Medical images support diagnostic care and research in medicine. The acquisition and availability of medical images in digital form can facilitate quick diagnosis, ease of access, continuity of care, ...analysis and contribute to modern medical research. Digital Imaging and Communications in Medicine (DICOM) is a universal standard that promises standardized representation and exchange of medical images and related information from various radiological and waveform sources. DICOM software development kits or tools or libraries make it easier to implement DICOM standard in healthcare applications. There are several such API libraries available from different providers that promise DICOM integration. In this paper, we explore available DICOM API libraries and conduct a comparative study between a set of selected libraries on the four major criteria (DICOM features, technical aspects, the robustness of the library, and the level of user support available). The aim is to provide a complete picture of options available that can help in finding a best-fit open-source DICOM standard integration API library for developing standardized and interoperable healthcare applications.
Cybersecurity is increasingly affecting the healthcare sector. In a recent article, the authors analyzed specific attacks against picture archiving and communications systems (PACS) and medical ...imaging networks and proposed security measures. This article discusses issues that require consideration when deploying these proposed measures and provides recommendations on how to implement them. Hospitals should deploy virus scanners on systems where permitted, with high priority on devices that are part of the central IT infrastructure of the hospital. They should introduce a systematic management of software updates on operating system, application software and virus scanner level and clarify the provision of security updates for the intended duration of use when purchasing a new device. They should agree with the PACS vendor on a long-term strategy for implementing access rights, and enable encrypted network communication where possible. This requires an agreement on the encryption algorithms to be used, and a public-key infrastructure. For most of these tasks, standards and profiles exist today. There are, however, some gaps: Implementation of cybersecurity measures would be facilitated by integration profiles on certificate and signature management, and access rights in a PACS environment.
Medical imaging repositories based on the DICOM format such as Picture Archiving and Communication Systems have a huge potential from a big data perspective. The study reported by this paper aimed to ...verify on how the big data lifecycle processes, from production to consumption might be implemented to take advantage of the DICOM standard and respective data sources. After identifying the different processes (i.e., data collection, integration, filtering, anonymization and enrichment, and knowledge extraction) the study demonstrate their implementation using an open-source application able to access the DICOM metadata independently of the medical imaging modalities and equipment manufacturers.
Purpose
We report on the development of the open‐source cross‐platform radiation treatment planning toolkit matRad and its comparison against validated treatment planning systems. The toolkit enables ...three‐dimensional intensity‐modulated radiation therapy treatment planning for photons, scanned protons and scanned carbon ions.
Methods
matRad is entirely written in Matlab and is freely available online. It re‐implements well‐established algorithms employing a modular and sequential software design to model the entire treatment planning workflow. It comprises core functionalities to import DICOM data, to calculate and optimize dose as well as a graphical user interface for visualization. matRad dose calculation algorithms (for carbon ions this also includes the computation of the relative biological effect) are compared against dose calculation results originating from clinically approved treatment planning systems.
Results
We observe three‐dimensional γ‐analysis pass rates ≥ 99.67% for all three radiation modalities utilizing a distance to agreement of 2 mm and a dose difference criterion of 2%. The computational efficiency of matRad is evaluated in a treatment planning study considering three different treatment scenarios for every radiation modality. For photons, we measure total run times of 145 s–1260 s for dose calculation and fluence optimization combined considering 4–72 beam orientations and 2608–13597 beamlets. For charged particles, we measure total run times of 63 s–993 s for dose calculation and fluence optimization combined considering 9963–45574 pencil beams. Using a CT and dose grid resolution of 0.3 cm3 requires a memory consumption of 1.59 GB–9.07 GB and 0.29 GB–17.94 GB for photons and charged particles, respectively.
Conclusion
The dosimetric accuracy, computational performance and open‐source character of matRad encourages a future application of matRad for both educational and research purposes.
Purpose:
Interest in adaptive radiation therapy research is constantly growing, but software tools available for researchers are mostly either expensive, closed proprietary applications, or free ...open-source packages with limited scope, extensibility, reliability, or user support. To address these limitations, we propose SlicerRT, a customizable, free, and open-source radiation therapy research toolkit. SlicerRT aspires to be an open-source toolkit for RT research, providing fast computations, convenient workflows for researchers, and a general image-guided therapy infrastructure to assist clinical translation of experimental therapeutic approaches. It is a medium into which RT researchers can integrate their methods and algorithms, and conduct comparative testing.
Methods:
SlicerRT was implemented as an extension for the widely used 3D Slicer medical image visualization and analysis application platform. SlicerRT provides functionality specifically designed for radiation therapy research, in addition to the powerful tools that 3D Slicer offers for visualization, registration, segmentation, and data management. The feature set of SlicerRT was defined through consensus discussions with a large pool of RT researchers, including both radiation oncologists and medical physicists. The development processes used were similar to those of 3D Slicer to ensure software quality. Standardized mechanisms of 3D Slicer were applied for documentation, distribution, and user support. The testing and validation environment was configured to automatically launch a regression test upon each software change and to perform comparison with ground truth results provided by other RT applications.
Results:
Modules have been created for importing and loading DICOM-RT data, computing and displaying dose volume histograms, creating accumulated dose volumes, comparing dose volumes, and visualizing isodose lines and surfaces. The effectiveness of using 3D Slicer with the proposed SlicerRT extension for radiation therapy research was demonstrated on multiple use cases.
Conclusions:
A new open-source software toolkit has been developed for radiation therapy research. SlicerRT can import treatment plans from various sources into 3D Slicer for visualization, analysis, comparison, and processing. The provided algorithms are extensively tested and they are accessible through a convenient graphical user interface as well as a flexible application programming interface.
•Introduce conversion tools for different vendors.•Explain conversion basics.•Present methods to detect and correctproblems.
Clinical imaging data are typically stored and transferred in the DICOM ...format, whereas the NIfTI format has been widely adopted by scientists in the neuroimaging community. Therefore, a vital initial step in processing the data is to convert images from the complicated DICOM format to the much simpler NIfTI format. While there are a number of tools that usually handle DICOM to NIfTI conversion seamlessly, some variations can disrupt this process.
We provide some insight into the challenges faced with image conversion. First, different manufacturers implement the DICOM format differently which complicates the conversion. Second, different modalities and sub-modalities may need special treatment during conversion. Lastly, the image transferring and archiving can also impact the DICOM conversion.
We present results in several error-prone domains, including the slice order for functional imaging, phase encoding direction for distortion correction, effect of diffusion gradient direction, and effect of gantry correction for some imaging modality.
Conversion tools are often designed for a specific manufacturer or modality. The tools and insight we present here are aimed at different manufacturers or modalities.
The imaging conversion is complicated by the variation of images. An understanding of the conversion basics can be helpful for identifying the source of the error. Here we provide users with simple methods for detecting and correcting problems. This also serves as an overview for developers who wish to either develop their own tools or adapt the open source tools created by the authors.
Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered ...by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse. DICOM stands out as a standard that transcends internal image formats via metadata-driven image exchange in this context. DICOM defines imaging workflow information objects for images, patients’ studies, reports, etc. DICOM promises standards-based pathology imaging, but its clinical use is limited. No FDA-approved digital pathology system natively generates DICOM, and only one high-performance whole slide images (WSI) device has been approved for diagnostic use in Asia and Europe. In a recent series of Digital Pathology Connectathons, the interoperability of our solution was demonstrated by integrating DICOM digital pathology imaging, i.e., WSI, into PACs and enabling their visualisation. However, no system that incorporates state-of-the-art AI methods and directly applies them to DICOM images has been presented. In this paper, we present the first web viewer system that employs WSI DICOM images and AI models. This approach aims to bridge the gap by integrating AI methods with DICOM images in a seamless manner, marking a significant step towards more effective CAD WSI processing tasks. Within this innovative framework, convolutional neural networks, including well-known architectures such as AlexNet and VGG, have been successfully integrated and evaluated.
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