DICOM Metadata repository for technical information in digital medical images

Successes and challenges in extracting information from DICOM image databases for audit and research

In radiography, much valuable associated data (metadata) is generated during image acquisition. The current setup of picture archive and communication systems (PACS) can make extraction of this metadata difficult, especially as it is typically stored with the image. The aim of this work is to examine the current challenges in extracting image metadata and to discuss the potential benefits of using this rich information. This work focuses on breast screening, though the conclusions are applicable to other modalities.The data stored in PACS contain information, currently underutilised, and is of great benefit for auditing and improving imaging and radiographic practice. From the literature, we present examples of the potential clinical benefit such as audits of dose, and radiographic practice, as well as more advanced research highlighting the effects of radiographic practice, e.g. cancer detection rates affected by imaging technology.This review considers the challenges in extracting data, namely,• The search tools for data on most PACS are inadequate being both time-consuming and limited in elements that can be searched.• Security and information governance considerations• Anonymisation of data if required• Data curationThe review describes some solutions that have been successfully implemented.• Retrospective extraction: direct query on PACS• Extracting data prospectively• Use of structured reports• Use of trusted research environmentsUltimately, the data access process will be made easier by inclusion during PACS procurement. Auditing data from PACS can be used to improve quality of imaging and workflow, all of which will be a clinical benefit to patients.

Technical note: Four-year experience with utilization of DICOM metadata analytics in clinical digital radiography practice

BACKGROUND

Digital radiography (DR) still presents many challenges and could have complex imaging acquisition and processing patterns in a clinical practice hindering quality standardization.

PURPOSE

This technical note aims to report the 4-year experience with utilizing a custom DICOM metadata analytics program in clinical DR at a large institution.

METHODS

Thirty-eight DR systems of three vendors at multiple locations were configured to automatically send clinical DICOM images to a DICOM receiver. A suite of custom MATLAB programs was established to extract and store public and private header data weekly. Specific use cases are provided for systematic image acquisition investigation, image processing harmonization, exposure index (EI) longitudinal monitoring and EI target optimization.

RESULTS

For systematic acquisition investigation, an example of adult lumbar spine exam analysis was provided with statistics on manual acquisition versus the use of automatic exposure control (AEC, including AEC dose level, active cell, and backup timer), grid usage, and collimation for various projections. For processing harmonization, up to 12.6% of protocols were revealed to have processing parameter differences in an example of a mobile radiography fleet. In addition, inconsistent use of a post-acquisition image size function was also demonstrated, which resulted in anatomy size display variations. Bimonthly monitoring of median EI values showed expected trends, including changes after an AEC dose level adjustment for adult posterior-anterior chest imaging on a scanner system. An example of adult axillary shoulder EI target refinement was shared using the median value, e^μ , based on the lognormal EI data distribution after parsing down to acquisitions with appropriate techniques.

CONCLUSIONS

This analytics program enables systematic analysis of image acquisition and processing details. The information provides invaluable insights into real practice patterns, which can support data-driven quality standardization and optimization.

Deep Learning-based Detection of Intravenous Contrast Enhancement on CT Scans

Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly documented in imaging metadata, necessitating impractical manual annotation by clinician experts. Authors developed a convolutional neural network (CNN)-based deep learning platform to identify intravenous contrast enhancement on CT scans. For model development and validation, authors used six independent datasets of head and neck (HN) and chest CT scans, totaling 133 480 axial two-dimensional sections from 1979 scans, which were manually annotated by clinical experts. Five CNN models were trained first on HN scans for contrast enhancement detection. Model performances were evaluated at the patient level on a holdout set and external test set. Models were then fine-tuned on chest CT data and externally validated. This study found that Digital Imaging and Communications in Medicine metadata tags for intravenous contrast material were missing or erroneous for 1496 scans (75.6%). An EfficientNetB4-based model showed the best performance, with areas under the curve (AUCs) of 0.996 and 1.0 in HN holdout (n = 216) and external (n = 595) sets, respectively, and AUCs of 1.0 and 0.980 in the chest holdout (n = 53) and external (n = 402) sets, respectively. This automated, scan-to-prediction platform is highly accurate at CT contrast enhancement detection and may be helpful for artificial intelligence model development and clinical application. Keywords: CT, Head and Neck, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms, Contrast Material Supplemental material is available for this article. © RSNA, 2022.

Deep Semi-Supervised Algorithm for Learning Cluster-Oriented Representations of Medical Images Using Partially Observable DICOM Tags and Images

The task of automatically extracting large homogeneous datasets of medical images based on detailed criteria and/or semantic similarity can be challenging because the acquisition and storage of medical images in clinical practice is not fully standardised and can be prone to errors, which are often made unintentionally by medical professionals during manual input. In this paper, we propose an algorithm for learning cluster-oriented representations of medical images by fusing images with partially observable DICOM tags. Pairwise relations are modelled by thresholding the Gower distance measure which is calculated using eight DICOM tags. We trained the models using 30,000 images, and we tested them using a disjoint test set consisting of 8000 images, gathered retrospectively from the PACS repository of the Clinical Hospital Centre Rijeka in 2017. We compare our method against the standard and deep unsupervised clustering algorithms, as well as the popular semi-supervised algorithms combined with the most commonly used feature descriptors. Our model achieves an NMI score of 0.584 with respect to the anatomic region, and an NMI score of 0.793 with respect to the modality. The results suggest that DICOM data can be used to generate pairwise constraints that can help improve medical images clustering, even when using only a small number of constraints.

ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION

Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a general workflow that can begin to address this issue. What is described is a data management process comprising the three steps of collection, mining and contextualisation. This process offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow effectively combines the video and structured data to enable a new level of insights in the optimisation process.

Automated Billing Code Retrieval from MRI Scanner Log Data

Although the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit from an automated system, and thus a prediction model for automated assignment of billing codes for MRI exams based on MRI log data is developed in this work. To the best of our knowledge, it is the first attempt to focus on the prediction of billing codes from modality log data. MRI log data provide a variety of information, including the set of executed MR sequences, MR scanner table movements, and given a contrast medium. MR sequence names are standardized using a heuristic approach and incorporated into the features for the prediction. The prediction model is trained on 9754 MRI exams and tested on 1 month of log data (423 MRI exams) from two MRI scanners of the radiology site for the Swiss medical tariffication system Tarmed. The developed model, an ensemble of classifier chains with multilayer perceptron as a base classifier, predicts medical billing codes for MRI exams with a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%). Manual coding reaches a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%). Thus, the performance of automated coding is close to human performance. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding an administrative task, therefore enhance the MRI workflow, and prevent coding errors.

Understanding Scanner Utilization With Real-Time DICOM Metadata Extraction

Understanding system performance metrics ensures better utilization of the radiology resources with more targeted interventions. The images produced by radiology scanners typically follow the DICOM (Digital Imaging and Communications in Medicine) standard format. The DICOM images consist of textual metadata that can be used to calculate key timing parameters, such as the exact study durations and scanner utilization. However, hospital networks lack the resources and capabilities to extract the metadata from the images quickly and automatically compute the scanner utilization properties. Thus, they resort to using data records from the Radiology Information Systems (RIS). However, data acquired from RIS are prone to human errors, rendering many derived key performance metrics inadequate and inaccurate. Hence, there is motivation to establish a real-time image transfer from the Picture Archiving and Communication Systems (PACS) to receive the DICOM images from the scanners to research clusters to conduct such metadata processing to evaluate scanner utilization metrics efficiently and quickly. This paper analyzes the scanners' utilization by developing a real-time monitoring framework that retrieves radiology images into a research cluster using the DICOM networking protocol and then extracts and processes the metadata from the images. Our proposed approach facilitates a better understanding of scanner utilization across a vast healthcare network by observing properties such as study duration, the interval between the encounters, and the series count of studies. Benchmarks against using the RIS data indicate that our proposed framework based on real-time PACS data estimates the scanner utilization more accurately. Furthermore, our framework has been running stable and performing its computation for more than two years on our extensive healthcare network in pseudo real-time.

Scalable and flexible management of medical image big data

Digital imaging plays a critical role for image guided diagnosis and clinical trials, and the amount of image data is fast growing. There are two major requirements for image data management: scalability for massive scales and support of comprehensive queries. Traditional Picture Archiving and Communication Systems (PACS for short) are based on relational data management systems and suffer from limited scalability and query support. Therefore, new systems that support fast, scalable and comprehensive queries on image data are highly demanded. In this paper, we introduce two alternative approaches: DCMRL/XMLStore (RL/XML for short)-a parallel, hybrid relational and XML data management approach, and DCMDocStore (DOC for short)-a NoSQL document store approach. DCMRL/XMLStore manages DICOM images as binary large objects and metadata as relational tables and XML documents based on IBM DB2, which is parallelized through data partitioning. DCMDocStore manages DICOM metadata as JSON objects, and DICOM images as encoded attachments in MongoDB running on multiple nodes. We have delivered two open source systems DCMRL/XMLStore and DCMDocStore. Both systems support scalable data management and comprehensive queries. We also evaluated them with nearly one million DICOM images from National Biomedical Imaging Archive. The results show that, DCMDocStore demonstrates high data loading speed, high scalability and fault tolerance. DCMRL/XMLStore provides efficient queries, but comes with slower data loading. Traditional PACS systems have inherent limitations on flexible queries and scalability for massive amount of images.

DICOM Metadata Analysis for Population Studies

This article reports an experimental study to determine how to use the stored Digital Imaging and Communication in Medicine (DICOM) metadata to perform population studies. As a case study, it was considered three types of medical imaging studies (i.e. routine head computed tomography, thorax computed radiography and thorax digital radiography) stored in the picture archiving and communication systems (PACS) of three healthcare institutions. The final sample consisted of DICOM metadata belonging to 1370360 images, corresponding to 109160 medical imaging studies performed on 72716 patients. The study followed a methodological approach that allows the identification of the number of patients with performed studies by age group and gender, as well as the average number of studies by patient, age group and gender in each one of the three healthcare institutions. The results show the relevance of the aggregation and analyses of DICOM metadata stored in heterogeneous PACS facilities.

DBCG hypo trial validation of radiotherapy parameters from a national data bank versus manual reporting

INTRODUCTION

The current study evaluates the data quality achievable using a national data bank for reporting radiotherapy parameters relative to the classical manual reporting method of selected parameters.

METHODS

The data comparison is based on 1522 Danish patients of the DBCG hypo trial with data stored in the Danish national radiotherapy data bank. In line with standard DBCG trial practice selected parameters were also reported manually to the DBCG database. Categorical variables are compared using contingency tables, and comparison of continuous parameters is presented in scatter plots.

RESULTS

For categorical variables 25 differences between the data bank and manual values were located. Of these 23 were related to mistakes in the manual reported value whilst the remaining two were a wrong classification in the data bank. The wrong classification in the data bank was related to lack of dose information, since the two patients had been treated with an electron boost based on a manual calculation, thus data was not exported to the data bank, and this was not detected prior to comparison with the manual data. For a few database fields in the manual data an ambiguity of the parameter definition of the specific field is seen in the data. This was not the case for the data bank, which extract all data consistently.

CONCLUSIONS

In terms of data quality the data bank is superior to manually reported values. However, there is a need to allocate resources for checking the validity of the available data as well as ensuring that all relevant data is present. The data bank contains more detailed information, and thus facilitates research related to the actual dose distribution in the patients.

DICOM Standard Conformance in Veterinary Medicine in Germany: a Survey of Imaging Studies in Referral Cases

In 2016, the recommendations of the DICOM Standards Committee for the use of veterinary identification DICOM tags had its 10th anniversary. The goal of our study was to survey veterinary DICOM standard conformance in Germany regarding the specific identification tags veterinarians should use in veterinary diagnostic imaging. We hypothesized that most veterinarians in Germany do not follow the guidelines of the DICOM Standards Committee. We analyzed the metadata of 488 imaging studies of referral cases from 115 different veterinary institutions in Germany by computer-aided DICOM header readout. We found that 25 (5.1%) of the imaging studies fully complied with the "veterinary DICOM standard" in this survey. The results confirmed our hypothesis that the recommendations of the DICOM Standards Committee for the consistent and advantageous use of veterinary identification tags have found minimal acceptance amongst German veterinarians. DICOM does not only enable connectivity between machines, DICOM also improves communication between veterinarians by sharing correct and valuable metadata for better patient care. Therefore, we recommend that lecturers, universities, societies, authorities, vendors, and other stakeholders should increase their effort to improve the spread of the veterinary DICOM standard in the veterinary world.

Digital radiography exposure indices: A review

Digital radiography (DR) technologies have the advantage of a wide dynamic range compared to their film-screen predecessors, however, this poses a potential for increased patient exposure if left unchecked. Manufacturers have developed the exposure index (EI) to counter this, which provides radiographers with feedback on the exposure reaching the detector. As these EIs were manufacturer-specific, a wide variety of EIs existed. To offset this, the international standardised EI has been developed by the International Electrotechnical Commission (IEC) and the American Association of Physicists in Medicine (AAPM). The purpose of this article is to explore the current literature relating to EIs, beginning with the historical development of the EI, the development of the standardised EI and an exploration of common themes and studies as evidenced in the research literature. It is anticipated that this review will provide radiographers with a useful guide to understanding EIs, their application in clinical practice, limitations and suggestions for further research.

Dicoogle - an open source peer-to-peer PACS

Picture Archiving and Communication Systems (PACS) have been widely deployed in healthcare institutions, and they now constitute a normal commodity for practitioners. However, its installation, maintenance, and utilization are still a burden due to their heavy structures, typically supported by centralized computational solutions. In this paper, we present Dicoogle, a PACS archive supported by a document-based indexing system and by peer-to-peer (P2P) protocols. Replacing the traditional database storage (RDBMS) by a documental organization permits gathering and indexing data from file-based repositories, which allows searching the archive through free text queries. As a direct result of this strategy, more information can be extracted from medical imaging repositories, which clearly increases flexibility when compared with current query and retrieval DICOM services. The inclusion of P2P features allows PACS internetworking without the need for a central management framework. Moreover, Dicoogle is easy to install, manage, and use, and it maintains full interoperability with standard DICOM services.

Automated detection of changes in patient exposure in digital projection radiography using exposure index from DICOM header metadata

PURPOSE

Automated collection of image data from DICOM headers enables monitoring of patient dose and image quality parameters. Manual monitoring is time consuming, owing to the large number of exposure scenarios, thus automated methods for monitoring needs to be investigated. The aim of the present work was to develop and optimise such a method.

MATERIAL AND METHODS

Exposure index values from digital systems in projection radiography were collected over a period of five years, representing data from 1.2 million projection images. The exposure index values were converted to detector dose and an automated method for detection of sustained level shifts in the resulting detector dose time series was applied using the statistical analysis tool R. The method combined handling of outliers, filtering and estimation of variation in combination with two different statistical rank tests for level shift detection. A set of 304 time series representing central body parts was selected and the level shift detection method was optimised using level shifts identified by ocular evaluation as the gold standard.

RESULTS

Two hundred and eighty-one level changes were identified that were deemed in need of further investigation. The majority of these changes were abrupt. The sensitivity and specificity of the optimised and automated detection method concerning the ocular evaluation were 0.870 and 0.997, respectively, for detected abrupt changes.

CONCLUSIONS

An automated analysis of exposure index values, with the purpose of detecting changes in exposure, can be performed using the R software in combination with a DICOM header metadata repository containing the exposure index values from the images. The routine described has good sensitivity and acceptable specificity for a wide range of central body part projections and can be optimised for more specialised purposes.

Efficient quality assurance programs in radiology and nuclear medicine in Ostergotland, Sweden

Owners of imaging modalities using ionising radiation should have a documented quality assurance (QA) program, as well as methods to justify new radiological procedures to ensure safe operation and adequate clinical image quality. This includes having a system for correcting divergences, written imaging protocols, assessment of patient and staff absorbed doses and a documented education and training program. In this work, how some aspects on QA have been implemented in the County of Ostergötland in Sweden, and efforts to standardise and automate the process as an integrated part of the radiology and nuclear medicine QA programs were reviewed. Some key performance parameters have been identified by a Swedish task group of medical physicists to give guidance on selecting relevant QA methods. These include low-contrast resolution, image homogeneity, automatic exposure control, calibration of air kerma-area product metres and patient-dose data registration in the radiological information system, as well as the quality of reading stations and of the transfer of images to the picture archive and communication system. IT-driven methods to automatically assess patient doses and other data on all examinations are being developed and evaluated as well as routines to assess clinical image quality by use of European quality criteria. By assessing both patient absorbed doses and clinical image quality on a routine basis, the medical physicists in our region aim to be able to spend more time on imaging optimisation and less time on periodic testing of the technical performance of the equipment, particularly on aspects that show very few divergences. The role of the Medical Physics Expert is rapidly developing towards a person doing advanced data-analysis and giving scientific support rather than one performing mainly routine periodic measurements. It is concluded that both the European Council directive and the rapid development towards more complex diagnostic imaging systems and procedures support this changing role of the medical physics professional.

Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?

Despite major advances in x-ray sources, detector arrays, gantry mechanical design and especially computer performance, one component of computed tomography (CT) scanners has remained virtually constant for the past 25 years-the reconstruction algorithm. Fundamental advances have been made in the solution of inverse problems, especially tomographic reconstruction, but these works have not been translated into clinical and related practice. The reasons are not obvious and seldom discussed. This review seeks to examine the reasons for this discrepancy and provides recommendations on how it can be resolved. We take the example of field of compressive sensing (CS), summarizing this new area of research from the eyes of practical medical physicists and explaining the disconnection between theoretical and application-oriented research. Using a few issues specific to CT, which engineers have addressed in very specific ways, we try to distill the mathematical problem underlying each of these issues with the hope of demonstrating that there are interesting mathematical problems of general importance that can result from in depth analysis of specific issues. We then sketch some unconventional CT-imaging designs that have the potential to impact on CT applications, if the link between applied mathematicians and engineers/physicists were stronger. Finally, we close with some observations on how the link could be strengthened. There is, we believe, an important opportunity to rapidly improve the performance of CT and related tomographic imaging techniques by addressing these issues.