Tiago Marques Godinho

Anatomy of an Extensible Open Source PACS.

The conception and deployment of cost effective Picture Archiving and Communication Systems (PACS) is a concern for small to medium medical imaging facilities, research environments, and developing countries’ healthcare institutions. Financial constraints and the specificity of these scenarios contribute to a low adoption rate of PACS in those environments. Furthermore, with the advent of ubiquitous computing and new initiatives to improve healthcare information technologies and data sharing, such as IHE and XDS-i, a PACS must adapt quickly to changes. This paper describes Dicoogle, a software framework that enables developers and researchers to quickly prototype and deploy new functionality taking advantage of the embedded Digital Imaging and Communications in Medicine (DICOM) services. This full-fledged implementation of a PACS archive is very amenable to extension due to its plugin-based architecture and out-of-the-box functionality, which enables the exploration of large DICOM datasets and associated metadata. These characteristics make the proposed solution very interesting for prototyping, experimentation, and bridging functionality with deployed applications. Besides being an advanced mechanism for data discovery and retrieval based on DICOM object indexing, it enables the detection of inconsistencies in an institution’s data and processes. Several use cases have benefited from this approach such as radiation dosage monitoring, Content-Based Image Retrieval (CBIR), and the use of the framework as support for classes targeting software engineering for clinical contexts.

A Routing Mechanism for Cloud Outsourcing of Medical Imaging Repositories

Web-based technologies have been increasingly used in picture archive and communication systems (PACS), in services related to storage, distribution, and visualization of medical images. Nowadays, many healthcare institutions are outsourcing their repositories to the cloud. However, managing communications between multiple geo-distributed locations is still challenging due to the complexity of dealing with huge volumes of data and bandwidth requirements. Moreover, standard methodologies still do not take full advantage of outsourced archives, namely because their integration with other in-house solutions is troublesome. In order to improve the performance of distributed medical imaging networks, a smart routing mechanism was developed. This includes an innovative cache system based on splitting and dynamic management of digital imaging and communications in medicine objects. The proposed solution was successfully deployed in a regional PACS archive. The results obtained proved that it is better than conventional approaches, as it reduces remote access latency and also the required cache storage space.

A Multimodal Search Engine for Medical Imaging Studies

The use of digital medical imaging systems in healthcare institutions has increased significantly, and the large amounts of data in these systems have led to the conception of powerful support tools: recent studies on content-based image retrieval (CBIR) and multimodal information retrieval in the field hold great potential in decision support, as well as for addressing multiple challenges in healthcare systems, such as computer-aided diagnosis (CAD). However, the subject is still under heavy research, and very few solutions have become part of Picture Archiving and Communication Systems (PACS) in hospitals and clinics. This paper proposes an extensible platform for multimodal medical image retrieval, integrated in an open-source PACS software with profile-based CBIR capabilities. In this article, we detail a technical approach to the problem by describing its main architecture and each sub-component, as well as the available web interfaces and the multimodal query techniques applied. Finally, we assess our implementation of the engine with computational performance benchmarks.

An efficient architecture to support digital pathology in standard medical imaging repositories

In the past decade, digital pathology and whole-slide imaging (WSI) have been gaining momentum with the proliferation of digital scanners from different manufacturers. The literature reports significant advantages associated with the adoption of digital images in pathology, namely, improvements in diagnostic accuracy and better support for telepathology. Moreover, it also offers new clinical and research applications. However, numerous barriers have been slowing the adoption of WSI, among which the most important are performance issues associated with storage and distribution of huge volumes of data, and lack of interoperability with other hospital information systems, most notably Picture Archive and Communications Systems (PACS) based on the DICOM standard. This article proposes an architecture of a Web Pathology PACS fully compliant with DICOM standard communications and data formats. The solution includes a PACS Archive responsible for storing whole-slide imaging data in DICOM WSI format and offers a communication interface based on the most recent DICOM Web services. The second component is a zero-footprint viewer that runs in any web-browser. It consumes data using the PACS archive standard web services. Moreover, it features a tiling engine especially suited to deal with the WSI image pyramids. These components were designed with special focus on efficiency and usability. The performance of our system was assessed through a comparative analysis of the state-of-the-art solutions. The results demonstrate that it is possible to have a very competitive solution based on standard workflows.

Assessing the relational database model for optimization of content discovery services in medical imaging repositories

Medical imaging has been an essential contributor to high-quality medical decisions. In the past few years, the production of medical imaging data has grown impressively, thanks to the increasing number of imaging centers and higher resolution modalities. Keeping high availability and acceptable performance in this scenario raises new challenges related to storage, discovery and distribution of imaging data. Nowadays Picture Archiving and Communication System (PACS) must optimize these processes to the limit to cope with Big Data usage scenarios. In this regard, this work explores novel technologies to improve the performance of query and retrieve services in medical imaging context, ensuring always the compatibility with Digital Imaging and Communications in Medicine (DICOM) standard. The focus is the optimization of querying services. Namely, we conducted several controlled experiments to determine the best database model to support these services. More precisely, we studied the performance of a traditional PACS archive, based on a relational database, against a more recent NoSQL database. We used large datasets with 7 million medical images that represent accurately a year of medical practice. The result of this work is a set of guidelines for the correct usage of analyzed databases in big data medical imaging scenarios, including the advantages and limitations of each model.

An automation framework for PACS workflows optimization in shared environments

The usage paradigm of PACS (Picture Archive and Communications Systems) has been suffering significant alterations throughout the years. Recently, pushed by new services related to Cloud and Web technologies, the usage of PACS in geographically distributed medical environments has promised significant productivity gains as well as increased quality medical services. However, the usage of PACS to support radiology workflows, previously local to institutions, in remote environments still raises great technological and technical challenges to PACS administrators. The following article describes the most common workflows associated with the radiology service and proposes a computer method for their optimization. The proposed method provides an automatic framework for scheduling and performing repetitive tasks without human interaction. Moreover, it was designed to optimize the workflows associated with distributed PACS scenarios by enabling prefetching rules to be applied to medical image studies. The idea is to leverage multiple indicators associated with the radiology workflows in order to predict whether a given study is likely to be requested, and anticipate its transference procedure so that the study is already available when requested.

DICOM metadata aggregation from multiple healhtcare facilities

Medical imaging procedures generate an enormous amount of information that is stored in multiple storage units by Picture Archiving and Communication Systems (PACS). Using Dicoogle and Digital Imaging and Communication in Medicine (DICOM) metadata the authors developed a merging and reconciliation procedure to create a seamlessly unique PACS view. This procedure was applied to the metadata held by 19 storage units of a PACS of a Portuguese hospital centre composed by three branches. In total, more than 36 million images, from more than ten modalities were collected. The merging and reconciliation procedure have proven to be robust and efficient. This shows that it is possible to merge the metadata collected from multiple PACS into a single unified view, preserving the relation between patients, studies, and images, which is essential for large-scale analyses of medical imaging repositories from multiple institutions.

Efficient support for digital pathology in standard medical imaging repositories

Today, the field of digital pathology is in the spotlight thanks to advances in imaging technologies on complete slides. In this area, the operation of digital laboratories has significant advantages, namely faster and more precise diagnosis, better support for tele-pathology as well as new clinical and research applications. Despite these advantages, there has been very slow adoption of whole slide imaging. In fact, it raises several technical challenges that can compromise the benefits of its operation, including performance issues associated with the storage and distribution of huge volumes of data and the deficiency of interoperability with further hospital evidence systems. , such as Depiction Archive and Communications Systems (PACS) grounded on the DICOM standard. We have developed the architecture of a web pathology PACS which is able to overcome these challenges and unlock the full potential of digital pathology and full slide imaging for clinical practice. Our solution fully complies with the DICOM standard for both communications and data formats. It embraces a PACS record capable of storing whole slide images with other medical imaging modalities as well as a zerofingerprint viewer that runs in any common web browser. In summary, it allows the integration of digital pathology and whole slide imaging with other medical imaging methods while being very competitive in terms of efficiency and user-friendliness.Background: Significance of non goblet (GC) columnar mucosa (CM) present at lower end esophagus (LEE) remains controversial, and there is limited information of the follow-up data. Aim: to evaluate outcome of Barretts mucosa (BM) and NGCM in long term follow-up biopsies. Methods: retrospectively evaluated biopsies reported as columnar mucosa (CM) with and without GC and correlated with clinical outcome. Results: There were 178 patients, mean age of 52.1±15.6, 7

Intelligent generator of big data medical imaging repositories

The production of medical imaging data has grown tremendously in the last decades. Nowadays, even small institutions produce a considerable amount of studies. Furthermore, the general trend in new imaging modalities is to produce more data per examination. As a result, the design and implementation of tomorrows storage and communication systems must deal with big data issues. The research on technologies to cope with big data issues in large scale medical imaging environments is still in its early stages. This is mostly due to the difficulty of implementing and validating new technological approaches in real environments, without interfering with clinical practice. Therefore, it is crucial to create test bed environments for research purposes. This study proposes a methodology for creating simulated medical imaging repositories, based on the indexing of model datasets, extraction of patterns and modelling of study production. The system creates a model from a real-world repositorys representative time window and expands it according to on-going research needs. In addition, the solution provides distinct approaches to reducing the size of the generated datasets. The proposed system has already been used by other research projects in validation processes that aim to assess the performance and scalability of developed systems.

Integrating multiple data sources in a cardiology imaging laboratory

Nowadays, medical imaging laboratories are supported by heterogeneous systems, including image repositories, acquisition devices, viewer workstations and other administrative information systems. They hold tremendous amounts of data resulting not only from imaging modalities, but also from patient diagnosis, treatment, and services management. Unfortunately, the interoperability between the different medical information systems is still a major limitation. Despite the existence of standards to support the distinct RIS and PACS applications, such as DICOM and HL7, the interoperability between them is, in most cases, limited to a few sets of information elements. As a result, the establishment of cooperative workflows or the integrated visualization of patient data is still compromised. Moreover, this scenario severely constraints the usage of these data for research and business analytics purposes, commonly referred as secondary uses of data. In this document, we propose a method for transforming echocardiography reports held by proprietary information systems into DICOM Structured Reports (SR), the gold standard for interoperability in medical imaging. As a result, reports, images, and associated metadata can be accessed and shared by all PACS applications in an integrated and structured manner. Furthermore, the large-scale federation of those elements has a tremendous interest for data analytics and secondary uses of data.