The use of the Digital Imaging and Communications in Medicine (DICOM) standard for storage and interchange of radiology images is ubiquitous in human clinical imaging environments. DICOM started when the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) were encouraged by the Food and Drug Administration (FDA) to institute a standard format, spurred by the rapid growth of X-ray computed tomography usage in the 1970’s and the proliferation of incompatible manufacturer-specific image formats. The new standard provided for (1) for the transfer and display of digital images, (2) development of picture archiving and communication systems (PACS) with interfaces to other health care information systems, and (3) allowed for the creation of diagnostic databases. The DICOM standard has undergone numerous enhancements as manufacturers improved and developed new radiological equipment but retains backward compatibility. Unfortunately, DICOM has seen only limited adoption for small animal pre-clinical research. Pre-clinical modality manufacturers have either developed their own proprietary image formats or used a minimum subset of features within the clinical DICOM standard. Equipment intended for human imaging that is re-purposed for animal imaging generally produces DICOM, but typically suffers from a lack of animal-specific identifying and descriptive parameters.
With increased attention to issues of translation of research from bench to bedside, co-clinical (animal and human) trials, multiple-mouse acquisitions and analysis, complex animal models, and quantitative imaging, there are small animal-oriented tasks that need to be supported to measure, record and process various small animal data in minimally labor-intensive methods.
To address these issues, The National Cancer Institute (NCI) Division of Cancer Treatment and Diagnosis (DCTD) and the Center for Biomedical Informatics and Information Technology (CBIIT), formed a DICOM working-group in 2013, comprised of members from National Laboratories, the DICOM standard, academia, contract research organizations (CRO’s), and modality manufacturers, in order to extend DICOM to better support small animal imaging and to employ established semantically interoperable terminology.
The new DICOM working group (WG30) realized early on the importance of recording pertinent data about the research animals and their conditions. Since this type of information potentially influences the interpretation and quantification of images, it is referred to as Image Acquisition Context Structured Report (SR) [1
]. It includes information about anesthesia, mouse model (i.e., NOD.Cg-PrkdcscidIl2rgtm1Wjl
/SzJ, C57BL/6, etc.), cancer (cell or fragment) model, animal date of birth, date and site of cell/fragment implant, date of fragment excision, and facility-vivarium information, etc.) [2
Personalized medicine, the ability to define a therapy to a specific patient has inaugurated co-clinical studies, where in vivo pre-clinical and early clinical studies are closely associated. As part of this endeavor, pre-clinical researchers instituted the mouse hospital to provide the testing of various therapies and monitor responses with statistically significant datasets. Since many animals are involved, it is efficient to image multiple mice at the same time, if the equipment allows for it. The implementation of multi-mouse imaging requires careful attention to the identification of groups of animals and individual animals, whether using manual or automated recording methods. For interpretation and analysis, when a cohort of more than one mouse has been imaged in the multi-mouse field of view, it is desirable to split the multi-mouse images into single mouse images, with corresponding updated DICOM information in the derived image headers.
Presently, most pre-clinical researchers use either a spreadsheet or database for maintaining and tracking the animal model and the animal handling processes. This brief article is to provide co-clinical and pre-clinical researchers the method to convert their tracking spreadsheet into the small animal acquisition context structured report and linked to the DICOM image. To assist researchers to convert their spreadsheets into the respective structured report, we provide all the necessary files and toolkits on The Cancer Imaging Archive (TCIA) (University of Arkansas for Medical Sciences, Little Rock, AR, USA) website [8
]. In addition to the required files, we also provide a pre-clinical example including a tracking spreadsheet used in a patient-derived xenograft study, associated MRI data, and the resulting image acquisition context SR.
To provide the researcher the ability to query between pre-clinical and clinical datasets, a one-to-one query, it became necessary to split the pre-clinical multi-rodent datasets into single images and linked to the appropriate acquisition context structured report. The process and algorithms to split any DICOM dataset are also provided and as an example, MRI data from a patient-derived xenograft study, is provided and uploaded to the TCIA website.
The small animal imaging community and the National Cancer Institute realized that to advance co-clinical research and to address the issues of translation from bench to bedside, the DICOM information that was developed for clinical images needed to be extended to pre-clinical data. The interpretation of pre-clinical data is influenced by numerous factors, such as anesthesia, mouse model (genetically engineered, mouse strain), cancer model (cell or fragment, orthotopic, xenograft), animal date of birth, date and site of cell/fragment implant, date of fragment excision, and facility-vivarium information, which are necessary to include in the pre-clinical DICOM. The DICOM WG30 group noted that the addition of the small animal data to the radiological image header should be performed in a minimally labor-intensive process, the Acquisition Context Structure Report template was utilized and developed the Pre-clinical Small Animal Image Acquisition Context (TID 8101). Additionally, to provide researchers the ability to cross-reference human-to-mouse anatomies, the mapping and harmonization developed by the Mouse-Human Anatomy Project was implemented.
Pre-clinical researchers implement mouse hotels to improve experimental statistics and to increase the number of mice within a cohort and the number of cohorts (vehicle, single, and combination drugs). Unfortunately, it is impossible to query a single mouse within the multi-mouse image with a single (human) radiological data. Therefore, a multi-mouse image splitting routine was implemented with unique UID’s with the ability to cross-reference to the original multi-mouse image.
In conclusion, this pre-clinical enhancement to DICOM enhances the researcher’s ability to query pre-clinical and clinical datasets using standard vocabularies and enhance co-clinical studies.