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

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.

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.

Method for automatic detection of defective ultrasound linear array transducers based on uniformity assessment of clinical images - A case study

The purpose of the present study was to test an idea of and describe a concept of a novel method of detecting defects related to horizontal nonuniformities in ultrasound equipment. The method is based on the analysis of ultrasound images collected directly from the clinical workflow. In total over 31000 images from three ultrasound scanners from two vendors were collected retrospectively from a database. An algorithm was developed and applied to the images, 150 at a time, for detection of systematic dark regions in the superficial part of the images. The result was compared with electrical measurements (FirstCall) of the transducers, performed at times when the transducers were known to be defective. The algorithm made similar detection of horizontal nonuniformities for images acquired at different time points over long periods of time. The results showed good subjective visual agreement with the available electrical measurements of the defective transducers, indicating a potential use of clinical images for early and automatic detection of defective transducers, as a complement to quality control.

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.

Normalizing Heterogeneous Medical Imaging Data to Measure the Impact of Radiation Dose

The production of medical imaging is a continuing trend in healthcare institutions. Quality assurance for planned radiation exposure situations (e.g. X-ray, computer tomography) requires examination-specific set-ups according to several parameters, such as patient's age and weight, body region and clinical indication. These data are normally stored in several formats and with different nomenclatures, which hinder the continuous and automatic monitoring of these indicators and the comparison between several institutions and equipment. This article proposes a framework that aggregates, normalizes and provides different views over collected indicators. The developed tool can be used to improve the quality of radiologic procedures and also for benchmarking and auditing purposes. Finally, a case study and several experimental results related to radiation exposure and productivity are presented and discussed.

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.

DCMDSM: a DICOM decomposed storage model

OBJECTIVE

To design, build, and evaluate a storage model able to manage heterogeneous digital imaging and communications in medicine (DICOM) images. The model must be simple, but flexible enough to accommodate variable content without structural modifications; must be effective on answering query/retrieval operations according to the DICOM standard; and must provide performance gains on querying/retrieving content to justify its adoption by image-related projects.

METHODS

The proposal adapts the original decomposed storage model, incorporating structural and organizational characteristics present in DICOM image files. Tag values are stored according to their data types/domains, in a schema built on top of a standard relational database management system (RDBMS). Evaluation includes storing heterogeneous DICOM images, querying metadata using a variable number of predicates, and retrieving full-content images for different hierarchical levels.

RESULTS AND DISCUSSION

When compared to a well established DICOM image archive, the proposal is 0.6-7.2 times slower in storing content; however, in querying individual tags, it is about 48.0% faster. In querying groups of tags, DICOM decomposed storage model (DCMDSM) is outperformed in scenarios with a large number of tags and low selectivity (being 66.5% slower); however, when the number of tags is balanced with better selectivity predicates, the performance gains are up to 79.1%. In executing full-content retrieval, in turn, the proposal is about 48.3% faster.

CONCLUSIONS

DCMDSM is a model built for the storage of heterogeneous DICOM content, based on a straightforward database design. The results obtained through its evaluation attest its suitability as a storage layer for projects where DICOM images are stored once, and queried/retrieved whenever necessary.