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A set of experiments were performed where metadata updates were applied to a set of DICOM studies stored in both the traditional Single Frame DICOM (SFD) format and the MSD format. The first file contains the de-duplicated study metadata, and the second contains pixel data and other bulkdata. MSD stores studies using two files rather than in many single frame files typical of DICOM. The MSD format separates metadata from pixel data, and at the same time eliminates duplicate attributes. This work uses the Multi-Series DICOM (MSD) format to reduce the time required for tag morphing. It is typically used for order reconciliation on study acquisition or to localize the Issuer of Patient ID and the Patient ID attributes when data from one Medical Record Number (MRN) domain is transferred to or displayed in a different domain. Tag or attribute morphing includes insertion, deletion, or modification of one or more of the metadata attributes in a study. Tag morphing is an example of one such use case. However, there are important use cases that only need access to metadata, and the DICOM format increases the running time of those use cases. It is not possible to access the metadata separately from the pixel data. The DICOM information model combines image pixel data and associated metadata into a single object. Most medical images are archived and transmitted using the DICOM format. We demonstrate a prototype of our framework using various medical datasets on commodity tablet devices. The choice of low-latency CPU- and GPU-based encoders is particularly important in enabling the interactive nature of our system. H.264 is ubiquitously hardware accelerated, resulting in faster compression and lower power requirements. Specifically, upon user interaction the volume is rendered on the server and encoded into an H.264 video stream. We utilize the display and interaction capabilities of the mobile device, while performing interactive volume rendering on a server capable of handling large datasets. To deal with this issue, we propose a thin-client architecture, where the entirety of the data resides on a remote server where the image is rendered and then streamed to the client mobile device. This explosion in data size makes data transfers to mobile devices challenging, and even when the transfer problem is resolved the rendering performance of the device still remains a bottleneck. For example, the resolution of typical CT Angiography (CTA) data easily reaches 512^3 voxels and can exceed 6 gigabytes in size by spanning over the time domain while capturing a beating heart. The necessity of such a pipeline stems from the large size of the medical imaging data produced by current CT and MRI scanners with respect to the complexity of the volumetric rendering algorithms.
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We introduce a novel remote volume rendering pipeline for medical visualization targeted for mHealth (mobile health) applications.
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The developed SR conversion model and GUI support aim to further demonstrate how to incorporate CAD post-processing components in a PACS and imaging informatics-based environment. The methodology of converting CAD data in native MATLAB format to DICOM-SR and displaying the tabulated DICOM-SR along with the patient’s clinical information, and relevant study images in the GUI will be demonstrated. In addition, the GUI supports lesion contour overlay, which matches a detected MS lesion with its corresponding DICOM-SR data when a user selects either the lesion or the data. The GUI is able to parse DICOM-SR files and extract SR document data, then display lesion volume, location, and brain matter volume along with the referenced DICOM imaging study. A web-based GUI based on our existing web-accessible DICOM object (WADO) image viewer has been designed to display the CAD results from generated SR files. Open-source dcmtk and customized XML templates are used to convert quantitative MS CAD results from MATLAB to DICOM-SR format. For compliance with IHE integration protocols, long-term storage in PACS, and data query and display in a DICOM compliant clinical setting, CAD results need to be converted into DICOM-Structured Report (SR) format. The system integrates the patient’s clinical data with imaging studies and a computer-aided detection (CAD) algorithm for quantifying MS lesion volume, lesion contour, locations, and sizes in brain MRI studies. In the past, we have presented an imaging-informatics based eFolder system for managing and analyzing imaging and lesion data of multiple sclerosis (MS) patients, which allows for data storage, data analysis, and data mining in clinical and research settings.