Information Object Definition Research Articles

IODeep: An IOD for the introduction of deep learning in the DICOM standard

Background and objectiveIn recent years, Artificial Intelligence (AI) and in particular Deep Neural Networks (DNN) became a relevant research topic in biomedical image segmentation due to the availability of more and more data sets along with the establishment of well known competitions. Despite the popularity of DNN based segmentation on the research side, these techniques are almost unused in the daily clinical practice even if they could support effectively the physician during the diagnostic process. Apart from the issues related to the explainability of the predictions of a neural model, such systems are not integrated in the diagnostic workflow, and a standardization of their use is needed to achieve this goal. MethodsThis paper presents IODeep a new DICOM Information Object Definition (IOD) aimed at storing both the weights and the architecture of a DNN already trained on a particular image dataset that is labeled as regards the acquisition modality, the anatomical region, and the disease under investigation. ResultsThe IOD architecture is presented along with a DNN selection algorithm from the PACS server based on the labels outlined above, and a simple PACS viewer purposely designed for demonstrating the effectiveness of the DICOM integration, while no modifications are required on the PACS server side. Also a service based architecture in support of the entire workflow has been implemented. ConclusionIODeep ensures full integration of a trained AI model in a DICOM infrastructure, and it is also enables a scenario where a trained model can be either fine-tuned with hospital data or trained in a federated learning scheme shared by different hospitals. In this way AI models can be tailored to the real data produced by a Radiology ward thus improving the physician decision making process.Source code is freely available at https://github.com/CHILab1/IODeep.git.

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.

I095] Patient dosimetry in interventional radiology: concepts and tools

Biological effects induced by exposure to radiation made evident the need for an accurate dosimetry from the early days of X-ray use in medicine. Later the scientific interest turned also to the estimation of risks of radiation-induced cancer and genetic effects. Various dosimetric quantities have been used in diagnostic radiology and sometimes confusion is caused because the same name is used for different quantities. The question arises: what is patient dose and what is not? It’s common practice to indicate the dose received by a patient in radiology using the dosimetric indexes defined for the different imaging modalities. However, dosimetric indexes are a useful tool for practices optimization and comparison, but certainly they don’t describe patient dose. To accurately estimate patient dose there is the need to collect a lot of information concerning patient characteristics and irradiation events in order to obtain organ or tissue dose. Nowadays, thanks to the digital technology used in medical imaging, various ways of collecting these data are available. The information is communicated using specific standards (i.e. DICOM), such as the non-image information object definitions. These objects are carrying information about equipment output and other dose related information which allow the user to provide a more accurate estimate of patient dose. The standards are in continuous development, to provide more and more detailed information and to include most recent modalities (e.g. CBCT). This talk will review the basic concepts on patient dosimetry as well as the existing DICOM objects and IHE profiles used to monitor patient exposure, highlighting pros and cons and introducing to the future developments.

DICOM standards for patient dosimetry

Data related to patient radiation exposure during radiologic procedures can be stored and communicated using the non-image information object definitions (non IOD) DICOM objects. More specifically, there are three DICOM objects in the standard that can be used to transport exposure information: the DICOM header, the DICOM Modality Performed Procedure Step (MPPS) and the DICOM Radiation Dose Structured Report (RDSR). All these objects have several advantages and some limitations. All the mentioned objects are carrying information about equipment output and do not provide any information on patient dose. The most complete of the three objects previously mentioned is the DICOM RDSR, which was added to the Standard in 2005. This object transports the information related to the equipment output along with information about the patient and the procedure that could be used to estimate the exposure to the patient. Although unfortunately this information is not complete. Moreover some new modalities (e.g: CBCT) need specific attributes to be stored and communicated and the present RDSR structure needs to be enhanced to allow it. Furthermore none of this DICOM object includes a template for patient-specific dose data to be recorded after estimation. For this reason a new DICOM object, the Patient Radiation Dose Structured Report (P-RDSR) has being developed. The new P-RDSR will provide the archival tool to record the results of the estimations and the methodology used to achieve this estimate.

Storage of Fractional Flow Reserve Hemodynamic Waveforms Using Semantic Extension of the DICOM Standard.

Visual assessment of coronary stenoses by coronary angiography remains widely used but correlates poorly with ischemia, particularly for moderate lesions. Fractional flow reserve (FFR) is a cardiac catheterization procedure that aims to provide objective measures of coronary lesion hemodynamic significance and involves the acquisition of phasic pressure and electrocardiographic waveforms. The dataset from these procedures currently remains in proprietary systems with restricted data access, inability for data exchange, and often inadequate archiving. Digital Imaging and Communications in Medicine (DICOM) includes a waveform information object definition. We describe the method of encapsulating FFR procedural information into a DICOM waveform file. We define private data elements to capture modality-specific data that is not represented by standard DICOM data elements. We propose the adoption of this semantic extension of the DICOM waveform information object for exchange and archiving of data from studies of pressure-derived indices of coronary stenoses.

The DICOM Radiation Dose Structured Report: What It Is and What It Is Not

Although most radiologists and medical physicists have heard of the term “DICOM,” which stands for Digital Imaging and Communications inMedicine,many associate this term onlywith the format used to save radiographic images. However, the DICOM standard actually includes the specifications for communicating many types of medical-related data in an organized manner so that devices from different hardware and software manufacturers can talk to one another [1]. Examples of these different types of data, called nonimage information object definitions (IODs) in DICOMspeak, are treatment plans, waveforms, and structured reports, among others. Over the past few years, there has been an increasing awareness of the importance of the radiation dose used to acquire images with ionizing radiation, and with it there has been an increase in the efforts to optimize the levels of radiation used. For this, it is important to be able to store and analyze data regarding the exposure involved during these acquisitions in a standard format that can be machine read and interpreted. Although exposure information is recorded in the header of each image, this solution is not complete or ideal for various reasons. In the first place, there is no standardmethod to decouple the image itself from the exposure information. Therefore, to access and store the exposure information, it must be accompanied by the image data,

Design and Implementation of Standard DICOM Interface Module

DICOM (Digital Imaging and Communications in Medicine) which is standard of PACS(Picture Archiving Communication System) is set up as draft international standard for the purpose of collection and exchange the data and image among main medical display equipments. Medical display equipments is normally utilized as connected feature with surgery monitoring devices like endoscope, X-lay camera. However, most of medical surgery monitoring devices are not adaptable for DICOM, thus it is diffcult to connect data thru PACS. We suggest the connection methodology for PACS and medical surgery monitoring devices by implementation of HL7 interface which can share the medical information with DICOM. The standard DICOM interface module suggested in this thesis creates DICOM IOD (Information Object Definition) information class according to the checkup information received by HIS (Hospital Information System) and also creates standard DICOM format after blending with surgery monitoring data.

TU‐B‐134‐01: Imaging Informatics 2

Radiation Dose Structured ReportsMultiple diagnostic and/or interventional systems may expose a patient to radiation. The DICOM Information Object Definition (IOD) for these modalities may include acquisition data concerning the irradiation in each image instance produced. Yet, the aggregate irradiation information may not be easily determined from the individual image instances. To alleviate these issues the Radiation Dose Reporting information object was developed. In this session the configuration and organization of DICOM structured reports, and specifically the Radiation Dose Structured Report (RDSR), will be presented. This overview will include the basic construct of structured reporting IOD's and where this content in found in the DICOM standard. The current content and potential future enhancements to the RDSR will be presented. In addition, an overview of a new DICOM work item on the develpment of a Patient Radiation Dose Structured Report and the issues that this new IOD will require will be presented.Learning Objectives:1. Understand the basic concepts of DICOM structured reporting.2. Describe the organization of the DICOM standard in relation to structured reporting with references to the relevant parts and sections.3. Review the current content and recent extensions to the Radiation Dose Structured Report.4. Discuss the proposed organization for developing a Patient Radiation Dose Structured Report.

WE-SAM - BRB-02: DICOM- an Overview with an Emphasis on Therapy

In recent years, there has been a large increase in the amount of data required to deliver a radiotherapytreatment due to advances in IMRT, modulated arc delivery,IGRT, and 4D imaging. This has also resulted in an increased reliance on the Digital Imaging and Communications in Medicine (DICOM) standard in radiation oncology in order to move treatment and verification data from imaging to planning to treatment. This session will go over the fundamentals of the DICOM standard, with an emphasis on radiotherapy. Topics to be covered include the DICOM communications protocol, DICOM workstations and application entity configurations, content and structure of a part 10 file, RT‐Plan Information Object Definition (IOD), free ware and shareware applications, and clinical examples of troubleshooting data transfers and extracting associated information found in DICOM headers. Learning Objectives: 1. Understand the communication amongst DICOM Application Entities. 2. Learn the content and structure of a DICOM part 10 file. 3. Understand the DICOM RT‐Plan IOD. 4. Review DICOM software applications and their uses in clinical radiotherapy processes.

Development of a software for the exchange of electrophysiological data of vision with PACS systems based on DICOM

Abstract Purpose Digital imaging and communications in medicine (DICOM) is widely acknowledged as standard in medical informatics. It provides a communication protocol for interchange of diagnostic and therapeutic information, images, and any associated data. Medical devices with DICOM interfaces can be connected to share data. DICOM evolves continuously in supporting new modalities and services and now allows also the interchange of waveforms. In electrophysiology of vision however, storage and exchange of data is mostly based on proprietary protocols of device vendors. We developed a software for vendor neutral storage of electrophysiological data based on DICOM. Methods DICOM follows the object‐oriented paradigm and defines information object definitions (IODs), describing the type of data dealt with (e. g. CT image) as well as service classes (SC) specifying valid operations for specific IODs (e. g. store). SCs and IOPs form so called service object pairs (SOP). In this work, the IOD for waveform data was extended using private attributes as designated by the DICOM standard. A service class user (SCU) for the connection to a picture archiving and communication system (PACS) was developed. The software is based on the PixelMed Java DICOM Toolkit. Testing was done using dcm4chee as PACS. Results The software is integrated in Tuebingen ERG Explorer, allowing for submission of electrophysiological recordings to a PACS. It is used as a prototype at the University Eye Hospital Tuebingen. Conclusion We have introduced a IOD for storage of electrophysiological data of vision as a first step of integration into a distributed eHealth network, leveraging existing DICOM infrastructure like PACS.

The semantic extension and storage of EECP Hemodynamic Waveforms based on DICOM Standard

Digital imaging and communications in medicine (DICOM) Standard has a detailed description on the Information Object Definition (IOD) of all kinds of medical images and waveforms. With the development and application of DICOM, all medical imaging and waveform devices will support the standard. This article describes the method and implementation on how to encapsulate the EECP Hemodynamic Waveforms data acquired from EECP device, integrating patient information, EECP physiological parameters, and diagnosis and treat information into DICOM Hemodynamic Waveform file. We define Private Data Elements to encode and represent EECP parameters which haven't been registered as Standard Data Elements. This is the semantic extension of DICOM applied in EECP. The paper introduces following parts in detail: the structure of DICOM waveform file, Data Element, Nesting of Data Sets, the Waveform IOD Modules and the specification of Private Data Element. Then the method and process of our program are analyzed in depth. According to object-oriented methodology, firstly, Data Element, Nesting of Data Sets and waveform IOD with their corresponding operations and services are respectively abstracted into classes. Then the waveforms data and other attributes are assigned to the corresponding Data Members of the waveform class. Finally, they are stored into a DICOM waveform file by invoking related functions.

Integration of haemodynamic and electrocardiographic waveform data with DICOM images.

The cardiac catheterization procedure involves acquisition of haemodynamic and electrocardiographic waveforms, as well as X-ray angiographic images. The DICOM standards is being extended to address robust interchange of this waveform data together with the image data, so as to present an integrated data set for the procedure. The proposed waveform information object definition is one of the new generation of DICOM extensions, and uses controlled vocabularies and coded entry sequences to represent clinically significant aspects of the data acquisition. This enables a generic waveform object to be specialized for disparate uses, e.g., haemodynamics, electrocardiograms, phonocardiograms, and electrophysiological data.

Exchange of medical imaging and data information in radiotherapy: needs, methods and current limits

Extension of the image network within radiotherapy departments provides the technical infrastructure which is made necessary by the rapid evolution of techniques in the field of diagnosis and treatment in radiotherapy. The system is aimed at managing the whole set of data (textual data and images) that are needed for planning and control of treatments. The radiotherapy network addresses two objectives: managing both the information necessary for treatment planning (target volumes definition, planning dosimetry) and the control of all parameters involved during the patient's treatment under the treatment unit. The major challenge is to improve the quality of treatment. Multimodal imaging is a major advance as it allows the use of new dosimetry and simulation techniques. The need for standards to exchange medical imaging information is now recognized by all the institutions and a majority of users and manufacturers. It is widely accepted that the lack of standard has been one of the fundamental obstacles in the deployment of operational "Picture Archiving Communication Systems". The International Standard Organisation Open System Interconnection model is the standard reference mode used to describe network protocols. The network is based on the Ethernet and TCP/IP protocol that provides the means to interconnect imaging devices and workstations dedicated to specific image processing or machines used in radiotherapy. The network uses Ethernet cabled on twisted-pair (10 BaseT) or optical fibres in a star-shaped physical layout. Dicom V3.0 supports fundamental network interactions: transfer of images (computerized tomography magnetic resonance imaging query and retrieve of images), printing on network attached cameras, support of HIS/RIS related interfacing and image management. The supplement to the Dicom standard, Dicom RT, specifies five data objects known in Dicom as Information Object Definition for relevant radiotherapy. Dicom RT objects can provide a mean for standardized transfer of most of the information wich circulates in a radiotherapy department. A wide range of device types are represented, (treatment planning systems, portal imaging devices, linear accelerators, recording and verifying systems, conventional and virtual simulators). There will be additions and perhaps new developments in dataflow management, more complete integration with HIS/RIS system and printing, but the lion's share of the work has now been done. A project to integrate some security features into the Dicom protocol has begun.