A national repository of complete radiotherapy plans: design, Results, and experiences.

BACKGROUND: Previously, many radiotherapy (RT) trials were based on a few selected dose measures. Many research questions, however, rely on access to the complete dose information. To support such access, a national RT plan database was created. The system focuses on data security, ease of use, and re-use of data. This article reports on the development and structure, and the functionality and experience of this national database. METHODS AND MATERIALS: A system based on the DICOM-RT standard, DcmCollab, was implemented with direct connections to all Danish RT centres. Data is segregated into any number of collaboration projects. User access to the system is provided through a web interface. The database has a finely defined access permission model to support legal requirements. RESULTS: Currently, data for more than 14,000 patients have been submitted to the system, and more than 50 research projects are registered. The system is used for data collection, trial quality assurance, and audit data set generation.Users reported that the process of submitting data, waiting for it to be processed, and then manually attaching it to a project was resource intensive. This was accommodated with the introduction of triggering features, eliminating much of the need for users to manage data manually. Many other features, including structure name mapping, RT plan viewer, and the Audit Tool were developed based on user input. CONCLUSION: The DcmCollab system has provided an efficient means to collect and access complete datasets for multi-centre RT research. This stands in contrast with previous methods of collecting RT data in multi-centre settings, where only singular data points were manually reported. To accommodate the evolving legal environment, DcmCollab has been defined as a 'data processor', meaning that it is a tool for other research projects to use rather than a research project in and of itself.

FHIR-PYrate: a data science friendly Python package to query FHIR servers.

BACKGROUND: We present FHIR-PYrate, a Python package to handle the full clinical data collection and extraction process. The software is to be plugged into a modern hospital domain, where electronic patient records are used to handle the entire patient's history. Most research institutes follow the same procedures to build study cohorts, but mainly in a non-standardized and repetitive way. As a result, researchers spend time writing boilerplate code, which could be used for more challenging tasks. METHODS: The package can improve and simplify existing processes in the clinical research environment. It collects all needed functionalities into a straightforward interface that can be used to query a FHIR server, download imaging studies and filter clinical documents. The full capacity of the search mechanism of the FHIR REST API is available to the user, leading to a uniform querying process for all resources, thus simplifying the customization of each use case. Additionally, valuable features like parallelization and filtering are included to make it more performant. RESULTS: As an exemplary practical application, the package can be used to analyze the prognostic significance of routine CT imaging and clinical data in breast cancer with tumor metastases in the lungs. In this example, the initial patient cohort is first collected using ICD-10 codes. For these patients, the survival information is also gathered. Some additional clinical data is retrieved, and CT scans of the thorax are downloaded. Finally, the survival analysis can be computed using a deep learning model with the CT scans, the TNM staging and positivity of relevant markers as input. This process may vary depending on the FHIR server and available clinical data, and can be customized to cover even more use cases. CONCLUSIONS: FHIR-PYrate opens up the possibility to quickly and easily retrieve FHIR data, download image data, and search medical documents for keywords within a Python package. With the demonstrated functionality, FHIR-PYrate opens an easy way to assemble research collectives automatically.

Implementation of Automated Pipeline for Resting-State fMRI Analysis with PACS Integration.

In recent years, the quantity and complexity of medical imaging acquisition and processing have increased tremendously. The explosion in volume and need for advanced imaging analysis have led to the creation of numerous software programs, which have begun to be incorporated into clinical practice for indications such as automated stroke assessment, brain tumor perfusion processing, and hippocampal volume analysis. Despite these advances, there remains a need for specialized, custom-built software for advanced algorithms and new areas of research that is not widely available or adequately integrated in these "out-of-the-box" solutions. The purpose of this paper is to describe the implementation of an image-processing pipeline that is versatile and simple to create, which allows for rapid prototyping of image analysis algorithms and subsequent testing in a clinical environment. This pipeline uses a combination of Orthanc server, custom MATLAB code, and publicly available FMRIB Software Library and RestNeuMap tools to automatically receive and analyze resting-state functional MRI data collected from a custom filter on the MR scanner output. The processed files are then sent directly to Picture Archiving and Communications System (PACS) without the need for user input. This initial experience can serve as a framework for those interested in simple implementation of an automated pipeline customized to clinical needs.

BlueLight: An Open Source DICOM Viewer Using Low-Cost Computation Algorithm Implemented with JavaScript Using Advanced Medical Imaging Visualization.

Recently, WebGL has been widely used in numerous web-based medical image viewers to present advanced imaging visualization. However, in the scenario of medical imaging, there are many challenges of computation time and memory consumption that limit the use of advanced image renderings, such as volume rendering and multiplanar reformation/reconstruction, in low-cost mobile devices. In this study, we propose a client-side rendering low-cost computation algorithm for common two- and three-dimensional medical imaging visualization implemented by pure JavaScript. Particularly, we used the functions of cascading style sheet transform and combinate with Digital Imaging and Communications in Medicine (DICOM)-related imaging to replace the application programming interface with high computation to reduce the computation time and save memory consumption while launching medical imaging interpretation on web browsers. The results show the proposed algorithm significantly reduced the consumption of central and graphics processing units on various web browsers. The proposed algorithm was implemented in an open-source web-based DICOM viewer BlueLight; the results show that it has sufficient rendering performance to display 3D medical images with DICOM-compliant annotations and has the ability to connect to image archive via DICOMweb as well.Keywords: WebGL, DICOMweb, Multiplanar reconstruction, Volume rendering, DICOM, JavaScript, Zero-footprint.

Tensor-RT-Based Transfer Learning Model for Lung Cancer Classification.

Cancer is a leading cause of death across the globe, in which lung cancer constitutes the maximum mortality rate. Early diagnosis through computed tomography scan imaging helps to identify the stages of lung cancer. Several deep learning-based classification methods have been employed for developing automatic systems for the diagnosis and detection of computed tomography scan lung slices. However, the diagnosis based on nodule detection is a challenging task as it requires manual annotation of nodule regions. Also, these computer-aided systems have yet not achieved the desired performance in real-time lung cancer classification. In the present paper, a high-speed real-time transfer learning-based framework is proposed for the classification of computed tomography lung cancer slices into benign and malignant. The proposed framework comprises of three modules: (i) pre-processing and segmentation of lung images using K-means clustering based on cosine distance and morphological operations; (ii) tuning and regularization of the proposed model named as weighted VGG deep network (WVDN); (iii) model inference in Nvidia tensor-RT during post-processing for the deployment in real-time applications. In this study, two pre-trained CNN models were experimented and compared with the proposed model. All the models have been trained on 19,419 computed tomography scan lung slices, which were obtained from the publicly available Lung Image Database Consortium and Image Database Resource Initiative dataset. The proposed model achieved the best classification metric, an accuracy of 0.932, precision, recall, an F1 score of 0.93, and Cohen's kappa score of 0.85. A statistical evaluation has also been performed on the classification parameters and achieved a p-value <0.0001 for the proposed model. The quantitative and statistical results validate the improved performance of the proposed model as compared to state-of-the-art methods. The proposed framework is based on complete computed tomography slices rather than the marked annotations and may help in improving clinical diagnosis.

Initial Experience of 10 Imaging Vendors with the IHE SHARAZONE: a New Multivendor Peer-to-Peer Test Service for DICOM Objects.

Alignment of DICOM (Digital Imaging and Communications in Medicine) capabilities among vendors is crucial to improve interoperability in the healthcare industry and advance medical imaging 2. However, a sustainable model for sharing DICOM samples is not available. To address this issue, Integrating the Healthcare Enterprise (IHE) has introduced the IHE SHARAZONE, a continuous cross-vendor DICOM data sharing test service. IHE is a highly regarded organization known for profiling standards such as DICOM, HL7 v2 (Health Level Seven, version 2), HL7 CDA (Clinical Document Architecture), and HL7 FHIR (Fast Healthcare Interoperability Resources) into practical solutions for clinical practice. The primary goal of the IHE SHARAZONE is to provide a reliable and consistent cross-vendor DICOM data sharing system. To evaluate its effectiveness, a 5-month pilot was conducted with ten imaging vendors. The pilot concluded with a participant survey, which yielded valuable insights into the initial experience with the IHE SHARAZONE. These findings can inform future improvements and developments to this important service.

3D maxillofacial surgery planning - one decade development of technology.

Nowadays, techniques and the use of patient specific implants seem to be the recent high technology standard in reconstructive surgery. Surgery planning is as old as the surgery procedures themselves. Any good surgeon, before entering the operating theatre, has a plan for how to proceed. It is based on knowledge and experience in combination of evaluation of all case relevant information. In fact, virtual surgery planning and CAD/CAM reflects the technological "state of the art" into the medical daily practice. Recently, 3D printing technologies became easy and accessible for everyone. Virtual 3D images substituted the plaster models, the film profile analysis switched to digital, 3D printed bone models of the case helped to understand the morphology of the deformity and prepare the osteotomies with "hands on the bone". The authors' own 20 years of experience on surgical planning, the development of digital technologies in oral and maxillofacial surgery is traced and comments on case examples are presented.

Applying Convolutional Neural Networks to Neuroimaging Classification Tasks: A Practical Guide in Python.

In this chapter, we describe the process of obtaining medical imaging data and its storage protocol. The authors also explain in a step-by-step approach how to extract and prepare the medical imaging data for machine learning algorithms. And finally, the process of building and assessing a convolutional neural network for medical imaging data is illustrated.

Development of a novel program for conversion from tetrahedral-mesh-based phantoms to DICOM dataset for radiation treatment planning: TET2DICOM.

PURPOSE: Tetrahedral mesh (TM)-based computational human phantoms have recently been developed for evaluation of exposure dose with the merit of precisely representing human anatomy and the changing posture freely. However, conversion of recently developed TM phantoms to the Digital Imaging and Communications in Medicine (DICOM) file format, which can be utilized in the clinic, has not been attempted. The aim of this study was to develop a technique, called TET2DICOM, to convert the TM phantoms to DICOM datasets for accurate treatment planning. MATERIALS AND METHODS: The TM phantoms were sampled in voxel form to generate the DICOM computed tomography images. The DICOM-radiotherapy structure was defined based on the contour data. To evaluate TET2DICOM, the shape distortion of the TM phantoms during the conversion process was assessed, and the converted DICOM dataset was implemented in a commercial treatment planning system (TPS). RESULTS: The volume difference between the TM phantoms and the converted DICOM dataset was evaluated as less than about 0.1% in each organ. Subsequently, the converted DICOM dataset was successfully implemented in MIM (MIM Software Inc., Cleveland, USA, version 6.5.6) and RayStation (RaySearch Laboratories, Stockholm, Sweden, version 5.0). Additionally, the various possibilities of clinical application of the program were confirmed using a deformed TM phantom in various postures. CONCLUSION: In conclusion, the TM phantom, currently the most advanced computational phantom, can be implemented in a commercial TPS and this technique can enable various TM-based applications, such as evaluation of secondary cancer risk in radiotherapy.

DICOMization of Proprietary Files Obtained from Confocal, Whole-Slide, and FIB-SEM Microscope Scanners.

The evolution of biomedical imaging technology is allowing the digitization of hundreds of glass slides at once. There are multiple microscope scanners available in the market including low-cost solutions that can serve small centers. Moreover, new technology is being researched to acquire images and new modalities are appearing in the market such as electron microscopy. This reality offers new diagnostics tools to clinical practice but emphasizes also the lack of multivendor system's interoperability. Without the adoption of standard data formats and communications methods, it will be impossible to build this industry through the installation of vendor-neutral archives and the establishment of telepathology services in the cloud. The DICOM protocol is a feasible solution to the aforementioned problem because it already provides an interface for visible light and whole slide microscope imaging modalities. While some scanners currently have DICOM interfaces, the vast majority of manufacturers continue to use proprietary solutions. This article proposes an automated DICOMization pipeline that can efficiently transform distinct proprietary microscope images from CLSM, FIB-SEM, and WSI scanners into standard DICOM with their biological information maintained within their metadata. The system feasibility and performance were evaluated with fifteen distinct proprietary modalities, including stacked WSI samples. The results demonstrated that the proposed methodology is accurate and can be used in production. The normalized objects were stored through the standard communications in the Dicoogle open-source archive.

A New Digital Platform for Collecting Measurement Data from the Novel Imaging Sensors in Urology.

The use of UT and EIT technologies gives the opportunity to develop new, effective, minimally invasive diagnostic methods for urology. The introduction of new diagnostic methods into medicine requires the development of new tools for collecting, processing and analysing the data obtained from them. Such system might be seen as a part of the electronic health record EHR system. The digital medical data management platform must provide the infrastructure that will make medical activity possible and effective in the presented scope. The solution presented in this article was implemented using the newest computer technologies to obtain advantages such as mobility, versatility, flexibility and scalability. The architecture of the developed platform, technological stack proposals, database structure and user interface are presented. In the course of this study, an analysis of known and available standards such as Hl7, RIM, DICOM, and tools for collecting medical data was performed, and the results obtained using them are also presented. The developed digital platform also falls into an innovative path of creating a network of sensors communicating with each other in the digital space, resulting in the implementation of the IoT (Internet of Things) vision. The issues of building software based on the architecture of microservices were discussed emphasizing the role of message brokers. The selected message brokers were also analysed in terms of available features and message transmission time.

How Image Exchange Breaks Down: the Image Library Perspective.

While in the last decade there has been significant technical infrastructure development to support standards-based image exchange through organizations like Integrating the Healthcare Enterprise, Carequality, DICOM, and HL7 FHIR, the human operationalization of such infrastructure using centralized, intuitive, standards-based applications remains the cornerstone of effective and reliable electronic image exchange. Image libraries managing the highly transactional and often uncertain inflows and outflows of images have a unique perspective on the challenges of image exchange. This manuscript will summarize frequent collaboration and communication, release of information, staffing, technology, information localization, and analytics difficulties for image exchange from the perspective of the image library staff managing the transactions.

Diagnostic Accuracy of Caries and Periapical Lesions on a Monitor with and without DICOM-GSDF Calibration Under Different Ambient Light Conditions.

To evaluate the diagnostic accuracy of caries and periapical lesions on a monitor with and without DICOM part 14: grayscale standard display function (DICOM-GSDF) calibration under different ambient light conditions. Forty digital bitewing radiographs were selected, with or without radiographic images of carious lesions and forty digital periapical radiographs with or without periapical lesions were selected from archives of the Radiology Department at the University Hospital of the Federal University of Sergipe. The gold standard radiographic images were determined through consensus between two radiologists with more than 15 and 30 years of experience. The selected radiographs were evaluated on a LG LED monitor with and without DICOM-GSDF calibration under different ambient light conditions: Lx1 (low ambient lighting), Lx2 (moderate ambient lighting) and Lx3 (high ambient lighting). Kappa (Kw) values determined that evaluator 1 showed almost perfect agreement for all devices, while evaluator 2 presented a substantial agreement for all devices. Monitors with and without DICOM-GSDF calibration have similar accuracy values. The three ambient light conditions analyzed have similar accuracy and can be used for caries lesions diagnosis (p > 0.05); however, the best diagnostic accuracy of periapical lesions was found in Lx 2. The displays with and without DICOM-GSDF calibration studied in this research have similar accuracy and can be used to evaluate digital radiographs without changing the diagnostic capacity. The different ambient lighting conditions did not influence the evaluation of caries lesions. The best diagnostic accuracy of periapical lesions was found in moderate ambient lighting.

Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology.

Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has been a major barrier for clinical integration and evaluation. The DICOM® standard specifies information object definitions (IODs) and services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with datasets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface (API) for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library leverages NumPy arrays for efficient data representation and ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers and researchers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source at https://github.com/herrmannlab/highdicom .

The RSNA ImageShare - Enabling Secure, Transparent, and Easy Image Exchange.

In the early 2000s, the radiology community was awakened to the limitations of electronic media (CDs, DVDs) for exchanging imaging exams. Clinicians frustrated by the time-consuming task of opening discs, while Internet-based exchange of music, photos, and videos were becoming more widespread. The RSNA, which had extensive experience working on interoperability issues in medical imaging, began to look for opportunities to address the issue. In 2007, in the wake of the financial crisis, the National Institute of Biomedical Imaging and Bioengineering (NIBIB) issued an RFP to address Internet-based exchange of medical images. The RFP defined requirements for the network, including that it needed to be patient controlled and standards based. The RSNA was awarded funding for what came to be known as RSNA ImageShare. Over the next 8 years, the RSNA worked in partnership with several vendors and academic institutions to create a network for sharing image-enabled personal health records (PHR). The foundation of interoperability standards used in ImageShare was provided by Integrating the Healthcare Enterprise (IHE), a standards-development organization with which RSNA has had a long association. In 2018 and 2019, the RSNA looked at what had been accomplished and asked if we could take that next step at a national level and promote a solution by which any standards-compliant party could exchange imaging exams through an HIE mechanism.

An Interview with the Carequality Imaging Use Case Implementers.

Imagine you had a cell phone plan that only allowed you to call other customers within the same carrier network. That is the situation most healthcare providers experience when joining a data sharing network. Carequality is a network-to-network trust framework that brings together the entire healthcare industry to overcome this challenge by providing a national-level, consensus built, common interoperability framework to enable health information exchange between and among health data sharing networks. The RSNA partnered with Carequality in 2019 to develop an implementation guide to enable the Imaging Exchange Use Case. The implementation guide was published in December 2019 for early adopters to sign up as implementers to the Carequality framework. Exchange standards must be clearly laid out so that all implementers can easily follow and be held accountable to enable interoperability of medical imaging. The guide was reviewed and tested by implementers and approved for production use in March 2021. Since the launch of the implementation guide, five Carequality Implementers have participated in Carequality's Image Exchange Use Case: Ambra Health, Hyland, Life Image, Nuance, and Philips. These implementers recognized a gap in image interoperability and the need for change and collaboration. Carequality has asked each of the implementers to share their thoughts on issues pertinent to becoming an implementer and imaging interoperability with the hope that the reader will gain insight as to the evolution of network-based image exchange.

How Carequality, The Sequoia Project, and eHealth Exchange Support the Interoperable Exchange of Health Data in the USA.

Every organization in the health IT industry plays an important role in overcoming barriers to health information exchange in the United States. It is important to understand imaging interoperability in the overall context of Health Information Exchange (HIE). The rapid evolution of storage, bandwidth and network transport technologies has made the handling of imaging data converge with the primarily text-based healthcare data. The radiology community must understand the overall environment and become a tightly integrated part of it. As the health IT ecosystems continue to evolve, it became clear that there would not be a single health information exchange network to service the nation. Rather, like other industries such as telecom and banking, there would be multiple networks that would need to interconnect. To support compliance to interoperability standards and specifications, The Sequoia Project began collaborating with industry to create testing programs and tooling that supports transport, security and content testing requirements for four production testing programs today. These testing programs validate compliance to standards for transport and security as well standards for the payloads such as clinical documents and imaging data. While once operating under the same umbrella, The Sequoia Project, Carequality and eHealth Exchange ( https://ehealthexchange.org/ ) have been separate companies since 2018. Each plays a unique role in helping patient information move where and when it is needed, each working with a framework of standards published by IHE, DICOM, and HL7 to enable health information exchange.

Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization.

Preparing radiology examinations for interpretation requires prefetching relevant prior examinations and implementing hanging protocols to optimally display the examination along with comparisons. Body part is a critical piece of information to facilitate both prefetching and hanging protocols, but body part information encoded using the Digital Imaging and Communications in Medicine (DICOM) standard is widely variable, error-prone, not granular enough, or missing altogether. This results in inappropriate examinations being prefetched or relevant examinations left behind; hanging protocol optimization suffers as well. Modern artificial intelligence (AI) techniques, particularly when harnessing federated deep learning techniques, allow for highly accurate automatic detection of body part based on the image data within a radiological examination; this allows for much more reliable implementation of this categorization and workflow. Additionally, new avenues to further optimize examination viewing such as dynamic hanging protocol and image display can be implemented using these techniques.

Design and Development of a Medical Image Databank for Assisting Studies in Radiomics.

CompreHensive Digital ArchiVe of Cancer Imaging - Radiation Oncology (CHAVI-RO) is a multi-tier WEB-based medical image databank. It supports archiving de-identified radiological and clinical datasets in a relational database. A semantic relational database model is designed to accommodate imaging and treatment data of cancer patients. It aims to provide key datasets to investigate and model the use of radiological imaging data in response to radiation. This domain of research area addresses the modeling and analysis of complete treatment data of oncology patient. A DICOM viewer is integrated for reviewing the uploaded de-identified DICOM dataset. In a prototype system we carried out a pilot study with cancer data of four diseased sites, namely breast, head and neck, brain, and lung cancers. The representative dataset is used to estimate the data size of the patient. A role-based access control module is integrated with the image databank to restrict the user access limit. We also perform different types of load tests to analyze and quantify the performance of the CHAVI databank.

Medical Image Sharing in Japan.

This paper reports the history, background including politics, current status of Japan's health imaging study and other information sharing. Its realization was slow until the Ministry of Health, Labour and Welfare (MHLW) started paying digital image storage at the same rate as films in 2008. Information sharing was initiated in early 2010s, which was before vendors became ready for Integrating the Healthcare Enterprise (IHE) cross-enterprise document sharing (XDS), with the result that most of 34 large regional sharing systems are in non-standardized protocol. One standardized example is the Hamamatsu area where inexpensive online PDI (portable data for imaging) was introduced.