adopted open standards to potentially become the de facto standard for securing cloud computing and mobile applications, which is also regarded as "Kerberos of cloud." We introduce OpenID Connect as an authentication and authorization service in cloud-based diagnostic imaging (DI) systems, and propose enhancements that allow for incorporating this technology within distributed enterprise environments. The objective of this study is to offer solutions for secure sharing of medical images among diagnostic imaging repository (DI-r) and heterogeneous picture archiving and communication systems (PACS) as well as Web-based and mobile clients in the cloud ecosystem. The main objective is to use OpenID Connect open-source single sign-on and authorization service and in a user-centric manner, while deploying DI-r and PACS to private or community clouds should provide equivalent security levels to traditional computing model.

In June 2008, the Canadian Association of Radiologists published its Standards for Irreversible Compression in Digital Diagnostic Imaging within Radiology (Canadian Association of Radiologists 2012). The study suggested that at low levels of compression there was no difference in diagnostic accuracy between uncompressed JPEG and JPEG 2000. There were two exceptions; CT neurological and CT body images resulted in lower rating of image quality (Koff et al., J Digit Imaging 22(6):569-78, 2009). The slice thicknesses used in the previous study were greater than 5 mm. However, other studies (Ringl et al., Radiology 240:869-87, 2006) suggest that thin CT slices might modify image tolerance to irreversible compression. Therefore, a new clinical evaluation using CT slices less than 3 mm was initiated. We examined CT images in four body regions (chest, body, musculoskeletal, and neurological). Twenty-five radiologists from across Canada participated. Each read a total of 70 CTs in his specialty; 10 at each of seven levels of compression (uncompressed, JPEG and JPEG 2000 at low, medium, and high compression (varying by region)). Each reader diagnosed the case, rated his confidence, and compared the compressed to the uncompressed image and rated the degree of degradation. Data were analyzed for sensitivity, specificity, accuracy, confidence, and degradation at three levels and two types of compression as well as the original image. There were no overall differences in sensitivity, specificity, accuracy, or confidence. JPEG images, at all levels of compression, were rated lower in terms of perceived difference (4.16/5 vs. 4.53/5 for JPEG 2000 and 4.68/5 for uncompressed). However, the rating of perceived difference was not significantly correlated with accuracy. Analysis of individual body regions did not reveal any systematic effects of compression in any region.