Integrating the Healthcare Enterprise (IHE) has recently published a new integration profile for sharing documents
between multiple enterprises. The Cross-Enterprise Document Sharing Integration Profile (XDS) lays the basic
framework for deploying regional and national Electronic Health Record (EHR). This profile proposes an architecture
based on a central Registry that holds metadata information describing published Documents residing in one or multiple
Documents Repositories. As medical images constitute important information of the patient health record, it is logical to
extend the XDS Integration Profile to include images. However, including images in the EHR presents many
challenges. The complete image set is very large; it is useful for radiologists and other specialists such as surgeons and
orthopedists. The imaging report, on the other hand, is widely needed and its broad accessibility is vital for achieving
optimal patient care. Moreover, a subset of relevant images may also be of wide interest along with the report.
Therefore, IHE recently published a new integration profile for sharing images and imaging reports between multiple
enterprises. This new profile, the Cross-Enterprise Document Sharing for Imaging (XDS-I), is based on the XDS
architecture. The XDS-I integration solution that is published as part of the IHE Technical Framework is the result of an
extensive investigation effort of several design solutions. This paper presents and discusses the design challenges and
the rationales behind the design decisions of the IHE XDS-I Integration Profile, for a better understanding and
appreciation of the final published solution.
Despite the increasing interest in three-dimensional (3D) visualization, rendering algorithms still suffer from high numerical complexity and large memory requirements. With the continuously increasing volume of medial imaging data, fast visualization algorithms become crucial. Powerful mathematical techniques based on the wavelet transform promise to provide efficient multi-resolution visualization algorithms, optimizing hence 3D rendering. Maximum Intensity Projection (MIP) is a 3D rendering algorithm that is used to visualize high-intensity structures within volumetric data. At each pixel the highest data value, which is encountered along a corresponding viewing ray, is depicted. In this paper, we propose a fast MIP 3D rendering that is based on a new hierarchical data representation. The proposed approach uses on a new morphological wavelet decomposition that allows for fast initial rendering and progressive subsequent refinements. Our method includes a pre-processing step that is based on a non-linear wavelet representation in order to achieve efficient data compression and storage. It results in a very fast visualization algorithm. The rendering speed-up results from removing cells that do not contribute to any MIP projection and from an innovative storage scheme of the volume cells. The proposed algorithm gives very promising results. Very good MIP projections can be obtained with less than 20% of the volumetric data. This makes our algorithm very competitive with the best MIP methods proposed so far in the literature.
A medical record contains a large amount of data about the patient such as height, weight and blood pressure. It also contains sensitive information such as fertility, abortion, psychiatric data, sexually transmitted diseases and diagnostic results. Access to this information must be carefully controlled.
Information technology has greatly improved patient care. The recent extensive deployment of digital medical images made diagnostic images promptly available to healthcare decision makers, regardless of their geographic location. Medical images are digitally archived, transferred on telecommunication networks, and visualized on computer screens. However, with the widespread use of computing and communication technologies in healthcare, the issue of data security has become increasingly important.
Most of the work until now has focused on the security of data communication to ensure its integrity, authentication, confidentiality and user accountability. The mechanisms that have been proposed to achieve the security of data communication are not specific to healthcare. Data integrity can be achieved with data signature. Data authentication can be achieved with certificate exchange. Data confidentiality can be achieved with encryption. User accountability can be achieved with audits. Although these mechanisms are essential to ensure data security during its transfer on the network, access control is needed in order to ensure data confidentiality and privacy within the information system application.
In this paper, we present and discuss an access control mechanism that takes into account the notion of a care process. Radiology information is categorized and a model to enforce data privacy is proposed.
Medical image processing methods and algorithms, developed by researchers, need to be validated and tested. Test data should ideally be real clinical data especially when that clinical data is varied and exists in large volume. In nowadays, clinical data is accessible electronically and has important value for researchers. However, the usage of clinical data for research purposes should respect data confidentiality, patient right to privacy and the patient consent. In fact, clinical data is nominative given that it contains information about the patient such as name, age and identification number. Evidently, clinical data should be de-identified to be exported to research databases.
However, the same patient is usually followed during a long period of time. The disease progression and the diagnostic evolution represent extremely valuable information for researchers, as well. Our objective is to build a research database from de-identified clinical data while enabling the database to be easily incremented by exporting new pseudonymous data, acquired over a long period of time. Pseudonymisation is data de-identification such that data belonging to the same individual in the clinical environment bear the same relation to each other in the de-identified research version.
In this paper, we propose a software architecture that enables the implementation of a research database that can be incremented in time. We also evaluate its security and discuss its security pitfalls.
The growing volume of medical images acquired with new imaging
modalities poses big challenges to the radiologist's interpretation
process. Innovative image visualization techniques can play a major
role in enabling efficient and accurate information presentation and
navigation, by combining computational efficiency with diagnostic
resolution. Efficiency and resolution, two opposing requirements, can
be accomplished by focusing on full resolution regions of interest
while maintaining sufficient contextual information. In fact,
structures of interest typically occupy a small percentage of the
data, but their analysis requires context information like locations
within a specific organ or adjacency to sensitive structures. We propose a 3D visualization technique that is based on the
multi-resolution property of the wavelet transform in order to display
a full resolution region of interest while displaying a coarser
context to achieve efficiency in rendering during the exploratory
navigation phase. A full resolution context can also be rendered when
needed for a specific view. In a preprocessing stage the data is
decomposed with a three-dimensional wavelet transform. The interactive
visualization process then uses the wavelet representation and a
user-specified region to render a full resolution region of interest
and a coarser context directly from the wavelet space through wavelet
splatting, thus avoiding volume reconstruction. This efficient
rendering approach is combined with lighting calculations, in the
preprocessing stage. While greatly enhancing depth perception and
objects shape, lighting does not add additional cost to the
interactive visualization process, resulting in a good compromise
between computational efficiency and image quality.
The radiology diagnostic reporting is a process that results in generating a diagnostic report to be made available outside the radiology department. The report captures the radiologist’s interpretations and impressions. It is an element of the patient healthcare record and represents important clinical information to assist in healthcare decisions. The reporting process is initiated by the existence of images or other radiology evidences to be interpreted. The work of individuals is controlled by systems that manage workflow. These systems may introduce delays or constraints on how and when tasks are performed. In order to design and implement efficient information systems that manage the reporting workflow, an accurate workflow modeling is needed. Workflow modeling consists in describing what is done by whom and in what sequence, that is the roles, tasks and sequences of tasks. The workflow model is very important and has major consequences. An inaccurate model introduces inefficiencies, frustrations and may result in a useless information system. In this paper, we will model several common reporting workflows by describing the roles, tasks and information flows involved.
The expectation maximization method for maximum likelihood image reconstruction (ML- EM) is one of the most popular algorithms used in SPECT and PET, because it is based on the realistic assumption that photon emission and counts follow a Poisson process. Moreover, this method retains two important theoretical and practical properties namely nonnegativity and self-normalization of the reconstructed image. This latter property means that the number of emitted photons is equal to the number of counts. However, the major disadvantage of this method is the large amount of computation that is required, due to its slow rate of convergence. In this paper, we demonstrate that the ML-EM algorithm is a special case of the modified Newton method and can thus be accelerated by multiplying at each iteration the changes to the image, as calculated by the standard algorithm, by an overrelaxation parameter. This accelerated ML-EM algorithm can further be optimally accelerated, and converges to a good maximum likelihood estimator.
Iterative reconstruction algorithms for single photon emission tomography (SPECT) have generally outperformed conventional Fourier back-projections algorithms. Since faster computers have allowed their use in clinical investigations it is important to identify the best iterative approach. In this paper, the MENT and MART (Multiplicative Algebraic Reconstruction Technique) algorithms have been compared. They are considered to be quite different and their performance are frequently compared in the literature. MENT is generally preferred because it produces a maximum entropy solution to the problem of reconstruction but it is much slower than MART. We have demonstrated that MART can be mathematically derived from MENT. MART should then be the algorithm of choice for entropy maximization.